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NeurIPS/2025/UniMotion_ A Unified Motion Framework for Simulation, Prediction and Planning/full.md

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+# UniMotion: A Unified Motion Framework for Simulation, Prediction and Planning
+
+Nan Song<sup>1</sup> Junzhe Jiang<sup>1</sup> Jingyu Li<sup>1,2</sup> Xiatian Zhu<sup>3</sup> Li Zhang<sup>1,2*</sup>
+
+$^{1}$ School of Data Science, Fudan University
+
+$^{2}$ Shanghai Innovation Institute  $^{3}$ University of Surrey
+
+https://github.com/LogosRoboticsGroup/UniMotion
+
+# Abstract
+
+Motion simulation, prediction and planning are foundational tasks in autonomous driving, each essential for modeling and reasoning about dynamic traffic scenarios. While often addressed in isolation due to their differing objectives, such as generating diverse motion states or estimating optimal trajectories, these tasks inherently depend on shared capabilities: understanding multi-agent interactions, modeling motion behaviors, and reasoning over temporal and spatial dynamics. Despite this underlying commonality, existing approaches typically adopt specialized model designs, which hinders cross-task generalization and system scalability. More critically, this separation overlooks the potential mutual benefits among tasks. Motivated by these observations, we propose UniMotion, a unified motion framework that captures shared structures across motion tasks while accommodating their individual requirements. Built on a decoder-only Transformer architecture, UniMotion employs dedicated interaction modes and tailored training strategies to simultaneously support these motion tasks. This unified design not only enables joint optimization and representation sharing but also allows for targeted fine-tuning to specialize in individual tasks when needed. Extensive experiments on the Waymo Open Motion Dataset demonstrate that joint training leads to robust generalization and effective task integration. With further fine-tuning, UniMotion achieves state-of-the-art performance across a range of motion tasks, establishing it as a versatile and scalable solution for autonomous driving.
+
+# 1 Introduction
+
+Motion understanding is a cornerstone of autonomous driving, supporting essential capabilities such as interactive behavior modeling, motion analysis, and decision-making for the ego vehicle. Core motion tasks include motion simulation, trajectory prediction, and ego planning. Among them, trajectory prediction [1, 2, 3] and ego planning [4] are critical intermediaries between perception modules and control systems in the driving pipeline, while motion simulation [5] generates rich and diverse agent behaviors, serving both development and evaluation for motion models. The complexity of real-world driving, characterized by uncertain behaviors and densely interactive environments, makes these tasks challenging.
+
+Accordingly, recent research has produced a wide range of task-specific models to optimize performance for its designated objective. Trajectory prediction and planning models have primarily focused on comprehensive representation learning [6, 7, 8, 9], along with a growing emphasis on precise waypoint estimation [10, 11, 12, 13]. In contrast, motion simulation has leaned heavily on
+
+![](images/31017e98cc176fddb756143f7d12c38469053ae9e2f6b0438c2b6d7e03e63af4.jpg)  
+Figure 1: Overview of our UniMotion pipeline. (a) We train jointly our model with combined generative and forecasting supervision, resulting in a multi-task model that can simultaneously address all kinds of motion tasks in autonomous driving. (b) We further adopt dedicated fine-tuning strategies for each task, producing task-specific models to promote specialization.
+
+![](images/b73e4585398c1992dc228ae5c732f6eb71911509a5a4175c96d148d5a1ecab26.jpg)
+
+generative modeling. Inspired by the success of Large Language Models (LLMs), recent GPT-style architectures [14, 15] have been applied to simulate agent behaviors. Furthermore, this paradigm has also been introduced into prediction [16, 17] and planning [13] tasks to achieve progressive and more accurate regression.
+
+While these task-customized models have proven effective for different motion tasks, it is challenging to transfer models and learned knowledge across tasks. Considering that these tasks are essentially similar due to their reliance on interaction modeling and motion reasoning, our goal is to build a general model that encourages knowledge sharing among motion tasks and enables cross-task generalization. To achieve this aim, we revisit the formulation of motion tasks and propose a unified motion framework for autonomous driving. We begin by abstracting existing motion tasks into two fundamental categories: diverse motion generation and long-range trajectory forecasting, which serve as a conceptual foundation for unification. From this, we develop UniMotion, a unified framework based on a decoder-only Transformer, chosen for its simplicity, scalability, and strong generative capacity across tasks. By incorporating task-aware interaction modes and training strategies (see Figure 1(a)), UniMotion enables joint training across simulation, prediction and planning, fostering shared representations and efficient multi-task learning. To further enhance task proficiency, we also introduce dedicated fine-tuning strategies (see Figure 1(b)) that adapt the unified model to specific objectives, improving performance and practical deployment.
+
+Our contributions are summarized as follows: (i) We abstract motion tasks into two core categories and propose UniMotion, a unified Transformer-based framework that jointly models simulation, prediction and planning to promote generality and cross-task knowledge sharing. (ii) We design effective fine-tuning strategies that specialize the jointly trained model for individual tasks, allowing the model to master task-specific expertise. (iii) Extensive experiments on the Waymo Open Motion Dataset (WOMD) show that UniMotion achieves competitive performance under joint training and sets new state-of-the-art results when fine-tuned for specific tasks.
+
+# 2 Related work
+
+# 2.1 Motion tasks in autonomous driving
+
+Existing motion tasks in autonomous driving can be roughly categorized into three types according to their objectives: motion simulation, trajectory prediction, and ego planning. We provide a detailed discussion for each type in the following.
+
+Motion simulation focuses on simulating diverse and complex motion patterns, augmenting training samples and offering off-board assessment for autonomous driving algorithms. To effectively measure simulation quality, Sim Agents [5] first proposes a benchmark with comprehensive metrics, expecting models to progressively evolve the motion states of all traffic agents. Motivated by the performance of LLMs, existing methods [14, 15] tokenize the agent trajectories and map elements, and employ a scalable decoder-only model to perform auto-regressive trajectory generation. Besides, CATK [18] utilizes closed-loop supervised finetuning with Closest Among Top-K rollouts, which mitigates covariate shift in open-loop behavior cloning and further improves these GPT-style methods. In contrast, UniMM [19] introduces a unified mixture model with both continuous mixture models and discrete GPT-style models to address simulation.
+
+Trajectory prediction anticipates models to rapidly estimate the future trajectories of traffic participants, providing prior knowledge for subsequent decision-making. As a foundation task in autonomous driving, trajectory prediction has been widely explored [6, 7, 8, 11] with Transformer frameworks. Recent methods [20, 21, 22] uncover detailed temporal information and achieve more accurate prediction. Moreover, there are methods utilizing particular strategies for impressive performance improvements, such as model pre-training [23, 24] and trajectory post-refinement [10, 12].
+
+Ego planning is responsible for giving final driving trajectories for ego vehicle according to the driving environment and upstream results. Although rule-based models [25, 26] still hold a crucial position, learning-based methods have emerged and exceeded. PlanTF [27] and PLUTO [28] effectively alleviate the limit of imitation-based planners through designed model architecture and training strategies. Besides, BeTopNet [9] further improves planning performance by explicitly representing behavioral topology among multi-agent future states.
+
+# 2.2 GPT-style motion models
+
+GPT-style LLMs have demonstrated strong capabilities in language tasks, which stimulates the emergence of similar architectures in autonomous driving [29, 30]. Given that both trajectories and text share a sequential structure, it is natural to introduce analogous modules and designs into motion models. In particular, the aforementioned simulation methods directly utilize decoder-only models with next-token prediction for simulation tasks, generating diverse multi-agent trajectory sets. There are also some prediction and planning methods adopting modified auto-regressive decoding. For example, MotionLM [17] and Trajeglish [16] introduce a causal decoder for iterative trajectory prediction, and similarly, CarPlanner [13] adopts consistent auto-regressive planning with reinforcement learning. In contrast, we unify all motion tasks under a GPT-style model, fully exploiting the capability of this framework.
+
+# 3 Methodology
+
+# 3.1 Preliminary
+
+Task definition. Motion understanding in autonomous driving focuses on three tasks: simulation, prediction and planning. All these tasks share the same traffic scenario input, including historical agent states  $\mathcal{A}_h$  and a high-definition (HD) map  $\mathcal{M}$  of driving scenes. Motion simulation is typically designed to support other motion tasks. Given  $\mathcal{A}_h$  as initial motion states, it aims to iteratively and concurrently simulate multi-agent states  $\{\mathcal{A}_s^i\}_{i=1}^K$ , where  $K$  represents the number of generated rollouts. Simulation aims to exhibit high short-term generation diversity. In contrast, trajectory prediction provides distant future trajectories  $\mathcal{A}_f$  of traffic agents over a span of time  $T_f$ . In real-world scenarios, we expect the predictors to generate the results directly and efficiently, rather than relying on segment-by-segment auto-regressive generation [16, 17]. Accordingly, this task evaluates the ability of models to perform accurate long-range predictions. Ego planning aims to determine feasible trajectories for the ego vehicle by referencing surrounding agents. It can be viewed as first predicting the behaviors of other traffic participants, followed by the progressive generation of the ego vehicle's trajectory. In light of the above observations, we summarize motion tasks into two fundamental components: diverse motion generation and long-range trajectory forecasting. As motivated earlier, we strive to integrate these tasks into a unified framework and jointly develop both capabilities in a single model, thereby enhancing overall motion understanding and fostering mutual complementarity between tasks.
+
+Input representation. To ensure the generalization of representations across tasks, we standardize the input format via tokenization following [14, 15]. Concretely, the trajectories of all agents are segmented at fixed time intervals and normalized to form a trajectory set, which is then clustered to construct the agent token vocabulary  $S_{a}$ . Similarly, map tokens  $S_{m}$  are generated by dividing original map polylines into fixed-length segments followed by clustering. The corresponding agent token embeddings  $E_{sa} \in \mathbb{R}^{K_a \times C}$  and map token embeddings  $E_{sm} \in \mathbb{R}^{K_m \times C}$  obtained by applying MLP modules to  $S_{a}$  and  $S_{m}$ , respectively, where  $K_{(\cdot)}$  and  $C$  are the number of tokens and the dim of feature embeddings. Subsequently, the agent states  $\mathcal{A}$  and a HD map  $\mathcal{M}$  are tokenized based on geometric similarity of segments, forming the input agent embeddings  $E_{a} \in \mathbb{R}^{N_{a} \times T \times C}$  and map
+
+![](images/af350afd9c1d30e9a56b86f8a8f97da463017c313d6dbc16fc7ec27b41532b0b.jpg)  
+Figure 2: Overview of UniMotion architecture. It takes as input tokenized agent trajectories and map polylines, and adopts a decoder-only structure equipped with an additional map encoder to yield motion states. By employing task-specific attention masks and training objectives, UniMotion is empowered to flexibly and effectively address multiple motion tasks simultaneously.
+
+embeddings  $E_{m} \in \mathbb{R}^{N_{m} \times C}$ , where  $N_{a}$  and  $N_{m}$  denote the number of agents and map segments, and  $T$  denotes the time frames.
+
+# 3.2 UniMotion for multiple motion tasks
+
+As depicted in Figure 2, the overall design of our UniMotion follows a decoder-only architecture for agent trajectory generation and prediction, with an additional map context encoder. For each specific motion task, we introduce dedicated interactive patterns and training objectives for specialization, but enable weight sharing to learn general representations.
+
+# 3.2.1 Scene context encoding and motion decoding
+
+To ensure the robustness and generalizability of the model, we employ the Transformer architecture with relative positional embedding following [8, 14, 15], which remains invariant under coordinate transformations. Specifically, we first stack several self-attention modules to encode map embeddings, yielding map features  $F_{m}$ . Then, agent embeddings  $E_{a}$ , along with  $F_{m}$ , are further delivered to a agent Transformer decoder constructed with factorized attentions, which are composed of temporal self-attention, agent-map cross-attention and agent-agent self-attention. This can be formulated as:
+
+$$
+F _ {a} = \mathrm {M H S A} _ {t} (\mathrm {Q} = F _ {a}, \mathrm {K} = \mathrm {V} = F _ {a} + \mathcal {R} _ {t} (P _ {a})),
+$$
+
+$$
+F _ {a} = \mathrm {M H C A} _ {a - m} (\mathrm {Q} = F _ {a}, \mathrm {K} = \mathrm {V} = F _ {m} + \mathcal {R} _ {a - m} \left(P _ {a}, P _ {m}\right)), \tag {1}
+$$
+
+$$
+F _ {a} = \mathrm {M H S A} _ {a - a} (\mathrm {Q} = F _ {a}, \mathrm {K} = \mathrm {V} = F _ {a} + \mathcal {R} _ {a - a} (P _ {a})),
+$$
+
+where  $\mathcal{R}$  is the relative positional embedding for spatial and temporal position information  $P_{(\cdot)}$ , and the initial  $F_{a}$  is directly derived from  $E_{a}$ . This decoder module enables the thorough interaction among agents and between agents and road elements, finally giving step-wise motion states.
+
+# 3.2.2 Joint training across motion tasks
+
+Following the designs of multi-task language models, we apply customized interactive attention masks for different motion tasks and introduce tailored training objectives to improve both stepwise generation and long-range prediction. At inference time, we adaptively leverage the model's capabilities according to the specific task.
+
+Motion simulation. The motion simulation task closely resembles language generation, which aims to iteratively select suitable tokens from motion vocabulary. Accordingly, we adopt a causal attention mask and employ Next-Token Prediction (NTP) as the learning strategy, modeling the generation of
+
+![](images/de7c377a769003988d1a37b50166a559c9a0e39cf6885a27a37eb84706e0027f.jpg)  
+(a) RL-based fine-tuning
+
+![](images/fe4ed5c1f183c7e74c6f0c38095be931705c5d7f2ed0b3ee47a94f380e162029.jpg)  
+(b) Multi-modal fine-tuning
+
+![](images/dfdd2faf90e8942867b654ff9baef810e7c8fbafd456d468c629e9b3efffa9c7.jpg)  
+(c) P2G fine-tuning  
+Figure 3: Illustration of task-specific fine-tuning strategies. We adopt (a) RL-based fine-tuning to improve simulation likelihood while maintaining closed-loop consistency. (b) Multi-modal fine-tuning is introduced to refine the multi-modal behavior of predictions. With reference to the planning inference, we utilize (c) Pred2Gen fine-tuning to address distribution mismatch.
+
+the next agent token  $\mathcal{A}_t$  at step  $t$  from the conditional probability  $P(\mathcal{A}_t\mid \mathcal{A}_{< t})$ . During inference, motion processes for all agents are simultaneously simulated through auto-regressive generation.
+
+Trajectory prediction. We regard trajectory prediction as a sequence-to-sequence task, which maps historical segments to future trajectories. Tokens within the historical segments are allowed to attend only to each other, while future tokens can attend both to preceding tokens within the future segment and to all tokens in the historical segments. Moreover, we propose Long-range Future Regression (LFR) to meet the strict requirements of prediction task on accuracy. Specifically, we replace token classification with normalized trajectory regression, and it predicts a complete long-range future trajectory instead of short segments with token-level length, in which case future trajectories  $\mathcal{A}_f$  can be derived from:
+
+$$
+\mathcal {A} _ {f} = \operatorname {M L P} \left(F _ {a}\right), \mathcal {A} _ {f} \in \mathbb {R} ^ {N _ {a} \times T \times T _ {f} \times 2}. \tag {2}
+$$
+
+Notably, all these tokens are utilized for dense supervision in the training phase, while we only need to perform single-pass reasoning at current step for inference.
+
+Ego planning. As mentioned in Section 3.1, we consider planning task to be the combination of generation and prediction. Hence, the training objectives for simulation and prediction naturally benefit the planning task, without requiring any additional training strategies. After training, we adopt a two-stage inference approach for ego trajectory generation. Concretely, we first jointly predict future trajectories for all traffic participants, and then tokenize the future parts of these predictions to form the future tokens. Given the predicted future tokens of surrounding agents, we then progressively generate the planning trajectory of ego vehicle.
+
+# 3.2.3 Task-specific fine-tuning strategies
+
+Despite the capability of solving multiple tasks simultaneously, models are typically expected to exhibit higher specialization for individual tasks in real-world applications. To this end, we introduce task-specific fine-tuning strategies to further enhance the specialization of UniMotion. Overall, we prefer not to introduce additional parameters during fine-tuning stage. According to this principle, we utilize reinforcement learning-based fine-tuning for simulation and two-stage pred2gen finetuning for planning. Additionally, trajectory prediction requires to produce multi-modal outputs, which will significantly increase the computational burden under dense supervision. To alleviate this, we employ a multi-modal decoder to support the prediction process as a compromise.
+
+In line with the purpose of Reinforcement Learning from Human Feedback (RLHF) in LLMs, we enhance the model to generate trajectories that align with human driving behavior and traffic rules, which cannot be constrained through direct supervision alone. Hence, we introduce RL-based fine-tuning for simulation. As shown in Figure 3(a), we first generate  $n$  rollouts of driving scenes as a group following GRPO [31], with activating the gradient for only one rollout. Subsequently,
+
+rewards  $R$  are computed using the same algorithms as the metrics in Sim Agents [5], which consist of kinematic reward and collision reward for simplicity. We formulate this as:
+
+$$
+R = \exp \left(\frac {1}{T} \sum_ {i = 1} ^ {T} \log q ^ {\mathrm {g t}}\right) + \mathbf {1} \{\text {n o t c o l l i s i o n} \}, \tag {3}
+$$
+
+where the kinematic reward is defined as the exponentiated log-likelihood of ground truth samples under the distribution induced by corresponding generated trajectories, whereas the collision reward is an indicator function. Then, the advantage  $A$  is derived from the normalized rewards within the group of each agent. Notably, we only supervise one rollout to reduce computational overhead. More details are provided in Section 3.3 and supplementary materials.
+
+To improve multi-modality of predicted trajectories, we introduce a lightweight transformer decoder similar to [11, 32] as illustrated in Figure 3(b). At this stage, we only focus on the agents of interest that are utilized for evaluation, and perform prediction within perspective local system. To this end, the coordinate-invariant features are transformed into the local agent features  $F_{a}^{*}$  and map features  $F_{m}^{*}$  through adding the local normalized position content encoded by MLP layer. For each interested agent, the estimated multi-modal trajectories  $a_{f}^{*} \in \mathbb{R}^{N_{mo} \times T_{f} \times 2}$  for  $N_{mo}$  modes can be derived by:
+
+$$
+a _ {f} ^ {*} = \operatorname {T r a n s f o r m e r} \left(\mathrm {Q} = F _ {m o}, \mathrm {K} = \mathrm {V} = \left\{F _ {a} ^ {*}, F _ {m} ^ {*} \right\}\right), \tag {4}
+$$
+
+where  $F_{mo}$  denotes the mode features encoded from intention points, and  $F_{a}^{*}$  and  $F_{m}^{*}$  are with respect to current interested agent. Benefiting from distinct intention points, our model can provide more diverse trajectory choices after fine-tuning.
+
+During planning inference, the predicted trajectory tokens for surrounding agents will participate in and significantly affect ego generation, inconsistent with the independent training process. To alleviate potential distribution mismatch, we further fine-tune ego generation using the predicted results. As shown in Figure 3(c), the incorrectly predicted trajectories are first filtered out and replaced by the ground truth. Then, we tokenize all predictions and feed them into our network for ego generation, which is fine-tuned with end-to-end supervision.
+
+# 3.3 Model training
+
+During the joint training process, we mainly adopt NTP classification loss  $\mathcal{L}_{\mathrm{ntp}}$  between next tokens  $\mathcal{A}_s$  and corresponding ground truth  $\hat{\mathcal{A}}_s$  for generation supervision, and LFR regression loss  $\mathcal{L}_{\mathrm{lfr}}$  between  $\mathcal{A}_f$  and ground truth  $\hat{\mathcal{A}}_f$  for trajectory prediction supervision. We combine these two individual losses with equal weights to form the overall loss  $\mathcal{L}$ , formulated as follows:
+
+$$
+\mathcal {L} = \mathcal {L} _ {\mathrm {n t p}} \left(\mathcal {A} _ {s}, \hat {\mathcal {A}} _ {s}\right) + \mathcal {L} _ {\mathrm {l f r}} \left(\mathcal {A} _ {f}, \hat {\mathcal {A}} _ {f}\right), \tag {5}
+$$
+
+where we employ the cross-entropy loss and the smooth-L1 loss for  $\mathcal{L}_{\mathrm{ntp}}$  and  $\mathcal{L}_{\mathrm{lfr}}$ , respectively.
+
+During the fine-tuning stage, we employ different supervision strategies for each motion task. For simulation fine-tuning, we follow [33] to simplify the GRPO but still preserve the consistency constraint through closed-loop supervision on the gradient-activated rollout  $\mathcal{A}_r\in \mathbb{R}^{N_a\times T}$ . Besides, in light of efficiency concerns, we only use  $\mathcal{A}_r$  rather than all rollouts to perform a single policy update per iteration. The simulation fine-tuning loss is:
+
+$$
+\mathcal {L} _ {\mathrm {f t - s i m}} = \mathcal {L} _ {\mathrm {c e}} \left(\mathcal {A} _ {r}, \hat {\mathcal {A}} _ {r}\right) + w _ {\mathrm {s}} \mathcal {L} _ {\mathrm {p}} \left(\mathcal {A} _ {r}, A\right), \tag {6}
+$$
+
+where  $\mathcal{L}_{\mathrm{ce}}$  is the cross-entropy loss between  $\mathcal{A}_r$  and ground truth  $\hat{\mathcal{A}}_r$ ,  $\mathcal{L}_{\mathrm{p}}$  is the policy utilized in GRPO with taking  $\mathcal{A}_r$  and advantage  $A$  as inputs, and  $w_{s}$  is a balancing factor. The prediction loss consists of Gaussian NLL loss  $\mathcal{L}_{\mathrm{nll}}$  to supervise multi-modal trajectories  $a_{f}^{*}$  and cross-entropy loss for mode classification. Meanwhile, the model also learns to regress the local future trajectories  $\mathcal{A}_f^*$  for other agents as auxiliary supervision. The loss can be formulated as:
+
+$$
+\mathcal {L} _ {\mathrm {f t - p r e d}} = w _ {\mathrm {p r}} \mathcal {L} _ {\mathrm {l f r}} \left(\mathcal {A} _ {f} ^ {*}, \hat {\mathcal {A}} _ {f} ^ {*}\right) + \mathcal {L} _ {\mathrm {n l l}} \left(a _ {f} ^ {*}, \hat {a} _ {f} ^ {*}\right) + \mathcal {L} _ {\mathrm {c e}}. \tag {7}
+$$
+
+In addition, we simply supervise the generation  $a_{\mathrm{ego}}$  for ego vehicle and the predictions  $\mathcal{A}_f^{\mathrm{surr}}$  for surrounding agents in the planning fine-tuning stage as:
+
+$$
+\mathcal {L} _ {\mathrm {f t - p l a n}} = w _ {\mathrm {p l}} \mathcal {L} _ {\mathrm {l f r}} \left(\mathcal {A} _ {f} ^ {\text {s u r r}}, \hat {\mathcal {A}} _ {f} ^ {\text {s u r r}}\right) + \mathcal {L} _ {\mathrm {n t p}} \left(a _ {\mathrm {e g o}}, \hat {a} _ {\mathrm {e g o}}\right). \tag {8}
+$$
+
+Table 1: Performance comparison on 2025 Waymo Open Sim Agents Challenge.  
+
+<table><tr><td>Method</td><td>Realism Meta Metric ↑</td><td>Kinematic Metrics ↑</td><td>Interactive Metrics ↑</td><td>Map-based Metrics ↑</td><td>minADE ↓</td></tr><tr><td>UniTAM</td><td>0.7698</td><td>0.4869</td><td>0.7876</td><td>0.9085</td><td>1.3940</td></tr><tr><td>UniTFormer</td><td>0.7776</td><td>0.4892</td><td>0.7997</td><td>0.9140</td><td>1.3592</td></tr><tr><td>LLM2AD</td><td>0.7779</td><td>0.4846</td><td>0.8048</td><td>0.9109</td><td>1.2827</td></tr><tr><td>UniMM [34]</td><td>0.7829</td><td>0.4914</td><td>0.8089</td><td>0.9161</td><td>1.2949</td></tr><tr><td>CATK [18]</td><td>0.7846</td><td>0.4931</td><td>0.8106</td><td>0.9177</td><td>1.3065</td></tr><tr><td>UniMotion</td><td>0.7851</td><td>0.4943</td><td>0.8105</td><td>0.9187</td><td>1.3036</td></tr></table>
+
+Table 2: Performance comparison on WOMD Prediction Leaderboard. For each metric, the best and second best results are highlighted in bold and underline, respectively.  
+
+<table><tr><td>Method</td><td>minADE ↓</td><td>minFDE ↓</td><td>MR ↓</td><td>mAP ↑</td><td>Soft mAP ↑</td></tr><tr><td>HDGT [35]</td><td>0.5933</td><td>1.2055</td><td>0.1854</td><td>0.3577</td><td>0.3709</td></tr><tr><td>MTR [11]</td><td>0.6050</td><td>1.2207</td><td>0.1351</td><td>0.4129</td><td>0.4216</td></tr><tr><td>MTR++ [36]</td><td>0.5906</td><td>1.1939</td><td>0.1298</td><td>0.4329</td><td>0.4414</td></tr><tr><td>MGTR [37]</td><td>0.5918</td><td>1.2135</td><td>0.1298</td><td>0.4505</td><td>0.4599</td></tr><tr><td>EDA [32]</td><td>0.5718</td><td>1.1702</td><td>0.1169</td><td>0.4487</td><td>0.4596</td></tr><tr><td>ControlMTR [38]</td><td>0.5897</td><td>1.1916</td><td>0.1282</td><td>0.4414</td><td>0.4572</td></tr><tr><td>RMP-YOLO [39]</td><td>0.5737</td><td>1.1697</td><td>0.1160</td><td>0.4523</td><td>0.4673</td></tr><tr><td>UniMotion</td><td>0.5718</td><td>1.1643</td><td>0.1162</td><td>0.4534</td><td>0.4642</td></tr></table>
+
+# 4 Experiments
+
+# 4.1 Experimental settings
+
+Datasets and metrics. To evaluate the performance of our method, we conduct extensive experiments on the Waymo Open Motion Dataset, comprising 486,995 training scenarios, 44,097 validation scenarios and 44,920 testing scenarios. Each scenario has an observation window of 9.1 seconds, consisting of 91 frames sampled at  $10\mathrm{Hz}$ . Given 11-frame historical agent states and a HD map, the motion models are tasked with generating or predicting the future states of 80 frames. Additionally, prediction and planning tasks only perform one-step inference at the current time, while simulation task requires 32 simulations per scenario.
+
+We employ respective benchmark metrics to assess our model. For simulation, the core metric is the Realism Meta Metric, which is utilized to measure the similarity between the simulations and the real-world distribution. It is calculated by the weighted sum of Kinematic Metrics for motion features, Interactive Metrics to capture the interaction among agents and Map-based Metrics to test the agent behavior on the map. For prediction, 6 predicted trajectories for each interested agent are expected for evaluation, with metrics including minADE (minimum Average Displacement Error), minFDE (minimum Final Displacement Error), Miss Rate, mAP and Soft MAP. In addition, due to the absence of an official benchmark for planning, we adopt the metrics proposed by [40] as measures.
+
+Implementation details. We tokenize the trajectories and map elements following [15, 18], with 2,048 tokens for agents and 1,024 tokens for map. During the training phase, We train our models using the AdamW [41] optimizer with a total batch size of 48 on 8 NVIDIA A6000 GPUs for 30 epochs, with the weight decay set to 0.01. We initialize the learning rate of  $5 \times 10^{-4}$ , which is then decayed following a cosine annealing schedule. Regarding the fine-tuning stage, we employ the same learning rate for prediction, while a lower learning rate of  $5 \times 10^{-5}$  for simulation and planning. Beside, we also fine-tune these tasks with the same epochs.
+
+Each agent token covers 0.5 seconds, and each map token covers about 0.5 meters. After tokenization, the original 91-step trajectories in Waymo are compressed into 18 frames. For the training phase, the training time for full data is approximately 3.5 days, while using  $20\%$  of the data takes around 1 day in our experimental settings. In addition, we set the balance factor  $w_{\mathrm{s}}$ ,  $w_{\mathrm{pr}}$  and  $w_{\mathrm{pl}}$  to 0.1, 0.1 and 0.5, respectively.
+
+Table 3: Performance comparison for Waymo planning.  
+
+<table><tr><td rowspan="2">Method</td><td rowspan="2">Collision ↓</td><td rowspan="2">Red light ↓</td><td rowspan="2">Off route ↓</td><td colspan="3">Planning error ↓</td></tr><tr><td>@1s</td><td>@3s</td><td>@5s</td></tr><tr><td>IL</td><td>5.469</td><td>2.772</td><td>4.816</td><td>0.175</td><td>1.416</td><td>4.194</td></tr><tr><td>IL+Prediction</td><td>3.930</td><td>1.670</td><td>4.542</td><td>0.127</td><td>0.892</td><td>2.901</td></tr><tr><td>Sep. Plan+Pred.</td><td>1.813</td><td>1.327</td><td>0.527</td><td>0.238</td><td>1.251</td><td>3.466</td></tr><tr><td>DIPP [40]</td><td>1.802</td><td>1.235</td><td>0.506</td><td>0.227</td><td>1.187</td><td>3.335</td></tr><tr><td>UniMotion</td><td>1.565</td><td>1.309</td><td>0.477</td><td>0.083</td><td>0.591</td><td>2.246</td></tr></table>
+
+# 4.2 Comparison with state of the art
+
+We compare the performance of our UniMotion with top-ranked competitors on Sim Agents [5], WOMD Prediction and Waymo Planning [40] benchmarks. The leaderboards for Sim Agents and WOMD Prediction are presented in Table 1 and Table 2, respectively. For simulation, our method achieve the best score of 78.51 on Realism Meta Metric. Benefiting from the kinematic reward of fine-tuning, the model is encouraged to generate trajectories with higher motion similarity to real-world situations, driving better Kinematic Metrics than other methods. However, the minADE metric exhibits relatively poorer performance, which can be attributed to the tokenization-based design's stronger emphasis on generation diversity at the cost of slightly reduced prediction accuracy. The leaderboard of the prediction task demonstrate that our method achieves promising results across almost all metrics, except for Soft mAP. Regarding the planning task, UniMotion has far outperformed most of previous approaches as depicted in Table 3, showing that our method stands distinctly ahead of others in terms of planning error. Moreover, to ensure generality and consistency, we supervise model training solely using logged trajectories. This mechanism leads to suboptimal performance in reacting to red lights, which can be mitigated by incorporating traffic rule constraints, as in [40].
+
+# 4.3 General ablation study
+
+We conduct ablation studies on the WOMD validation split to examine the effectiveness of each component in UniMotion. For efficiency, we use only  $20\%$  of the training data for ablation studies, while adopting the default experimental settings following Section 4.1 for all other aspects. We focus on the core modules here, while leaving task-specific ablation to the supplementary materials.
+
+Effects of NTP and LFR. As shown in Table 4, we simply assess the effectiveness of NTP and LFR in our network on simulation and prediction tasks. The first and second rows demonstrate the results of training UniMotion using only NTP or LFR, respectively. Despite the absence of dedicated supervision for another one of tasks, we report all relevant metrics to compare with joint training and showcase the model's ability to handle multiple tasks. It can be observed that the model benefits from both NTP and LFR in generation and prediction, while it performs poorly on another task. In the third row, we implement a joint training strategy with these two types of supervision, simultaneously endowing the model with diverse generative and long-range predictive capabilities. Besides, we find that joint training further improves prediction accuracy compared to using LFR alone, which we attribute to the diverse generation directions serving as targets to guide the prediction process.
+
+Table 4: Ablation on joint training with NTP and LFR. "Kin."/“Inter.”: Kinematic/Interactive metrics.  
+
+<table><tr><td rowspan="2">NTP</td><td rowspan="2">LFR</td><td colspan="4">Simulation</td><td colspan="3">Prediction</td></tr><tr><td>Kin.</td><td>Inter.</td><td>Map</td><td>minADE</td><td>minADE</td><td>minFDE</td><td>mAP</td></tr><tr><td>✓</td><td></td><td>0.4884</td><td>0.7961</td><td>0.9148</td><td>1.4074</td><td>0.7697</td><td>1.5547</td><td>0.2629</td></tr><tr><td></td><td>✓</td><td>0.4401</td><td>0.7742</td><td>0.8949</td><td>1.2965</td><td>0.6668</td><td>1.3613</td><td>0.2935</td></tr><tr><td>✓</td><td>✓</td><td>0.4892</td><td>0.7968</td><td>0.9144</td><td>1.4011</td><td>0.6508</td><td>1.3296</td><td>0.3147</td></tr></table>
+
+Effects of task-specific fine-tuning. After training a preliminary model with a joint strategy, we further tailor it with designs specific to the target task. As demonstrated in Table 5, fine-tuning methods can intuitively bring consistent improvements across all motion tasks. For simulation, all metrics exhibit varying degrees of gains after fine-tuning, especially in the reward items. The 3rd and
+
+4th rows of prediction task show that the mAP metric has been remarkably enhanced by introducing an extra anchor-based decoder, which can cover more potential future trajectories. In addition, noticeable improvements are also observed on the minADE and MR metrics, achieving the expected goals of fine-tuning. Regrading the planning task, the 5th row shows limited performance due to the distribution mismatch between predicted tokens and original trajectory tokens. After fine-tuning, the model is adapted to the two-stage process, and there are considerable improvements for all metrics.
+
+Table 5: Ablation on task-specific fine-tuning for simulation, prediction and planning. Notably, we selected a set of metrics with substantial variation to facilitate comparison. Additionally, we fine-tune planning model with the data following [40] instead of  $20\%$  data.  
+
+<table><tr><td>Task</td><td>ft.</td><td colspan="4">Metrics</td></tr><tr><td rowspan="3">Simulation</td><td>✘</td><td>Kinematic ↑</td><td>Interactive ↑</td><td>Map-based ↑</td><td>minADE ↓</td></tr><tr><td rowspan="2">✓</td><td>0.4892</td><td>0.7968</td><td>0.9144</td><td>1.4011</td></tr><tr><td>0.4939</td><td>0.8032</td><td>0.9159</td><td>1.3904</td></tr><tr><td rowspan="3">Prediction</td><td>✘</td><td>minADE ↓</td><td>minFDE ↓</td><td>MR ↓</td><td>mAP ↑</td></tr><tr><td rowspan="2">✓</td><td>0.6508</td><td>1.3296</td><td>0.1626</td><td>0.3147</td></tr><tr><td>0.6413</td><td>1.3368</td><td>0.1493</td><td>0.3856</td></tr><tr><td rowspan="3">Planning</td><td>✘</td><td>Collision ↓</td><td>Err @1s ↓</td><td>Err @3s ↓</td><td>Err @5s ↓</td></tr><tr><td rowspan="2">✓</td><td>1.7347</td><td>0.1326</td><td>0.7943</td><td>2.7809</td></tr><tr><td>1.5650</td><td>0.0834</td><td>0.5908</td><td>2.2455</td></tr></table>
+
+# 4.4 Task-specific ablation study
+
+In this section, we adopt the same experimental settings and conduct task-specific ablation studies on the simulation and prediction tasks to investigate the effects of different module designs.
+
+Effects of RL-based fine-tuning for simulation. We present the effects of different constraints in Table 6. As shown in the 2nd and 3rd rows, closed-loop supervised fine-tuning leads to notable improvements, while relying solely on RL-based supervision, like DAPO [33], fails to produce satisfactory results. We attribute this to two primary factors: insufficient reward signals and limited model capacity.
+
+Table 6: Ablation on RL-based fine-tuning. "con:": consistency constraint. "Kin."/“Inter.”: Kinematic/Interactive metrics.  
+
+<table><tr><td>con.</td><td>rl</td><td>Kin.</td><td>Inter.</td><td>Map</td><td>minADE</td></tr><tr><td></td><td></td><td>0.4892</td><td>0.7968</td><td>0.9144</td><td>1.4011</td></tr><tr><td>✓</td><td></td><td>0.4921</td><td>0.8019</td><td>0.9156</td><td>1.3933</td></tr><tr><td></td><td>✓</td><td>0.4913</td><td>0.7974</td><td>0.9120</td><td>1.3967</td></tr><tr><td>✓</td><td>✓</td><td>0.4939</td><td>0.8032</td><td>0.9159</td><td>1.3904</td></tr></table>
+
+Effects of fine-tuning for prediction. We present the effects of different fine-tuning strategies in Table 7. The comparison between the 2nd and 3rd rows demonstrates that although the end-to-end fine-tuning strategy (without intention anchors) can significantly reduce prediction errors, it severely compromises prediction diversity and confidence, leading to degraded mAP performance. Hence, we retain the intention anchors as part of the fine-tuning process.
+
+Table 7: Ablation on prediction fine-tuning.  
+
+<table><tr><td>Strategy</td><td>minADE</td><td>minFDE</td><td>MR</td><td>mAP</td></tr><tr><td>w/o ft.</td><td>0.6508</td><td>1.3296</td><td>0.1626</td><td>0.3147</td></tr><tr><td>ft. w/o intention</td><td>0.6174</td><td>1.2639</td><td>0.1313</td><td>0.3389</td></tr><tr><td>ft. w/ intention</td><td>0.6413</td><td>1.3368</td><td>0.1493</td><td>0.3856</td></tr></table>
+
+![](images/a5a30c5386cd09e2f5c86d76d3fd860cce95bf7bd776f949a069b4964a13cbcf.jpg)  
+Figure 4: Qualitative results on WOMD validation split. The top and bottom rows present two rollouts of simulation and the results of prediction and planning, respectively. The red and green arrowed curves are ground truth and predicted trajectories.
+
+# 4.5 Qualitative results
+
+In Figure 4, we present qualitative results of our network on three motion tasks. The first two panels illustrate the diverse simulation results of all traffic agents at 0s, 4s, and 8s, respectively. The bottom-left figure depicts the multi-modal predictions for interested agents, while the bottom-right figure presents the ego trajectory generation process in a token-by-token manner. These qualitative results demonstrate our network's capability in handling multiple motion tasks.
+
+# 5 Conclusion
+
+In this work, we abstract mainstream motion tasks into two core types and aim to address all motion tasks within a unified framework, thereby promoting knowledge sharing and enhancing model generalization. Achieving this, we present UniMotion, an integrated model designed particularly to support joint training for these tasks. The critical components of our framework are distinct interactive modes and training strategies. Besides, we further perform task-specific fine-tuning for each task, fulfilling the requirement for expert-level models in real-world applications. Our extensive experiments on several task benchmarks comprehensively demonstrate that UniMotion is capable of unifying motion tasks, and the fine-tuned versions surpass the current state-of-the-art performance in the respective tasks.
+
+Limitations and future work. Although we employ a decode-only framework to ensure flexibility, the coupled spatial and temporal interaction and relative position embedding rely on detailed positional information of traffic elements, constraining the model from leveraging more LLM-related technologies. Moreover, after applying tokenization and conducting joint multi-task training, it is worthwhile to explore cross-dataset learning, which could lead to substantial improvements in joint training performance. Limited by computational resources and training time, we have not explored this technique in the present work.
+
+# Acknowledgments
+
+This work was supported in part by National Natural Science Foundation of China (Grant No. 62376060).
+
+# References
+
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+
+# A Details for simulation fine-tuning
+
+After obtaining the generated rollout  $\mathcal{A}_r$  and advantage  $A$ , we update policy gradient using simplified objective to ensure stable training of lightweight models, the gradient of which for token  $t$  can be estimated as:
+
+$$
+\nabla_ {\theta} \log \mathcal {F} _ {\theta} \left(\mathcal {A} _ {r} [ t ]\right) \cdot A _ {\left(\mathcal {A} _ {r}\right)} [ t ], \tag {9}
+$$
+
+where  $\mathcal{F}_{\theta}$  denotes our trainable models. Besides, considering the relatively uniform rewards and the large number of iterations, we perform policy update for only one generation per iteration to promote efficient training.
+
+# B Discussions
+
+Why we employ decoder-only Transformers as the backbone? We opt for a decoder-only Transformer structure because it is better suited for multi-step generative tasks like simulation, where input and output lengths can vary. Conversely, the encoder-decoder structure, while excellent for sequence-to-sequence prediction tasks, cannot generalize as well across the diverse demands of our multi-task framework. Adopting a decoder-only architecture during joint training ensures stronger generalization capabilities across all our tasks.
+
+Why the joint training in UniMotion works well? As detailed in Section 3.1, our primary objectives are NTP and LFR. Intuitively, this joint training paradigm fosters the learning of shared, rich intermediate representations across multiple tasks. This enables tokens within the model to attend simultaneously to both their immediate surroundings and more distant scene contexts, leading to a more holistic understanding of the environment.
+
+Further, despite their apparent differences, the two task objectives possess crucial commonalities. Specifically, while LFR provides direct supervision on long-range logged trajectories, the initial motion direction embedded within these trajectories concurrently serves as the maximization objective for NTP. This underlying consistency and synergistic relationship between LFR and NTP are fundamental to ensuring stable joint training and preventing mode collapse, allowing the model to learn effectively from both detailed local cues and broad long-range dependencies.
+
+# C More qualitative results
+
+We provide more qualitative results of our framework in Figure 5 and Figure 6 on the validation set of Waymo Open Motion Dataset [1, 5].
+
+# D Failure cases
+
+Although our UniMotion achieves strong performance across multiple motion tasks, it still encounters certain failure cases. We provide qualitative results and analysis to offer a more comprehensive understanding, which we hope will support future efforts toward developing more robust and capable algorithms, as shown in Figure 7 and Figure 8.
+
+In simulation task, there is still a possibility of collisions in complex scenario as illustrated in Figure 7 (a). Besides, we have also observed some unrealistic simulations in intersection scenarios as shown in Figure 7 (b), where agents remain stationary despite the green light. We provide the failure cases of prediction task in Figure 8 (a), which demonstrate that it is challenging to predict complex motion patterns, especially sudden turns. Similar issues are also observed in planning, particularly when dealing with sudden U-turns. Additionally, the model might yield reasonable trajectories that fail to reflect the driver's subjective intention.
+
+![](images/c1e155950225367f58eb6ace0f57c27409b630e0819337a0cc12ff886a42862c.jpg)  
+(a) Parking lot scene
+
+![](images/3af2f2cf8e89f502010ea36ae7472ff1c19211946d97aa572a616045352e0503.jpg)  
+(b) Intersection scene  
+Figure 5: Qualitative results on the Sim Agents. We present several complex scenarios in autonomous driving, each accompanied by two rollouts. The circled areas highlight the key differences between the two rollouts.
+
+![](images/464b3de393969e77be0c906819e8b5c734227b2d2609b2fda14e97dc270e254b.jpg)
+
+![](images/3b2cea10085fcec49b9b35a7ebc2e184e91b64a2ee442614feacae5d21fa1b1c.jpg)  
+(a) Prediction
+
+![](images/c341d400f3896e75a1902246cbd6547ac8c60c67534d9eccb543a78d0520e2ad.jpg)
+
+![](images/f1fc90bf5cf4dbf5d4b409e85e1a3ce15dc4208fa26b3b1a4e8539dfd3422fa6.jpg)  
+Figure 6: Qualitative results on the WOMD for prediction and planning task.
+
+![](images/3dbafd2535fcec3bb8cf6bf8e03a6cd79aa44c1c61d0aaa8640280c9037c12c7.jpg)  
+(b) Planning
+
+![](images/52044002deef6a6a8e7a5fd28fd5cd7deda2b02007523db2241a535062d1c936.jpg)
+
+![](images/6b2442d69d2c064b2bfc6f8c8f855f52a07f780f8862621aed946eebbf4a8b87.jpg)
+
+![](images/a79e08c56aa0329a1d740003e3a4bdb50e82997d3616d28310b203db8e7c609b.jpg)  
+(a) Collision
+
+![](images/397a240dbc498ebd52842402df319931f504da7414862b4605ceab2aa43606f7.jpg)
+
+![](images/90634ae22338e63ba6f388d215607deb2c8d22576bd6575650a272316c747967.jpg)
+
+![](images/b3a2b36ac4a04dca51f45b4b849774df38408b7b769cb0b576e14944f7d38843.jpg)  
+(b) Non-red-light stop
+
+![](images/c42f9d7596831378c681b927560631fbe4155dc4eac4a9e2a719b93bd55f31ce.jpg)  
+Figure 7: Failure cases in simulation. In the first row, the model causes a collision in a crowded parking lot. In the second row, it incorrectly incorrectly stop without a red light at an intersection.
+
+![](images/762bc99a1db32c0903ba7aa0520bb9e6908cdf1bfcdb28037babdabdb2644e3e.jpg)
+
+![](images/63e3d0f7b758ac2933d71652ecfe76064d148d73084f635b8a5382358501d0d4.jpg)  
+(a) Prediction
+
+![](images/32dbf075717e172bfd3d2685604f7959372d3e558327f86c19e7d4774eadb5f0.jpg)
+
+![](images/5c0d198c5a627bdd0eb65c95ce3e8f4e5e26021c9127690adb1ba21a62f7e0ae.jpg)  
+Figure 8: Failure cases in prediction and planning. The model fails to accurately predict the trajectories of vehicles and pedestrians. In terms of planning, it struggles with scenarios where the targets involve drivers' subjective intentions.
+
+![](images/4a0edb8666a904405c9dbaeeb11f02666bcf4577403c213f424797076932e1bb.jpg)  
+(b) Planning
+
+![](images/e809d9d7a134c7f70f424e3acd18bf92115d948961f50429a36b50dbe1be6bec.jpg)
+
+# NeurIPS Paper Checklist
+
+# 1. Claims
+
+Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
+
+Answer: [Yes]
+
+Justification: Please see the experiment section.
+
+Guidelines:
+
+- The answer NA means that the abstract and introduction do not include the claims made in the paper.  
+- The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.  
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+- It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.
+
+# 2. Limitations
+
+Question: Does the paper discuss the limitations of the work performed by the authors?
+
+Answer: [Yes]
+
+Justification: Please see the conclusion section.
+
+Guidelines:
+
+- The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.  
+- The authors are encouraged to create a separate "Limitations" section in their paper.  
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+
+# 3. Theory assumptions and proofs
+
+Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?
+
+Answer: [NA]
+
+Justification: This paper does not include theoretical results.
+
+# Guidelines:
+
+- The answer NA means that the paper does not include theoretical results.  
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+- Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.  
+- Theorems and Lemmas that the proof relies upon should be properly referenced.
+
+# 4. Experimental result reproducibility
+
+Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
+
+Answer: [Yes]
+
+Justification: Please see the experiment section.
+
+# Guidelines:
+
+- The answer NA means that the paper does not include experiments.  
+- If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.  
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+(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.  
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+(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
+
+# 5. Open access to data and code
+
+Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?
+
+# Answer: [No]
+
+Justification: The details are provided in the main paper and the appendix.
+
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+
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+- Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.
+
+# 6. Experimental setting/details
+
+Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
+
+# Answer: [Yes]
+
+Justification: Please see the experiment section and appendix.
+
+# Guidelines:
+
+- The answer NA means that the paper does not include experiments.  
+- The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.  
+- The full details can be provided either with the code, in appendix, or as supplemental material.
+
+# 7. Experiment statistical significance
+
+Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
+
+# Answer: [No]
+
+Justification: [NA]
+
+# Guidelines:
+
+- The answer NA means that the paper does not include experiments.  
+- The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.  
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+
+- It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a  $96\%$  CI, if the hypothesis of Normality of errors is not verified.  
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+- If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
+
+# 8. Experiments compute resources
+
+Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
+
+Answer: [Yes]
+
+Justification: Please see the experiment section.
+
+Guidelines:
+
+- The answer NA means that the paper does not include experiments.  
+- The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.  
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+- The paper should disclose whether the full research project required more computer than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).
+
+# 9. Code of ethics
+
+Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines?
+
+Answer: [Yes]
+
+Justification: [NA]
+
+Guidelines:
+
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+- If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.  
+- The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).
+
+# 10. Broader impacts
+
+Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
+
+Answer: [Yes]
+
+Justification: Our main idea is specifically designed for applications in autonomous driving. Guidelines:
+
+- The answer NA means that there is no societal impact of the work performed.  
+- If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.  
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+
+generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
+
+- The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.  
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+
+# 11. Safeguards
+
+Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
+
+Answer: [NA]
+
+Justification: This paper poses no such risks.
+
+Guidelines:
+
+- The answer NA means that the paper poses no such risks.  
+- Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.  
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+- We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.
+
+# 12. Licenses for existing assets
+
+Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
+
+Answer: [Yes]
+
+Justification: Please see the experiment section.
+
+Guidelines:
+
+- The answer NA means that the paper does not use existing assets.  
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+- If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.  
+- For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.  
+- If this information is not available online, the authors are encouraged to reach out to the asset's creators.
+
+# 13. New assets
+
+Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
+
+# Answer: [NA]
+
+Justification: This paper does not release new assets.
+
+# Guidelines:
+
+- The answer NA means that the paper does not release new assets.  
+- Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.  
+- The paper should discuss whether and how consent was obtained from people whose asset is used.  
+- At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
+
+# 14. Crowdsourcing and research with human subjects
+
+Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
+
+# Answer: [NA]
+
+Justification: This paper does not involve crowdsourcing nor research with human subjects.
+
+# Guidelines:
+
+- The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.  
+- Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.  
+- According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
+
+# 15. Institutional review board (IRB) approvals or equivalent for research with human subjects
+
+Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
+
+# Answer: [NA]
+
+Justification: This paper does not involve crowdsourcing nor research with human subjects.
+
+# Guidelines:
+
+- The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.  
+- Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.  
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+
+# 16. Declaration of LLM usage
+
+Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
+
+# Answer: [NA]
+
+# Justification: [NA]
+
+# Guidelines:
+
+- The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.  
+- Please refer to our LLM policy (https://neurips.cc/Conferences/2025/LLM) for what should or should not be described.

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+# UniRelight: Learning Joint Decomposition and Synthesis for Video Relighting
+
+Kai He $^{1,2,3}$  Ruofan Liang $^{1,2,3}$  Jacob Munkberg $^{1}$  Jon Hasselgren $^{1}$
+
+Nandita Vijaykumar $^{2,3}$  Alexander Keller $^{1}$  Sanja Fidler $^{1,2,3}$
+
+Igor Gilitschenski $^{2,3\dagger}$  Zan Gojcic $^{1\dagger}$  Zian Wang $^{1,2,3\dagger}$
+
+$^{1}$ NVIDIA  $^{2}$ University of Toronto  $^{3}$ Vector Institute
+
+# Abstract
+
+We address the challenge of relighting a single image or video, a task that demands precise scene intrinsic understanding and high-quality light transport synthesis. Existing end-to-end relighting models are often limited by the scarcity of paired multi-illumination data, restricting their ability to generalize across diverse scenes. Conversely, two-stage pipelines that combine inverse and forward rendering can mitigate data requirements but are susceptible to error accumulation and often fail to produce realistic outputs under complex lighting conditions or with sophisticated materials. In this work, we introduce a general-purpose approach that jointly estimates albedo and synthesizes relit outputs in a single pass, harnessing the generative capabilities of video diffusion models. This joint formulation enhances implicit scene comprehension and facilitates the creation of realistic lighting effects and intricate material interactions, such as shadows, reflections, and transparency. Trained on synthetic multi-illumination data and extensive automatically labeled real-world videos, our model demonstrates strong generalization across diverse domains and surpasses previous methods in both visual fidelity and temporal consistency. Our project page is https://research.nvidia.com/labs/toronto-ai/UniRelight/.
+
+# 1 Introduction
+
+Lighting is crucial in defining a scene's visual appearance, influencing both aesthetics and the perception of objects and materials. Modifying illumination while preserving scene content enables a wide range of applications, including creative editing, simulation, and robust vision systems. Realistic relighting necessitates an accurate understanding of scene geometry and materials, as well as faithful light transport modeling. Recent methods aim to learn these complex relationships from data, but a significant challenge is the scarcity of multi-illumination datasets capturing the same scene under varied lighting conditions. Acquiring such data in the real world requires controlled environments, calibrated equipment, and repeated captures, making it costly and impractical. As a result, most existing approaches rely on synthetic data or focus on narrow domains like portraiture, limiting their ability to generalize to real-world scenes.
+
+To address the lack of multi-illumination data, recent works [69, 39] have decomposed the relighting task into two stages: an inverse rendering step that estimates scene attributes such as albedo, normal, and depth (i.e., G-buffers), followed by a forward rendering step that synthesizes relit images conditioned on these estimates. This pipeline avoids the need for multi-illumination supervision, allowing each stage to be trained separately using single-illumination data. However, inverse and forward rendering are inherently coupled, and modeling them separately with discrete intermediate representations introduces significant limitations. The forward renderer is highly sensitive to errors in
+
+![](images/fc6c5a544ffd02a281c2660ab72e81aace09886fc5493d96b09e463ca8bad158.jpg)  
+Input & Albedo Pred.
+
+![](images/208641accb7554c730b31a50036855db1dfb948487f5bbfd84475c68633a62d5.jpg)
+
+![](images/1bf5ee94f42ab67f801152baf0a4aec309ee6c6596935e8f84766762909d39e9.jpg)  
+Figure 1: Given an input image (top left) or video, our method jointly estimates albedo (bottom left) and synthesizes relit videos with novel lighting conditions using provided HDR probes. Notably, our estimated albedo maps effectively demodulate shadows and specular highlights, while the relit images exhibit plausible shadows and specular highlights.
+
+![](images/a6351b86d69858856971b3826a01d5e9ff1294acbe1db816c0a24dbbde7f8793.jpg)  
+Relighting I
+
+![](images/a8dcd2ae8d563c77cb82017138edb85690bc1577d2fda081955e422cbdc389ee.jpg)
+
+![](images/5726e91013a41af0ac8b4723820ce445c69344582baa72a6995d2f0f0588eab8.jpg)  
+Relighting II
+
+![](images/381e3df3994b237b726c96534a8afda236e2a7f7a49404489e9e67a8bcb5034f.jpg)
+
+the estimated G-buffers and struggles to capture complex material properties not encoded by them, such as transparency and subsurface scattering.
+
+Learning an end-to-end relighting model in low data regime is challenging. While prior methods for intrinsic estimation (e.g., geometry [28, 19] and albedo [32, 39]) trained on synthetic data often generalize well to real-world scenes, we observe that directly training relighting models as video-to-video translation leads to poor generalization to unseen domains. We argue that robust relighting requires an explicit understanding of illumination and scene properties.
+
+Hence, we propose a relighting framework that jointly models the distribution of scene intrinsics and illumination. Inspired by VideoJAM [12], we train a video generative model that jointly denoises latent space for relighting and albedo demodulation in a single pass. Practically, we concatenate both latent representations and treat them as a single video clip. This design is motivated by the hypothesis that demodulation provides a strong prior for the relighting task, such as removing shadows. This joint formulation encourages the model to learn an internal representation of scene structure, leading to improved generalization across diverse and unseen domains. Our approach contrasts with two-stage pipelines that rely on explicit G-buffer estimation. Instead, we implicitly reason about intrinsic scene representation, enabling better representation learning, reducing error accumulation, and modeling of complex visual effects such as specular highlights, transparency, and subsurface scattering.
+
+Our formulation also offers flexibility to learn from diverse sources of supervision. We leverage high-quality synthetic data for full supervision and complement it with real-world single illumination videos that can be auto-labeled at scale. This hybrid training strategy enables the model to handle complex lighting effects while significantly improving generalization to unseen domains, see Figure 1. UNIRELIGHT enables high-quality relighting and intrinsic decomposition from a single input image or video, producing temporally consistent shadows, reflections, and transparency, and outperforms state-of-the-art methods.
+
+# 2 Related Work
+
+Inverse rendering estimates intrinsic scene properties such as geometry, materials, and lighting from input images. Traditional approaches use hand-crafted priors within an optimization framework [34, 3, 21, 11, 77, 2] to handle low-order effects like diffuse shading and reflectance. Recently, the field has been revitalized by applying supervised and self-supervised learning [4, 33, 35, 57, 66, 36, 37, 10, 61, 60, 63, 6] to inverse rendering tasks. These methods typically require large, domain-specific datasets, and struggle to generalize outside the training domain.
+
+![](images/df509f8a6ff6b060ee5d27a3a188f946bd93ddd2920df4bfdd2649b992f30aed.jpg)  
+Figure 2: Method overview. Given an input video  $\mathbf{I}$  and a target lighting configuration  $(\mathbf{E}_{\mathrm{ldr}},\mathbf{E}_{\mathrm{log}},\mathbf{E}_{\mathrm{dir}})$ , our method jointly predicts a relit video  $\hat{\mathbf{l}}_E$  and its corresponding albedo  $\hat{\mathbf{a}}$ . We use a pretrained VAE encoder-decoder pair  $(\mathcal{E},\mathcal{D})$  to map input and output videos to a latent space. The latents for the target relit video and albedo are concatenated along the temporal (frame) dimension with the encoded input video. Lighting features  $\mathbf{h}^{\mathbf{E}}$ , derived from the environment maps, are concatenated along the channel dimension with the relit video latent. A finetuned DiT video model denoises the joint latent according to Equation 1, enabling consistent generation of both relit appearance and intrinsic decomposition.
+
+Most related to our approach are methods that leverage large generative image and video models for inverse rendering [18, 32, 54, 40, 30, 14, 47, 41]. Notably, RGB  $\leftrightarrow$  X [69] demonstrates both intrinsic decomposition and neural rendering from intrinsics using finetuned image diffusion models. The DiffusionRenderer [39] extends RGB  $\leftrightarrow$  X to video and also supports relighting.
+
+Relighting is the task of modifying the lighting conditions in images or videos. Many methods first reconstruct 3D scenes from multi-view images [46, 22, 13, 9, 71, 76, 70, 56, 62, 38, 64, 42, 58, 25]. Previously, surface reflectance has been captured using calibrated light stages [15]. Now, relighting is supported by material properties recovered from inverse rendering. These methods typically require per-scene optimization and are limited to static, object-centric scenes with single illumination. Training across multiple scenes has led to the exploration of latent feature learning [78, 43, 72, 7], often incorporating neural rendering modules with intrinsic buffers as priors [53, 20, 50, 31, 65].
+
+Recent approaches [55, 31, 68, 26, 39, 5, 75, 45] leverage diffusion models for relighting tasks. These methods are often domain-specific, such as portraits, single objects, and outdoor scenes. Despite promising results, a significant challenge remains the need for multi-illumination datasets [48], which are difficult to capture at scale.
+
+Joint generative modeling approaches enable diffusion models to predict multiple modalities. Matrix3D [44] predicts pose estimation, depth, and novel view synthesis using a single DiT [52] model. VideoJAM [12] extends this by predicting both generated pixels and their corresponding motion from a single DiT. We leverage this approach to jointly predict a relit image and albedo.
+
+# 3 Preliminaries: Video Diffusion Models
+
+Diffusion models approximate a data distribution  $p_{\mathrm{data}}(\mathbf{I})$  by learning to iteratively denoise samples corrupted by Gaussian noise [59, 23, 17]. For efficiency, most video diffusion models (VDMs) operate in a lower-dimensional latent space [8, 1]. Given an RGB video  $\mathbf{I} \in \mathbb{R}^{L \times H \times W \times 3}$  consisting of  $L$  frames at resolution  $H \times W$ , a pre-trained VAE encoder  $\mathcal{E}$  encodes the video into a latent tensor  $\mathbf{z} = \mathcal{E}(\mathbf{I}) \in \mathbb{R}^{l \times h \times w \times C}$ . Then, the final video  $\hat{\mathbf{I}}$  is reconstructed by decoding  $\mathbf{z}$  with a pre-trained VAE decoder  $\mathcal{D}$ . Both the training and inference stages of the VDM are conducted in this latent space.
+
+In this work, we fine-tune a recent Diffusion Transformer (DiT) video model, Cosmos-Predict1 [1]. Encoding and decoding to and from latent space are performed by the pre-trained VAE Cosmos-1.0-Tokenizer-CV8x8x8, which compresses the video by a factor of eight along the spatial and temporal dimensions:  $l = \frac{L}{8}$ ,  $C = 16$ ,  $h = \frac{H}{8}$ , and  $w = \frac{W}{8}$ . The base model supports text- and image-guided video generation at a resolution of  $704 \times 1280$  pixels.
+
+To train the VDM, noisy versions  $\mathbf{z}_{\tau} = \alpha_{\tau}\mathbf{z}_0 + \sigma_{\tau}\epsilon$  are constructed by adding Gaussian noise  $\epsilon$ , with the noise schedule provided by  $\alpha_{\tau}$  and  $\sigma_{\tau}$  following the EDM [27]. The diffusion model parameters  $\theta$  of the denoising function  $\mathbf{f}_{\theta}$  are optimized using the denoising score matching objective [27]. Once trained, iteratively applying  $\mathbf{f}_{\theta}$  to a sample of Gaussian noise will produce a sample of  $p_{\mathrm{data}}(\mathbf{I})$ .
+
+# 4 Method
+
+We propose a generative relighting framework by fine-tuning a DiT-based video diffusion model [52, 1], which jointly predicts the albedo and relit appearance from an input image or video under a target lighting condition.
+
+At the core of our method is a joint denoising architecture: the latent representations for albedo and relit video are concatenated at the token level and denoised in a single pass using the DiT model. This formulation enables cross-modal interaction via self-attention, allowing the model to capture shared scene structure and improve generalization and temporal consistency.
+
+We leverage a combination of synthetic datasets, multi-illumination data, and auto-labeled real-world data to train our model, ensuring robust generalization and high-quality output. In the following sections, we detail the model architecture, data strategy, and training objectives.
+
+# 4.1 Model Design
+
+Given an input video  $\mathbf{I}$  and a temporally-varying target lighting condition  $\mathbf{E} \in \mathbb{R}^{L \times H \times W \times 3}$  of the same dimensions, our goal is to train a model  $\mathbf{f}_{\theta}$  that jointly denoises the albedo  $\mathbf{a}$  of the input video and a relit video  $\mathbf{I}_{\mathbf{E}}$  under the target illumination  $\mathbf{E}$ .
+
+As illustrated in Figure 2, the model comprises a VAE encoder-decoder pair, denoted  $(\mathcal{E},\mathcal{D})$ , and a transformer-based denoising function,  $\mathbf{f}_{\theta}$ . We use the VAE encoder  $\mathcal{E}$  to separately encode the input video  $\mathbf{I}$ , the albedo  $\mathbf{a}$ , and the relit video  $\mathbf{I_E}$ , producing the corresponding latent tensors  $(\mathbf{z^I},\mathbf{z^a},\mathbf{z^E})$ .
+
+Unlike VideoJAM [12], which compresses a pair of latents  $(\mathbf{z}^x, \mathbf{z}^y)$  using a linear layer, we find this approach does not yield acceptable quality for our applications. We adopt a simple yet effective strategy: concatenating the latents  $\mathbf{z}^{\mathbf{l}}$ ,  $\mathbf{z}^{\mathbf{a}}$ , and  $\mathbf{z}^{\mathbf{E}}$  along the temporal (frame) dimension. This formulation enables the DiT model to apply full self-attention across input, albedo, and relit frames, facilitating cross-modal information exchange.
+
+Token embeddings. To distinguish between the three modalities in the concatenated sequence, we combine standard RoPE positional embeddings with dedicated type embeddings. We first apply the same positional embedding to each of the three video clips, encoding the spatial-temporal position of tokens within each clip. To indicate modality, we introduce a learnable type embedding  $\mathbf{c}_{\mathrm{emb}} \in \mathbb{R}^{K_{\mathrm{emb}} \times C_{\mathrm{emb}}}$ , where  $K_{\mathrm{emb}} = 3$  denotes the number of video types. Each type embedding is broadcast across spatial-temporal dimensions and concatenated along the channel dimension with its corresponding latent representation:  $\mathbf{c}_{\mathrm{emb}}^{0}$  is appended to  $\mathbf{z}^{\mathbf{E}}$ ,  $\mathbf{c}_{\mathrm{emb}}^{1}$  to  $\mathbf{z}^{\mathbf{a}}$ , and  $\mathbf{c}_{\mathrm{emb}}^{2}$  to  $\mathbf{z}^{\mathbf{I}}$ .
+
+Condition mask. We attach a binary mask to each frame indicating whether it should be treated as a condition (e.g., input source video) or a denoising target (e.g., relit video). During training, we randomly vary which modalities are used as inputs, allowing the model to generalize under partial supervision. For instance, we may randomly treat a as either a conditioning input or a prediction target (see Section 4.3 for details).
+
+Encoding HDR lighting. Environment maps contain high dynamic range (HDR) values that often exceed the intensity range of standard images, while the VAEs used in latent diffusion models are trained on low dynamic range (LDR) inputs and cannot directly encode these high-intensity signals.
+
+To address this, we follow prior work [26, 39] and represent the lighting condition using three complementary buffers: (1) an LDR panorama  $\mathbf{E}_{\mathrm{ldr}}$  obtained via standard Reinhard tonemapping; (2) a normalized log-intensity map  $\mathbf{E}_{\mathrm{log}} = \log (\mathbf{E} + 1) / E_{\mathrm{max}}$ , where  $E_{\mathrm{max}}$  is the maximum log intensity; and (3) a directional encoding  $\mathbf{E}_{\mathrm{dir}} \in \mathbb{R}^{L \times H \times W \times 3}$ , where each pixel stores a unit vector indicating its direction in the camera coordinate system. These representations are passed through the VAE encoder, yielding  $\mathbf{h}^{\mathbf{E}} = (\mathcal{E}(\mathbf{E}_{\mathrm{ldr}}), \mathcal{E}(\mathbf{E}_{\mathrm{log}}), \mathcal{E}(\mathbf{E}_{\mathrm{dir}}))$  which is concatenated along the channel dimension with  $\mathbf{z}^{\mathbf{E}}$ . We also append a binary condition mask to indicate whether lighting features are present. For input video and albedo tokens, we use zero-padded placeholders to maintain shape consistency.
+
+# 4.2 Data Strategy
+
+Similar to prior work [39], we combine a large-scale synthetic dataset with smaller real-world datasets. The synthetic dataset offers a large volume of high-quality data, which, when combined with
+
+powerful diffusion models, shows impressive generalization to unseen domains [28, 19]. However, using synthetic target images biases the model output, resulting in a rendered look. Therefore, we also utilize smaller and lower-quality datasets of automatically labeled real-world data.
+
+Synthetic data curation. Our synthetic dataset consists of rendered video clips that are tuples  $(\mathbf{I},\mathbf{I}_{\mathbf{E}},\mathbf{a},\mathbf{E})$  of input video, relit result, albedo, and environment map. To create a large dataset with complex and diverse scenes and lighting conditions, we follow the methodology of the Diffusion-Renderer [39] and procedurally generate simple scenes with randomized environment map lighting. We curate a collection of 36,500 3D objects from the Objaverse LVIS subset, 4,260 PBR materials, and 766 HDRI environment maps, gathered from publicly available resources. Each scene contains a ground plane with a randomly selected material, and up to three randomly placed 3D objects. We perform collision detection to avoid intersecting objects. Additionally, we place up to three primitive objects (cube, sphere, and cylinder) with randomized shapes and materials, to increase variety. A randomly selected HDR environment map illuminates each scene. We generate videos with random motions including camera orbits, camera oscillation, lighting rotation, object rotation and translation. Each scene is rendered with two different illuminations under the same motion.
+
+We rendered the dataset using a custom path tracer based on OptiX [51] at high sample counts with path length three to capture global illumination effects, producing a total of 108k videos with 57 frames per video at a resolution of  $704 \times 1280$  pixels.
+
+MIT multi-illumination labeling. The multi-illumination dataset of Murmann et al. [48] consists of 985 real-world indoor scenes for training and 30 scenes for testing, each lit under 25 different conditions. Each image contains a reflective chrome sphere that has been isolated and exported as an HDR image, which can be easily remapped to the latitude-longitude representation expected by our model. We create a dataset by randomly selecting pairs from the 25 lighting conditions and extracting  $(\mathbf{I},\mathbf{E},\mathbf{I}_{\mathbf{E}})$ .
+
+To obtain pseudo-groundtruth albedo labels a, we re-implement the inverse renderer from Diffusion-Renderer [39] based on the Cosmos-1.0-Diffusion-7BVideo2World model [49], fine-tuned on our synthetic dataset. The resulting model produces high-quality albedo estimates across diverse images and videos. For each scene, we estimate albedo under all 25 lighting conditions and average the results to obtain a stable albedo map.
+
+Real-world auto-labeling. While multi-illumination datasets provide effective supervision for relighting, they are costly to acquire and limited in scale. In contrast, the Internet offers an abundance of high-quality videos captured under single illumination. To leverage this resource, we curate 150k real-world video clips, each with 57 frames at a resolution of  $704 \times 1280$ , and automatically annotate them with albedo maps to create paired data (I, a) using the reproduced inverse rendering model. These auto-labeled RGB-albedo pairs significantly increase the diversity and realism of our training set, enabling improved generalization to real-world scenes.
+
+# 4.3 Training
+
+Our model is trained on a combination of the synthetic video dataset, the MIT multi-illumination dataset, and real-world auto-labeled data. For image datasets, we treat images as single-frame videos. For both the synthetic video dataset and the MIT multi-illumination dataset, each data sample consists of an input video  $\mathbf{I}$ , its corresponding albedo  $\mathbf{a}$ , a new environment map  $\mathbf{E}$ , and the target relit video  $\mathbf{I}_{\mathbf{E}}$  under this new illumination. The target latent variable for these datasets is constructed by concatenating the latent of the relit video,  $\mathbf{z}_0^{\mathbf{E}}$ , and the corresponding albedo,  $\mathbf{z}_0^{\mathbf{a}}$ , along the temporal dimension. Noise is introduced independently to both  $\mathbf{z}_0^{\mathbf{E}}$  and  $\mathbf{z}_0^{\mathbf{a}}$  to produce  $\mathbf{z}_{\tau}^{\mathbf{E}}$  and  $\mathbf{z}_{\tau}^{\mathbf{a}}$ . The model parameters are optimized by minimizing the objective function:
+
+$$
+\hat {\mathbf {z}} ^ {\mathbf {E}} (\theta), \hat {\mathbf {z}} ^ {\mathbf {a}} (\theta) = \mathbf {f} _ {\theta} \left(\left[ \mathbf {z} _ {\tau} ^ {\mathbf {E}} + \mathbf {h} ^ {\mathbf {E}}, \mathbf {z} _ {\tau} ^ {\mathbf {a}}, \mathbf {z} ^ {\mathbf {I}} \right]; \mathbf {c} _ {\text {e m b}}, \tau\right) \tag {1}
+$$
+
+$$
+\mathcal {L} (\theta) = \mathbb {E} _ {\left(\mathbf {z} _ {0} ^ {\mathbf {E}}, \mathbf {z} _ {0} ^ {\mathbf {a}}\right) \sim p _ {\mathrm {d a t a}, \epsilon} \sim \mathcal {N} (0, \sigma^ {2} I)} \left[ \left\| \hat {\mathbf {z}} ^ {\mathbf {E}} (\theta) - \mathbf {z} _ {0} ^ {\mathbf {E}} \right\| _ {2} ^ {2} + \lambda_ {\mathbf {a}} \| \hat {\mathbf {z}} ^ {\mathbf {a}} (\theta) - \mathbf {z} _ {0} ^ {\mathbf {a}} \| _ {2} ^ {2} \right], \tag {2}
+$$
+
+where  $[\cdot]$  denotes concatenation in the temporal dimension, and  $\lambda_{\mathbf{a}} = 0.1$  is a scalar weight for albedo.
+
+Training strategy. Our training process incorporates specific conditioning strategies tailored to the data source. For synthetic data and the MIT multi-illumination dataset, we apply three different
+
+![](images/60f918d223dffd23eddb93620c43a0b545d8da99ef0317adce294d8de8b584e7.jpg)  
+Figure 3: Qualitative comparison on the synthetic dataset and MIT multi-illumination dataset. Our method produces high-quality inter-reflections and shadows in synthetic scenes (top rows). Crucially, on the MIT multi-illumination dataset (bottom rows), it delivers relighting results with higher accuracy than baselines, which fail when faced with complex materials.
+
+conditioning strategies. In  $12\%$  of training steps, the input video is dropped (i.e.,  $\mathbf{z}^{\mathbf{I}} = \mathbf{0}$ ), and the ground truth albedo serves as the sole conditioning signal. The denoising function is then  $\mathbf{f}_{\theta}([\mathbf{z}_{\tau}^{\mathbf{E}} + \mathbf{h}^{\mathbf{E}}, \mathbf{z}_0^{\mathbf{a}}, \mathbf{0}]; \mathbf{c}_{\mathrm{emb}}, \tau)$ . In another  $18\%$  of the training steps, the model is conditioned on both the input video  $\mathbf{z}^{\mathbf{I}}$  and the ground truth albedo, resulting in the function  $\mathbf{f}_{\theta}([\mathbf{z}_{\tau}^{\mathbf{E}} + \mathbf{h}^{\mathbf{E}}, \mathbf{z}_0^{\mathbf{a}}, \mathbf{z}^{\mathbf{I}}]; \mathbf{c}_{\mathrm{emb}}, \tau)$ . When using these two specific conditions, the conditioning latents of the input video and albedo have their corresponding condition masks set to 1. For the remaining  $70\%$  of the training steps, we use the default denoising function where we take input video tokens as conditions and predict the denoised relit video and albedo.
+
+Our real-world auto-labeled data consists of RGB videos and corresponding albedos but lacks pairwise RGB videos and environment maps. The provided albedo  $\mathbf{a}$  is used as a condition, and the RGB video is treated as the target relit video  $\mathbf{I}_{\mathbf{E}}$ . Since the original input video and the environment map information are unavailable for this dataset, their respective representations are set to  $\mathbf{0}$ : The environment map embedding  $\mathbf{h}^{\mathbf{E}}$  within the primary input array becomes  $\mathbf{0}$ , and the conditional input video representation  $\mathbf{z}^{\mathbf{I}}$  is dropped. The denoising function is thus  $\mathbf{f}_{\theta}([ \mathbf{z}_{\tau}^{\mathbf{E}} + \mathbf{0}, \mathbf{z}_0^{\mathbf{a}}, \mathbf{0} ]; \mathbf{c}_{\mathrm{emb}}, \tau)$ . We adopt a 10% probability of dropping conditions to enable classifier-free guidance [24].
+
+# 5 Results
+
+Before evaluating our method, we introduce baselines, metrics, and datasets. Then we conduct ablation studies on the principle of joint modeling and real-world auto-labeling of data. We refer to model implementation details in the Supplement.
+
+Baselines. For the relighting task, we compare with the 2D methods DiLightNet [68], NeuralGaffer [26], and DiffusionRenderer [39]. As the original DiffusionRenderer is built on Stable Video Diffusion [8], to isolate the effect of the base model and enable a fair algorithmic comparison, we re-implement DiffusionRenderer using the same Cosmos [49] backbone as our method, and adopt a data curation strategy closely following the original paper. We refer to this enhanced variant as DiffusionRenderer (Cosmos). For albedo estimation, we compare with IntrinsicImageDiffusion [32] and DiffusionRenderer [39].
+
+Metrics. We apply the standard image metrics PSNR, SSIM, and LPIPS [74] to evaluate relighting and albedo estimation results. For relighting, we also conduct a user study to evaluate the perceptual quality of the produced results. Following prior works [32, 26, 39], we counteract scale ambiguity in both relighting and albedo estimation by a three-channel scaling factor using least-squares error minimization before computing the metrics.
+
+![](images/ed9e3bed342f3b27c5f63df9c1799ad325baffcbcbf8f3f37edf91b9251ae4f3.jpg)  
+Figure 4: Qualitative comparison on in-the-wild data. Our method generates more plausible results than the baselines, with higher quality and more realistic appearance.
+
+Table 1: Quantitative evaluation of relighting, including a user study, where "Ours preferred" indicates the preference over the baselines. A preference over the  $>50\%$  indicates Ours outperforming baselines.  
+
+<table><tr><td rowspan="2"></td><td colspan="3">SyntheticScenes</td><td colspan="4">MIT multi-illumination</td></tr><tr><td>PSNR ↑</td><td>SSIM ↑</td><td>LPIPS ↓</td><td>PSNR ↑</td><td>SSIM ↑</td><td>LPIPS ↓</td><td>Ours preferred</td></tr><tr><td>DiLightNet [68]</td><td>19.13</td><td>0.584</td><td>0.319</td><td>15.79</td><td>0.539</td><td>0.368</td><td>92% ± 8%</td></tr><tr><td>Neural Gaffer [26]</td><td>19.17</td><td>0.638</td><td>0.263</td><td>17.87</td><td>0.683</td><td>0.241</td><td>84% ± 2%</td></tr><tr><td>DiffusionRenderer [39]</td><td>24.17</td><td>0.768</td><td>0.217</td><td>16.99</td><td>0.582</td><td>0.342</td><td>96% ± 4%</td></tr><tr><td>DiffusionRenderer (Cosmos)</td><td>26.61</td><td>0.841</td><td>0.222</td><td>17.29</td><td>0.622</td><td>0.355</td><td>88% ± 8%</td></tr><tr><td>Ours</td><td>26.97</td><td>0.847</td><td>0.190</td><td>20.76</td><td>0.749</td><td>0.251</td><td>n/a</td></tr></table>
+
+Datasets. We have curated a high-quality synthetic dataset named SyntheticScenes for quantitative and qualitative evaluation of both relighting and albedo estimation tasks. This dataset includes 3D assets from PolyHaven [67] and Objaverse [16], ensuring that no assets were used in the training of our method or any of the baseline methods. SyntheticScenes features 40 scenes, each constructed with a plane textured with random physically-based materials. Each scene is rendered into a 57-frame video sequence under four different lighting conditions, incorporating orbiting camera motions and rotating environment lighting. We cycle through the lighting conditions, selecting one video as the input, and the following as the relighting target, resulting in four different relighting tasks per scene. Additionally, we utilize the test set of MIT multi-illumination benchmark [48], which includes 30 scenes under 25 different illuminations. In this dataset, images are captured sequentially under adjacent flashlight illuminations. For our relighting tasks, the image captured under the  $i$ -th lighting condition serves as input, while the image captured under the  $(i + 12)$ -th lighting condition is utilized as the target illumination.
+
+# 5.1 Evaluation
+
+Qualitative comparison. In Figure 3 and Figure 4, we present a comparative analysis of our method against recent state-of-the-art relighting techniques: DiLightNet [68], NeuralGaffer [26], and DiffusionRenderer [39]. Our approach demonstrates superior performance, particularly in handling intricate shadows and inter-reflections. As illustrated in Figure 3, while DiffusionRenderer performs well on the synthetic dataset, it struggles to accurately represent complex materials—such as anisotropic surfaces, glass, and transparent objects—when utilizing G-buffers as an intermediate state, leading to suboptimal results. NeuralGaffer and DiLightNet do not produce accurate shadows and reflections, leading to poor relighting effects. Specifically, NeuralGaffer often makes very subtle, or no changes to the input video.
+
+Figure 4 compares relighting results on in-the-wild data. For the first two object-centric scenes, we provided DiLightNet and NeuralGaffer with masks (we used DiLightNet's auto-masking, which is based on Segment Anything [29]) to produce more reasonable results. Notably, DiffusionRenderer struggles with fur, human skin, and car windows in the first three cases, as these are hard to represent
+
+![](images/b275fbeeafd15a4d9815273b380719173c1f9edb9aa0de0c3a23dd83ad06c11c.jpg)  
+Input
+
+![](images/0e55a66ac171cdf9020339e006934cc33e4af8591873f43f019e287b957fedfb.jpg)  
+Ours (w/o joint modeling)
+
+![](images/ab9db577e0650f0d55a76b509e3f52ca5cd206c4e0f02056a84d5d673f2026ed.jpg)  
+Shadow baking artifact
+
+![](images/0f07bff43fb710240c918706bdeed9d742b97215ba3b04557b20467fc28d0e4b.jpg)  
+Ours
+
+![](images/7d2117759fe1989dfb9c0819505b15ef9dbf84c33da59b8b62d61c02ad5d78d7.jpg)  
+Env.
+
+![](images/d93f1f37d387e85919442ffe1723fcc485080d8758bd9c523824ea2a533a9d90.jpg)  
+Figure 5: Ablation on joint modeling. Relighting results on urban street scenes. The orange and green crops highlight regions where the pure relighting model (w/o joint modeling) clearly bakes shadows from the input image into the relit result. Our joint model correctly demodulates the shadows.
+
+![](images/e2a2b120511c019b88a902a607b920f71210021352e0813725cb09314c5c4eae.jpg)
+
+![](images/8d840c7fd572607d10c730a07669fc27582f62339a4a945cbc30c1b1426d131f.jpg)
+
+![](images/b34008aeb35fe237d25dff041db16693a2638d99de53962a288d20de1e488bc9.jpg)
+
+![](images/21f19722a40889d777fad554f083ba7fb24f3e7e3be106c4ee27b0ec71131b1c.jpg)
+
+with simple G-buffers. It also misestimates the material of the plastic basin, leading to metal-like appearances. Overall, our relighting model is more general-purpose and can be used more effectively in diverse scenes, yielding comparatively more accurate and high-fidelity relighting results.
+
+Quantitative comparison. In Table 1, we present a comparative analysis of our method against all baselines on both SyntheticScenes and the MIT multi-illumination benchmark [48]. DiffusionRenderer works well on SyntheticScenes, and the Cosmos version—a stronger base model—leads to improved fidelity. However, on the MIT multi-illumination dataset, DiffusionRenderer fails to model the more challenging materials using only the G-buffers, and even the Cosmos version cannot bridge this gap.
+
+An outlier is observed in the LPIPS score for NeuralGaffer on the MIT multi-illumination dataset. As mentioned before, NeuralGaffer makes minimal changes to the input and performs poorly, but still achieves the lowest LPIPS score. We speculate that LPIPS considers the images sufficiently similar due to the relatively small deviation in light direction for this dataset.
+
+User study. We conducted a user study to evaluate the perceptual quality of our relighting method compared to the baseline methods on the MIT multi-illumination dataset. Participants were shown a ground-truth relit image alongside two relighting results—one generated by our method and the other by a baseline model, with the order randomly shuffled. They were asked to select the result that more closely resembled the ground truth, considering aspects such as transparency, shadows, and reflections. For each sample pair, 11 users made a binary selection, and majority voting determined the preferred method for each comparison. We repeated the study three times, involving a total of 33 users, and report the average percentage of samples where our method is preferred over baselines in Table 1, along with the standard deviation for the three experiments. Our results are consistently preferred, strongly outperforming the other baselines.
+
+Albedo estimation. While our primary focus is on relighting, we also evaluate the quality of our albedo estimates compared to the baselines in Table 2. While the Cosmos version of DiffusionRenderer has a slight edge, our method performs or on par with previous work in all metrics.
+
+Table 2: Quantitative evaluation of albedo estimation on SyntheticScenes.  
+
+<table><tr><td></td><td colspan="3">SyntheticScenes</td></tr><tr><td></td><td>PSNR ↑</td><td>SSIM ↑</td><td>LPIPS ↓</td></tr><tr><td>IntrinsicImageDiffusion [32]</td><td>16.41</td><td>0.543</td><td>0.395</td></tr><tr><td>DiffusionRenderer [39]</td><td>26.22</td><td>0.837</td><td>0.166</td></tr><tr><td>DiffusionRenderer (Cosmos)</td><td>28.56</td><td>0.911</td><td>0.131</td></tr><tr><td>Ours</td><td>28.07</td><td>0.877</td><td>0.167</td></tr></table>
+
+# 5.2 Ablation Study
+
+Ablation on joint modeling. We ablate our joint modeling approach with quantitative and qualitative results in Table 3 and Figure 5. We compare our model to a pure relighting model (without joint modeling), whose denoising function is defined as  $\mathbf{f}_{\theta}([{\bf z}_{\tau}^{E} + {\bf h}^{{\bf E}}];{\bf z}^{{\bf I}},\tau)$ . Both models are trained exclusively on synthetic data. The quantitative results in Table 3 reveal that our joint model slightly improves relighting quality compared to the ablated model. The table also shows that providing ground truth albedo as input to our joint model further enhances quality, highlighting the model's capacity to leverage albedo information effectively. For a qualitative assessment, Figure 5 compares the results on urban street scenes outside the training distribution, where urban street scenes typically exhibit strong shadows under sunlight. The joint modeling approach demonstrates superior generalization, characterized by reduced false shadowing, as clearly shown in the figures, whereas the model without joint modeling largely bakes the shadow from the input.
+
+![](images/9c38f207394e7eba94b61b31623f9174af5ae5f79a1e5c224763f0dd5a692f56.jpg)  
+Input
+
+![](images/fb8ffc0768c5be31acc81960f2c72cc0fdc5e50f8be0571a998446f10ff336c4.jpg)  
+w/o auto-labeled data
+
+![](images/22fe44786b4fdbb163a4cb0551fd392a3641a7afa31ad17d444ec7955de383b8.jpg)  
+Ours  
+Env.
+
+![](images/e2cf22c2c3cb7bde8321b214274b0bcac7066151201d6e205a46b46be2c34c91.jpg)  
+Figure 6: Ablation on real-world auto-labeled data. Although the dataset is sparsely labeled, it helps the model generalize to natural images.
+
+![](images/f917291a9639286bbd2af6d17d1cfdd6bb467b15aa58e4d5fd7cbf75af1cfd51.jpg)
+
+![](images/8c5cb56900c83d9d094594c27de01463692745223e21a9d2d15d898983c62d66.jpg)
+
+![](images/4ab4baa0c41e27587f2e40b580c998a710472dbf153717bcd275ff07b245e34d.jpg)
+
+![](images/7a92f8a7e6860de00548fdc3c8e381c71340efed2e59f67d23dae29d9aa2ad9a.jpg)  
+Input
+
+![](images/53c51084608832fef02eaadda02ad40a214e2b903caf3bc1ba88a2afeddabd5e.jpg)  
+Random Samples
+
+![](images/dd44e68064fb2daf4346310f50e0c78dc4e071cb7e5448c9f385c1b158c32082.jpg)  
+Figure 7: Application for data augmentation. The top left image with green outline is the input image.
+
+![](images/e02b9445883d58acdd36a1dc0bef6d331ce2372509d41ea8e2d289cd0e57266a.jpg)
+
+Table 3: Ablations on joint modeling designs evaluated on the SyntheticScenes dataset.  
+
+<table><tr><td>Ablated Versions</td><td>PSNR ↑</td><td>SSIM ↑</td><td>LPIPS ↓</td></tr><tr><td>Ours (w/o joint modeling)</td><td>26.42</td><td>0.842</td><td>0.191</td></tr><tr><td>Ours</td><td>26.97</td><td>0.847</td><td>0.190</td></tr><tr><td>Ours (w/ GT albedo)</td><td>27.15</td><td>0.857</td><td>0.181</td></tr></table>
+
+Table 4: User study of relighting on StreetScenes. Each row compares an ablated variant against the base version (Ours w/o auto-labeled data), reporting percentage of samples where users prefer the base version.  
+
+<table><tr><td>Ablated Versions</td><td>Ours (w/o auto-labeled data) preferred</td></tr><tr><td>Ours (w/o auto-labeled data, w/o joint modeling)</td><td>68% ± 14%</td></tr><tr><td>Ours (w/ auto-labeled data)</td><td>45% ± 8%</td></tr><tr><td>Ours (w/o auto-labeled data, w/ inference-time albedo)</td><td>54% ± 13%</td></tr></table>
+
+Ablation on real-world automatically labeled data. We ablate the usefulness of our automatically labeled real-world data in Figure 6. The model trained on solely the synthetic and MIT multi-illumination datasets (center column) exhibits noticeable artifacts for out-of-focus regions and outdoor scenes. This is expected, as both datasets have very limited depth of field and primarily feature indoor scenes or lack sky-like backgrounds. As shown in the figure, even though the real-world auto-labeled dataset contains only a subset (I, a) of the video labels, it significantly helps the model generalize to out-of-domain data, thereby improving image quality.
+
+User study. We conducted a user study to evaluate the perceptual impact of our design choices on relighting quality, using 19 urban street scenes. For each scene, participants were shown a reference image and two relit videos produced by ablated versions of our method (randomized order). They were asked to select the video with more realistic lighting, focusing on aspects such as shadows and reflections. As before, each comparison was evaluated by 11 participants through binary selection, and the preferred result was determined by majority vote. We repeated the study three times with a total of 33 participants and report the average preference rate and the standard deviation in Table 4.
+
+When trained only on multi-illumination data (w/o auto-labeled data), the joint modeling variant is strongly preferred over direct relighting. Adding real-world auto-labeled data further improves perceptual quality, consistent with our qualitative and quantitative findings. When comparing models with and without estimated albedo at inference time, the larger standard deviation in user preference suggests the perceptual quality is comparable. This indicates our method does not strictly rely on estimated albedo at test time.
+
+# 5.3 Application: Illumination Augmentation
+
+Our model's strong generalization capability enables effective data augmentation for scenarios such as driving and robotics scenes. We test our model under conditions without environment maps and with different random seeds. Figure 7 shows five random illuminations of one input scene. Our model generates diverse data, including nighttime and dusk scenes, demonstrating that it accurately models the illumination distribution and can sample realistic relighting results under varying lighting conditions.
+
+# 6 Discussion
+
+To overcome data scarcity and the limitations of decoupled two-stage methods in realistic relighting, we present a novel framework UNIRELIGHT, that jointly models scene intrinsics and illumination. This approach enhances generalization, reduces error accumulation, and more effectively captures complex visual effects by implicitly reasoning about scene properties rather than relying on explicit G-buffers. Furthermore, when trained on real-world auto-labeled data, our model achieves state-of-the-art, high-quality, and realistic relighting results.
+
+Limitations and future work. While our method performs well on diverse data, our model cannot handle emitting objects, such as toggling lights within scenes. Our design, which is focused on environmental lighting, does not extend to emittance effects, which remains an area of future research. Moreover, incorporating text-based relighting could significantly enhance usability. Future work could investigate the conditioning of lighting with natural language by incorporating text crossattention in our DiT model, a feature currently not implemented. While our method can generate reasonable shadows, integrating our generative approach with explicit 3D information—such as traced shadow buffers—represents a promising direction for improving physical accuracy. Besides, in UNIRELIGHT, we incorporate the albedo to improve the model's generalization, and predicting additional properties could further enhance the model's understanding of scene structure and materials. However, memory requirements scale with the number of properties, making this approach expensive for high-resolution videos; it is worth exploring this direction further.
+
+# Acknowledgments and Disclosure of Funding
+
+The authors thank Tianshi Cao and Huan Ling for their insightful discussions that contributed to this project. This work was conducted at and supported by NVIDIA.
+
+# References
+
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+
+# NeurIPS Paper Checklist
+
+The checklist is designed to encourage best practices for responsible machine learning research, addressing issues of reproducibility, transparency, research ethics, and societal impact. Do not remove the checklist: The papers not including the checklist will be desk rejected. The checklist should follow the references and follow the (optional) supplemental material. The checklist does NOT count towards the page limit.
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+# 1. Claims
+
+Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
+
+Answer: [Yes]
+
+Justification: The abstract and introduction clearly state our contributions, which are fully supported by the experiments and analysis presented.
+
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+- The answer NA means that the abstract and introduction do not include the claims made in the paper.  
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+
+# 2. Limitations
+
+Question: Does the paper discuss the limitations of the work performed by the authors?
+
+Answer: [Yes]
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+Justification: We discuss limitations in Section 6.
+
+# Guidelines:
+
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+
+# 3. Theory assumptions and proofs
+
+Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?
+
+Answer: [NA]
+
+Justification: The paper does not contain theoretical results or formal proofs.
+
+Guidelines:
+
+- The answer NA means that the paper does not include theoretical results.  
+- All the theorems, formulas, and proofs in the paper should be numbered and cross-referenced.  
+- All assumptions should be clearly stated or referenced in the statement of any theorems.  
+- The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.  
+- Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.  
+- Theorems and Lemmas that the proof relies upon should be properly referenced.
+
+# 4. Experimental result reproducibility
+
+Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
+
+Answer: [Yes]
+
+Justification: We include all necessary details for reproduction in the main paper and supplementary materials.
+
+Guidelines:
+
+- The answer NA means that the paper does not include experiments.  
+- If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.  
+- If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.  
+- Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general, releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.  
+- While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example  
+(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.  
+(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.  
+(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).  
+(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
+
+# 5. Open access to data and code
+
+Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?
+
+# Answer: [No]
+
+Justification: We plan to release the code and data upon acceptance. The internal guidelines of our institution prevent us from releasing code at this stage.
+
+# Guidelines:
+
+- The answer NA means that paper does not include experiments requiring code.  
+- Please see the NeurIPS code and data submission guidelines (https://nips.cc/public/guides/CodeSubmissionPolicy) for more details.  
+- While we encourage the release of code and data, we understand that this might not be possible, so "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).  
+- The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https://nips.cc/public/guides/CodeSubmissionPolicy) for more details.  
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+- The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.
+
+- At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).  
+- Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.
+
+# 6. Experimental setting/details
+
+Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
+
+Answer: [Yes]
+
+Justification: All relevant details are provided in the paper and supplementary materials.
+
+Guidelines:
+
+- The answer NA means that the paper does not include experiments.  
+- The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.  
+- The full details can be provided either with the code, in appendix, or as supplemental material.
+
+# 7. Experiment statistical significance
+
+Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
+
+Answer: [No]
+
+Justification: Due to the high computational cost of training large-scale video diffusion models, we did not perform multiple runs to report error bars.
+
+Guidelines:
+
+- The answer NA means that the paper does not include experiments.  
+- The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.  
+- The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).  
+- The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)  
+- The assumptions made should be given (e.g., Normally distributed errors).  
+- It should be clear whether the error bar is the standard deviation or the standard error of the mean.  
+- It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a  $96\%$  CI, if the hypothesis of Normality of errors is not verified.  
+- For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).  
+- If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
+
+# 8. Experiments compute resources
+
+Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
+
+Answer: [Yes]
+
+Justification: We include sufficient information in supplementary materials.
+
+Guidelines:
+
+- The answer NA means that the paper does not include experiments.
+
+- The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.  
+- The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.  
+- The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).
+
+# 9. Code of ethics
+
+Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines?
+
+Answer: [Yes]
+
+Justification: Our work adheres to the NeurIPS Code of Ethics.
+
+Guidelines:
+
+- The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.  
+- If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.  
+- The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).
+
+# 10. Broader impacts
+
+Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
+
+Answer: [Yes]
+
+Justification: We discussed societal impacts of our work in the paper.
+
+Guidelines:
+
+- The answer NA means that there is no societal impact of the work performed.  
+- If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.  
+- Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.  
+- The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.  
+- The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.  
+- If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).
+
+# 11. Safeguards
+
+Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
+
+Answer: [NA]
+
+Justification: Our work does not involve models or data with high risk of misuse.
+
+# Guidelines:
+
+- The answer NA means that the paper poses no such risks.  
+- Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.  
+- Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.  
+- We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.
+
+# 12. Licenses for existing assets
+
+Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
+
+Answer: [Yes]
+
+Justification: Third-party assets are properly credited, and their licenses are respected.
+
+# Guidelines:
+
+- The answer NA means that the paper does not use existing assets.  
+- The authors should cite the original paper that produced the code package or dataset.  
+- The authors should state which version of the asset is used and, if possible, include a URL.  
+- The name of the license (e.g., CC-BY 4.0) should be included for each asset.  
+- For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.  
+- If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.  
+- For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.  
+- If this information is not available online, the authors are encouraged to reach out to the asset's creators.
+
+# 13. New assets
+
+Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
+
+Answer: [NA]
+
+Justification: Our paper does not release new assets.
+
+# Guidelines:
+
+- The answer NA means that the paper does not release new assets.  
+- Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.  
+- The paper should discuss whether and how consent was obtained from people whose asset is used.  
+- At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
+
+# 14. Crowdsourcing and research with human subjects
+
+Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
+
+# Answer: [Yes]
+
+Justification: We include the details of all crowdsourcing experiments in supplementary materials.
+
+# Guidelines:
+
+- The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.  
+- Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.  
+- According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
+
+# 15. Institutional review board (IRB) approvals or equivalent for research with human subjects
+
+Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
+
+# Answer: [Yes]
+
+Justification: An approval is obtained for all of our experiments.
+
+# Guidelines:
+
+- The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.  
+- Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.  
+- We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.  
+- For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
+
+# 16. Declaration of LLM usage
+
+Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
+
+# Answer: [NA]
+
+Justification: LLMs were not used in the core methods of this research.
+
+# Guidelines:
+
+- The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.  
+- Please refer to our LLM policy (https://neurips.cc/Conferences/2025/LLM) for what should or should not be described.
+
+In this Appendix, we first discuss the broader impact of our project (Appendix A). We then provide additional implementation details of our model and experiments (Appendix B), followed by further results and analysis (Appendix C). Please refer to the accompanying video for additional qualitative results and comparisons.
+
+# A Broader Impact
+
+We present UniRelight, a generative framework that jointly estimates albedo and synthesizes relit videos from a single input, enabling diverse lighting manipulation across both synthetic and real-world scenes. This capability can support a range of applications, including creative content generation, visual effects, virtual production, and potentially data augmentation for training more robust computer vision models in domains such as robotics and autonomous driving.
+
+As with all generative video models, UniRelight may reflect biases present in its training data. Such biases could lead to relighting results that fail to generalize to underrepresented scene types or lighting conditions. Furthermore, tools for lighting manipulation carry the risk of misuse, such as altering or misrepresenting visual content in sensitive contexts like surveillance or media.
+
+We discourage the use of UniRelight in applications where relighting may contribute to misinformation, misattribution, or privacy violations. In human-centric use cases, we recommend careful dataset curation to ensure fair representation across skin tones, races, and gender identities. Practitioners are encouraged to critically assess and de-bias training data to mitigate unintended harms where appropriate.
+
+# B Experimental Details
+
+# B.1 Implementation Details
+
+We fine-tune our models based on Cosmos-Predict1-7B-Video2World [49], a pre-trained DiT video diffusion model.
+
+The encoded latents  $\mathbf{z}^{\mathrm{I}},\mathbf{z}^{\mathrm{a}},\mathbf{z}^{\mathrm{E}},\mathcal{E}(\mathbf{E}_{\mathrm{ldr}}),\mathcal{E}(\mathbf{E}_{\mathrm{log}})$  and  $\mathcal{E}(\mathbf{E}_{\mathrm{dir}})$  are all in  $\mathbb{R}^{l\times h\times w\times C}$ , where  $C = 16$ . We use  $C_{\mathrm{emb}} = 3$  as the dimension of the type embedding. Thus, the concatenated tokens have a channel dimension of  $16 + 1 + (16 + 1)\times 3 + 3 = 71$ , where each latent has an associated binary condition mask (added as an extra channel), and the lighting features  $(\mathbf{E}_{\mathrm{ldr}},\mathbf{E}_{\mathrm{log}},\mathbf{E}_{\mathrm{dir}})$  each include a condition mask to indicate whether they are provided.
+
+We adopt an image-video co-training strategy and train the model in two stages. Firstly, we train with only synthetic data by mixing the image data (sampling one frame from the video data) and the video data in a ratio of  $1:1$  for 15,000 iterations. Then we train with all data with random sampling, including synthetic video data, synthetic image data, real-world auto-labeled data, and MIT multi-illumination data, in a ratio of  $8:1:3:2$  for 12,000 iterations. We augment the real-world auto-labeled data with random flipping.
+
+All training is done with a batch size of 64, using the AdamW optimizer with a learning rate of  $2 \times 10^{-5}$ , with mixed-precision (BF16) training at a resolution of  $480 \times 848$  pixels. The AdamW optimizer was employed with a weight decay of 0.1. The exponential decay rates for the moment estimates  $\beta$  are set to 0.9 for the first moment and 0.99 for the second moment, with  $\epsilon$  at  $1 \times 10^{-10}$ . The total training of two stages takes around 4 days on 32 A100 GPUs.
+
+During inference, we use 35 denoising steps. We do not apply classifier-free guidance (CFG), as we empirically found that inference without CFG yields more accurate and visually consistent results.
+
+Baseline configurations. Since DiLightNet [68] requires a text prompt per example, we use meta/llama-3.2-90b-vision-instruct to generate a short prompt for each example in the datasets based on the first image in each clip with the instruction "What is in this image? Describe the materials. Be concise and produce an answer with a few sentences, no more than 50 words."
+
+As each of the baselines generates videos in different resolutions, for UNet-based baselines, we run inference on the model with the video first resized to  $486 \times 864$  and then center-cropped to a
+
+![](images/9ce02258916a677a0f5cfd62f5a3ab405a39e7a37ef86c88932ba9b1c4275e3e.jpg)  
+Figure 8: Synthetic data visualization. Randomly sampled example images are shown from our synthetic rendering data. The top-most images show albedo maps, while the bottom two rows display rendered scenes under two different illuminations with the corresponding environment maps.
+
+Table 5: Quantitative comparison with ICLight.  
+
+<table><tr><td></td><td>PSNR ↑</td><td>SSIM ↑</td><td>LPIPS ↓</td></tr><tr><td>IC-Light [73]</td><td>18.08</td><td>0.834</td><td>0.096</td></tr><tr><td>Ours</td><td>23.19</td><td>0.901</td><td>0.079</td></tr></table>
+
+Table 6: Evaluation of inference runtime cost.  
+
+<table><tr><td></td><td>Runtime cost (seconds) ↓</td></tr><tr><td>DiffusionRenderer [39]</td><td>566.6</td></tr><tr><td>DiffusionRenderer (Cosmos)</td><td>780.0</td></tr><tr><td>Ours</td><td>445.5</td></tr></table>
+
+resolution of  $448 \times 832$ ; for our DiT-based model, we run inference on the model with the resized video with resolution of  $486 \times 864$  and then center-cropped to  $448 \times 832$  to align the results.
+
+Quantitative comparison configurations. For quantitative evaluation, we apply background masks to the synthetic dataset to focus on foreground appearance. For the MIT multi-illumination dataset, we follow the dataset protocol and mask out light probes in all outputs before computing metrics.
+
+# B.2 User Study Details
+
+We conducted two user studies on Amazon Mechanical Turk to evaluate the perceptual quality of relighting results.
+
+MIT multi-illumination dataset is a public benchmark with ground truth relighting. Participants were shown three images: a ground truth relit image and two relighting results—one generated by our method and one by a baseline model. Their task was to choose the result that more closely resembled the ground truth, considering attributes such as transparency, shadows, and reflections.
+
+The exact instructions shown to participants were as follows:
+
+Carefully compare Image A, the Reference Image, and Image B. Your task is to determine which image (A or B) is more similar to the Reference Image.
+
+To make an informed decision, you may zoom in to examine the details. Pay close attention to aspects such as lighting, reflections, and shadows, as these can affect how natural the image appears.
+
+Once you have compared the images, select the one that best matches the Reference Image.
+
+□ Image A  
+□ Image B
+
+We evaluated 30 scenes from the test set, comparing our method against four baselines. The study was repeated three times with 11 unique participants in each run. In total, this resulted in  $30 \times 4 \times 11 \times 3 = 3960$  individual comparisons.
+
+StreetScenes Dataset. This dataset contains 19 urban street scenes without ground-truth relighting. Participants were shown a reference image along with two relit videos generated by different ablated versions of our method.
+
+The instructions presented to participants were as follows:
+
+In this study, you will be shown a Reference Image and two videos - Video A and Video B - that changes the lighting of the scene. Your task is to watch both
+
+![](images/22e14b5ee2ca2f2d480a7832ced8a3c4e4a233f903be0e23a5f1db2b81c9b951.jpg)  
+Figure 9: Qualitative comparison with IC-Light [73]. We provide the environmental background used for IC-Light conditioning, with the reference environment ball on the left. Our method produces higher-quality and more accurate relighting results.
+
+videos and choose which one (A or B) you think has more realistic shadows and reflections. To make an informed decision, you may zoom in to examine the details. Pay close attention to aspects such as lighting, reflections, and shadows, as these can affect how natural the image appears.
+
+Once you have compared the videos, select the one that has more realistic lighting effects.
+
+Video A  
+Video B
+
+We evaluated 19 scenes, comparing a base version against two ablated versions of our method. The study was repeated three times with 11 unique participants in each run. In total, this resulted in  $19 \times 2 \times 11 \times 3 = 1254$  individual comparisons.
+
+# B.3 Synthetic data visualization
+
+We show a synthetic data visualization in Figure 8. Each scene contains albedo videos, two environment maps, and pairwise videos rendered under the environment maps.
+
+# C Additional Results
+
+Runtime cost. We evaluate the inference runtime of our model on a 57-frame video at a resolution of  $480 \times 848$ . The overall inference time for performing 35 denoising steps, including VAE encoding and decoding, is 445.5 seconds, measured on a single A100 GPU.
+
+To contextualize this cost, we compare our method against two baselines: DiffusionRenderer [39] and its Cosmos-based variant. All methods use 35 denoising steps for consistency. DiffusionRenderer is run at  $448 \times 832$  resolution (slightly smaller than ours but divisible by 32 to fit its architecture) while the Cosmos variant is run at our native resolution.
+
+For baseline methods, the total runtime is the sum of the inverse rendering and forward rendering durations. Notably, DiffusionRenderer requires five separate inverse rendering passes and one forward rendering pass per video, resulting in significantly higher computational cost. In contrast, our approach performs joint relighting and albedo estimation in a single pass and is correspondingly faster. Full timing results are shown in Table 6.
+
+Comparison with IC-Light [73]. We compare our method with the single-image relighting approach IC-Light [73] on object-centric synthetic data, as shown in Table 5 and Figure 9. Note that the
+
+![](images/5ef610476996498659046fe9406297a39e4bcc26a10738e80c34a6fa9eade30c.jpg)  
+Figure 10: Additional qualitative comparison on MIT multi-illumination dataset. Our method consistently achieves more accurate relighting results than all baselines on the MIT multi-illumination dataset, demonstrating strong capability in relighting complex materials.
+
+two methods follow different relighting formulations: IC-Light is designed for object relighting using background context as the primary cue—without access to an explicit environment map, while our method is conditioned on full HDR illumination, but is not specifically tuned for object-centric data.
+
+Our method shows improved quantitative and qualitative performance. Since IC-Light relies on background appearance as its primary cue and has less information about the surrounding lighting, it may retain input-specific effects in its outputs—such as specular highlights and shadows, which can limit accuracy under novel lighting conditions. In contrast, our method produces more faithful relighting results, with sharper specular highlights, more realistic shadows, and improved visual fidelity.
+
+Additional qualitative comparison on the MIT multi-illumination dataset. We provide additional qualitative comparisons on the MIT Multi-Illumination dataset in Figure 10. To ensure a fair comparison, we include results from our re-implemented version of DiffusionRenderer using the Cosmos backbone, which achieves higher visual fidelity than the original implementation. Our method
+
+![](images/094d1f27dc9d7884a16c5531a435f6ef1d7fe8780f4a39d021588d80ea7290f8.jpg)  
+Input
+
+![](images/98a0ef12b94f44d7909272da13f4c265a7743ede491361bec84e98885cec6ce7.jpg)  
+Relighting I
+
+![](images/38e9c4a189161c7890d6397565762c7979f7126258d918243c9d7a8871f9de56.jpg)  
+Relighting II
+
+![](images/c82780ac0a7ac2357295c5cdbdb3372132ceec2043856b79a613e8c2766b670d.jpg)  
+Relighting III
+
+![](images/6b14c2e21fe6192909ea033619165b1d9c63a153e986276f0ef845a95d9f33f0.jpg)  
+Figure 11: Additional qualitative results under point-light illumination. The bottom right of each column indicates the target lighting conditions. Our results show strong robustness of our method under point-light illumination.
+
+![](images/fbe1745a8515694f5d92b706aed1f5fa1219a142552e6dabcb9553f5766aab67.jpg)
+
+![](images/38c0fee9e46db2735997b5a061509d6cb6811a852b14cf644942b12283e2d167.jpg)
+
+![](images/10f6d7ab2c1b0b6a9de30f7e651e7b3330b679189f8cd5e6f23f16c83ae67083.jpg)
+
+![](images/220e2ba589f6e7edd8d6ad8ad5de1ca9b319103b113d542d5a16f8b80e7123ee.jpg)  
+Input
+
+![](images/ca53d2bf3780936001aa4c1b6969dd2e0ad730dc33a604e3914312f8a29e21a9.jpg)  
+Estimated Albedo
+
+![](images/1e4e1d8483c7ba1fe25f48bc16f1e09c4a940a504c2f712aff934c2042984a33.jpg)  
+Relighting
+
+![](images/73bbc93b4ccada7addf8ff1bc586d5e4ca42b11a491c0d066a41e5697456c9df.jpg)  
+Env.
+
+![](images/a531ee4980ceb85bb47c2f3fb58c6cf0c50606c1cb9a3bd5ddf6c9595b8967c4.jpg)
+
+![](images/682b4fc1085511af53bf7db102175b59cb43aa3c1ee01822897fb8cd22a27059.jpg)
+
+![](images/749b66c6e1a7cf8261dd81ef0e8bde1a9bf4910a236fad5dc0ffb5b6dce70dec.jpg)
+
+![](images/9e64057bd8d0ef3bc3f19a8e95363663a405b76dcdb52217370b0af495472552.jpg)
+
+![](images/240b732a2ad8a1452f7dbd2291253f7a81d679cd1cdeef82398f76af1f773fb0.jpg)
+
+![](images/b59b26713c7f40161596a8cbdfdd8a1e68f2a665f94d5c8885e5978e17a919fb.jpg)
+
+![](images/e53ea253220df3469d1f0919fb27378f57f2e00a80fbe463edf4bbfa4ccdff0b.jpg)
+
+![](images/ebea8c79ce652e666731f09c8604a2d50e123d45dd19f21f62d54e992e88dbac.jpg)
+
+![](images/53ad06874b2aa6c8e9fb69316c20aa86fe2032ce09b429dc9452a5f5968aac1b.jpg)
+
+![](images/04889c02731a642e4c4fe02cc36c3d812b81db96d89fffd0cf0f9d1bfc6f924e.jpg)
+
+![](images/af4c4bfd8a7954b314823ebf8af4114cf990a8935ffb7fc7cd8d676dc1a7a0fd.jpg)
+
+![](images/5244bca55478b1ba41bfea80776cf7d00198e0a4843880ca4bc406b4659c2427.jpg)
+
+![](images/87b1f57a00913401fb5f66381ffeaec93cf15e02e62134faed864346f66ff455.jpg)  
+Figure 12: Additional qualitative results on real scenes. Our method provides high-quality albedo estimation and realistic relighting results.
+
+![](images/59c0564e06a3eeab8ad0d5b92b4e5b350dedb4a98ead898a058c82f4f97c9757.jpg)
+
+![](images/cf280cd73fddecc54bc4868ec512092ba56e0ddde0ddf2b7688355f9417f0d3c.jpg)
+
+![](images/fc9e8d5c3d3554bbb188f1259214ed27705f5b5332725f07f511db33086fb160.jpg)
+
+consistently produces more accurate transparency, specular highlights, and shadows across scenes, demonstrating strong capability in handling complex materials and outperforming all baselines in visual quality.
+
+Additional qualitative results under point-light illumination. We further evaluate the robustness of our method in extreme cases, such as point-light illuminations, which do not exist in our training data. As shown in Figure 11, our method produces high-quality relighting results, demonstrating the strong robustness and generalization capability of our method.
+
+Additional qualitative results on real scenes. We present additional results on real scenes in Figure 12. Our method produces high-quality albedo and relighting results with realistic specular highlights and shadows under target lighting conditions.

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NeurIPS/2025/UniTok_ a Unified Tokenizer for Visual Generation and Understanding/full.md

@@ -0,0 +1,594 @@
+# UniTok: A Unified Tokenizer for Visual Generation and Understanding
+
+Chuofan Ma $^{1,2}$  Yi Jiang $^{2\dagger}$  Junfeng Wu $^{2,3}$  Jihan Yang $^{1}$   
+Xin Yu $^{1}$  Zehuan Yuan $^{2*}$  Bingyue Peng $^{2}$  Xiaojuan Qi $^{1\dagger*}$
+
+<sup>1</sup>The University of Hong Kong <sup>2</sup>ByteDance Inc.
+
+$^{3}$ Huazhong University of Science and Technology
+
+# Abstract
+
+Visual generative and understanding models typically rely on distinct tokenizers to process images, presenting a key challenge for unifying them within a single framework. Recent studies attempt to address this by connecting the training of VQVAE (for autoregressive generation) and CLIP (for understanding) to build a unified tokenizer. However, directly combining these training objectives has been observed to cause severe loss conflicts. In this paper, we show that reconstruction and semantic supervision do not inherently conflict. Instead, the underlying bottleneck stems from limited representational capacity of discrete token space. Building on these insights, we introduce UniTok, a unified tokenizer featuring a novel multi-codebook quantization mechanism that effectively scales up the vocabulary size and bottleneck dimension. In terms of final performance, UniTok sets a new record of 0.38 rFID and  $78.6\%$  zero-shot accuracy on ImageNet. Besides, UniTok can be seamlessly integrated into MLLMs to unlock native visual generation capability, without compromising the understanding performance. Additionally, we show that UniTok favors CFG-free generation, reducing gFID from 14.6 to 2.5 on ImageNet  $256 \times 256$  benchmark. GitHub: https://github.com/FoundationVision/UniTok.
+
+![](images/24d3288e99c12cc8d9b4b983ea4ccdf5415dd803d97462bfb369723bc48afe47.jpg)  
+Figure 1: The major challenge in unified tokenizer training. CLIP supervision cannot be easily incorporated into VQVAE training – This provides only marginal improvements in understanding performance, while drastically degrading reconstruction FID.
+
+![](images/e2e32779d8eda7fc9c15646b84602c23d59a4d20c99b46ad5155e54f524a924b.jpg)
+
+![](images/f357035c3ab7ca29285d01e54d5090b031ebef4e9ab9937423f9f04a566ee8c3.jpg)
+
+![](images/93709fddb0d411ee90ed97270d77c5226ce45906cd17ac8bfa9e70f79556b345.jpg)
+
+# 1 Introduction
+
+The advent of GPT-4o [33] highlights the immense potential of Multimodal Large Language Models (MLLMs) with native visual generation capabilities [12, 46, 62, 73, 58]. These unified models offer precise control in multimodal interactions, enabling exceptional fluency in tasks such as multi-turn image editing and visual in-context learning. However, a fundamental dilemma remains in the choice of visual tokenizers for unified MLLMs – e.g., the CLIP [38, 71] tokenizer excels in multimodal understanding but complicates generative modeling due to its high-dimensional, continuous feature
+
+space; Conversely, the discrete VQVAE [8] tokenizer fits autoregressive generation but struggles to capture essential semantics for understanding [62].
+
+In this work, we aim to design a unified visual tokenizer to bridge the gap in multimodal generation and understanding. Intuitively, this can be achieved by integrating CLIP supervision into VQVAE training, resulting in a discrete tokenizer capturing both fine-grained details and high-level semantics. However, we empirically find this training recipe confronts severe convergence issues [61] and largely falls behind the CLIP baseline in multimodal understanding (Figure 1). While prior studies commonly attribute these challenges to conflicts between semantic and pixel-level feature learning [61, 37, 58], recent progress in visual generation suggests the opposite, showing semantic regularization could benefit tokenizers in reconstruction-oriented training [63, 4, 19]. Such disparity motivates us question: Do reconstruction and semantic losses truly conflict in tokenizer training?
+
+To study the problem, we conduct a comprehensive ablation on the unified tokenizer training paradigm (Figure 3), which yields several intriguing findings: First, we show that removing reconstruction supervision, which leads to a vector-quantized CLIP model, does not improve understanding performance compared to the unified tokenizer. This observation indicates that the performance gap between unified and CLIP tokenizers mainly arises from vector quantization, rather than conflicts between learning objectives; Further analysis reveals that this gap is driven by two key factors: token factorization, which projects tokens into a lower-dimensional space for code index lookup [65], and discretization. These operations are essential for vector quantization but inevitably compromise the expressiveness of visual tokens. We thus argue that the primary bottleneck of unified tokenizers lies in the limited representational capacity of discrete token space.
+
+In light of the issue, we consider expanding the vocabulary size and latent code dimension, which allows for a closer approximation of the continuous feature space. However, extensive studies have shown that doing so could result in low codebook utilization [74, 65] and diminishing performance gains [67]. To address this, we introduce multi-codebook quantization to partition the visual token into several chunks, each discretized using a small, separate sub-codebook, akin to the multi-head attention mechanism [54]. This design exponentially scales the vocabulary size with the number of sub-codebooks, while avoiding the optimization problems of large monolithic codebooks. Besides, we replace traditional linear projection layers with adapted attention modules for token factorization, which is observed to consistently improve training stability and understanding performance.
+
+Building upon these techniques, we train a unified tokenizer called UniTok to bridge visual generation and understanding. Through extensive experiments, we demonstrate that UniTok achieves comparable or even better performance to domain-specific tokenizers: On ImageNet evaluation, UniTok records an impressive 0.38 reconstruction FID and  $78.6\%$  zero-shot accuracy at  $256 \times 256$  resolution; In building unified MLLMs, UniTok enables the MLLM with native visual generation capabilities while maintaining decent understanding performance. It outperforms the Liquid [59] baseline with a VQGAN tokenizer by  $5.5\%$  on VQAv2 [13],  $9.2\%$  on TextVQA [42], and 339 points on MME [64]; In addition, we demonstrate that semantic supervision leads to improved latent space structure for autoregressive generation, i.e., for class-conditional image generation on ImageNet  $256 \times 256$ , UniTok significantly reduces generation FID without classifier-free guidance from 14.6 to 2.5 under the LlamaGen [43] framework, which aligns with recent findings in diffusion modeling [19, 4, 63].
+
+# 2 Related Work
+
+Image Tokenization for Generation. In the domain of visual generation, image tokenization plays an important role in encoding raw pixels into compact latent features for generative modeling [53, 39]. Among a variety of tokenizers, the vector-quantized tokenizer [53] is favored for its discrete latent space and compatibility with autoregressive or masked generative models [48, 43, 3, 66]. The pioneering work VQVAE [53] initially introduced the concept of discretizing continuous tokens by mapping them to the nearest neighbors in a learnable codebook. Built on this, VQGAN [8] added perceptual loss [72] and discriminator loss [16] to improve the reconstruction quality. ViT-VQGAN [65] subsequently advanced the framework with the transformer architecture. In recent literature, considerable efforts have been devoted to developing better quantization methods such as residual quantization [18] and lookup-free quantization [67], which also constitute a focal point of this paper.
+
+Image Tokenization for Understanding. The unprecedented success of large language models (LLMs) [57, 1, 51, 47] has catalyzed the development of multimodal large language models (MLLMs)
+
+[28, 25, 31]. As a critical component of MLLMs, the selection of an effective vision tokenizer has been the subject of extensive study [55, 49]. A common choice of the vision tokenizer is the pretrained CLIP model [38], which undergoes alignment with language during its pretraining phase. While self-supervised learning models, such as DINOv2 [34], are shown to be advantageous at region-level tasks [30]. However, these tokenizers predominantly encode images into a continuous feature space, presenting challenges for uniformly modeling both vision and text tokens. To address this, some works have explored discretizing CLIP tokens [10] or employing VQVAE encoders [27, 62]. Yet, these methods have been observed to substantially impair understanding performance of MLLMs.
+
+Unified Vision-Language Models. The rise of MLLMs is not limited to the realm of visual understanding. Recent advancements have witnessed an increasing focus on unifying visual generation and understanding within one MLLM [7, 60, 46, 73, 62, 50, 21]. Specifically, a line of works employs continuous visual tokenizers for image encoding, and leverages pretrained diffusion models for image synthesis [7, 11, 44]. This approach inevitably increases model complexity and disconnects the visual sampling process from the MLLM. In contrast, another stream of research adopts VQVAE models to encode images into discrete tokens [46, 56, 62, 61, 59]. These tokens are subsequently modeled using the same cross-entropy loss that is applied to text tokens, facilitating a unified approach to multimodal learning. However, as reconstruction-oriented VQVAE does not naturally align with the LLM token space, these models typically suffer from degraded visual comprehension capabilities. Our research aligns with the second approach, with a particular focus on the tokenizer design that is suitable for both generation and understanding tasks.
+
+# 3 Method
+
+In this section, we introduce UniTok, a unified tokenizer well-suited for both visual generation and understanding tasks. We start with a unified training recipe that integrates reconstruction (VQVAE) and semantic (CLIP) supervisions (Section 3.1). However, we find that simply combining both training objectives leads to severe performance degradation, which can be mainly attributed to limited representational capacity of discrete tokens (Section 3.2). To this end, we propose multi-codebook quantization and attention projection to enhance the latent feature space and derive unified visual representations (Section 3.3). An overview of the framework is presented in Figure 2.
+
+![](images/e4a3740cba98d9b66dfe4c6a152f661c36e3ced96cc08deb4ac416eb0852f389.jpg)  
+Figure 2: An overview of UniTok. The tokenizer is trained to reconstruct the input image while aligning its discrete latent features with the text caption. For vector quantization, each visual token is split into multiple chunks, which then undergo code index lookup on corresponding sub-codebooks.
+
+# 3.1 Unified Supervision
+
+Visual generative and understanding models typically impose distinct demands on the visual tokenizers. For instance, generation emphasizes precise encoding of the visual signals, whereas understanding prioritizes capturing high-level semantics. To accommodate both requirements, we jointly train the tokenizer with  $(i)$  a VQVAE-based reconstruction loss to preserve low-level information, and  $(ii)$  an image-text contrastive loss that enhances high-level semantics of the features.
+
+To be specific, the VQVAE-based loss term  $\mathcal{L}_{\mathrm{recon}}$  consists of a pixel-level reconstruction loss  $\mathcal{L}_{\mathrm{R}}$ , a perceptual loss  $\mathcal{L}_{\mathrm{P}}$  based on the LPIPS metric [72], a discriminator loss  $\mathcal{L}_{\mathrm{G}}$  to enhance reconstruction
+
+fidelity [16], and a vector quantization loss  $\mathcal{L}_{\mathrm{VQ}}$  to minimize distance between the encoder output and its nearest code entry. It is denoted as:
+
+$$
+\mathcal {L} _ {\text {r e c o n}} = \mathcal {L} _ {\mathrm {R}} + \lambda_ {\mathrm {V Q}} \mathcal {L} _ {\mathrm {V Q}} + \lambda_ {\mathrm {P}} \mathcal {L} _ {\mathrm {P}} + \lambda_ {\mathrm {G}} \mathcal {L} _ {\mathrm {G}}, \tag {1}
+$$
+
+where  $\lambda$  is the weight factor for the corresponding loss term. The image-text contrastive loss term  $\mathcal{L}_{\mathrm{contra}}$  is basically the same as in CLIP [38]. Therefore, the final loss term can be written as:
+
+$$
+\mathcal {L} = \mathcal {L} _ {\text {r e c o n}} + \lambda_ {\text {c o n t r a}} \mathcal {L} _ {\text {c o n t r a}}. \tag {2}
+$$
+
+We simply choose  $\lambda_{\mathrm{contra}} = 1$  in this paper.
+
+# 3.2 Quantization Bottleneck
+
+Despite being augmented with CLIP supervision, we find that the unified tokenizer exhibits unsatisfactory performance in visual understanding tasks, significantly lagging behind the commonly used CLIP tokenizer. To figure out the underlying cause of this underperformance, we break down the key components involved in training a unified tokenizer, as illustrated in Figure 3. Starting with the CLIP baseline, we provide a step-by-step walk-through of all changes in following paragraphs.
+
+![](images/cc182200a28d3a44eac10b00a8abb6ca35b9a889c5a6cd3e5e5066fd82fdfcc8.jpg)  
+Figure 3: Roadmap from CLIP to UniTok. It is observed that major degradation in understanding performance comes from token factorization and discretization, rather than reconstruction supervision. The proposed multi-codebook quantization and attention projection effectively address this by scaling up the vocabulary size and bottleneck dimension. The VQA score is measured using the average score across the VQAv2, GQA, TextVQA, and POPE benchmarks. All tokenizers are trained from scratch on  $512\mathrm{m}$  image-text pairs from DataComp.
+
+Factorization. Modern VQ-tokenizers typically project continuous tokens to a lower-dimensional latent space for code index lookup (e.g. from 768-d to 8-d), known as token factorization [65]. This increases the relative density of codes by compressing the latent code space, thereby reducing quantization error. To evaluate the impact of factorization in CLIP training, we add two linear projection layers on top of the CLIP vision encoder (right before average pooling), which transforms tokens from 768-d to 16-d and then back to 768-d. Notably, vector quantization and reconstruction supervision are not included at this stage. Surprisingly, it turns out that this channel compression operation significantly compromises the expressiveness of tokens, leading to severe performance degradation in downstream VQA tasks.
+
+Discretization. Based on the implementation described above, we further introduce vector quantization to CLIP training, which maps factorized tokens to their nearest code entries. Compared to language tokenizers with vocabularies exceeding 200k entries, the vocabulary size of modern VQ-tokenizers is markedly smaller (i.e., typically ranging from 4k to 16k). Mapping continuous tokens to such a small codebook results in considerable information loss. This is validated in our experiment, which demonstrates that discretizing the factorized tokens with a 16k codebook causes an average accuracy drop of 2.1 in VQA tasks.
+
+Reconstruction Supervision. Finally, we integrate reconstruction losses into the training process to build a unified tokenizer, as outlined in Section 3.1. Previous literature suggests that loss conflict between VQVAE and CLIP is a major cause of performance degradation in joint training [61]. We observe a similar phenomenon where joint training results in sub-optimal ImageNet zero-shot classification accuracy and reconstruction FID compared to specialized training. However, surprisingly, we find that this degradation has negligible impacts on downstream understanding performance.
+
+Moreover, the degradation in classification accuracy and reconstruction FID diminishes after we improve the quantization methods (detailed in the next section). Based on these observations, we speculate that the perceived loss conflict is only a superficial issue, and the primary cause of the underperformance lies in the limited representational capacity of discrete tokens.
+
+# 3.3 UniTok
+
+A straightforward solution to breaking the quantization bottleneck could be increasing the codebook size and the latent code dimension. However, current studies on VQVAE tokenizers suggest that there is diminishing gain in scaling and the performance saturates after the codebook size reaches 16k [67, 43]. Continuing expansion results in a substantial portion of codes being rarely used or becoming 'dead' during training, which negatively impacts downstream task performance [65]. To address this, we propose multi-codebook quantization and attention projection in the following paragraphs.
+
+Multi-codebook quantization (MCQ) discretizes the latent tokens with a set of independent codebooks. Specifically, the latent vector  $f \in \mathbb{R}^d$  is first evenly split into  $n$  chunks  $\{f_1, f_2, \dots, f_n\}$ , where  $f_i \in \mathbb{R}^{\frac{d}{n}}$ . The subsequent quantization process is denoted as:
+
+$$
+\hat {f} = \operatorname {C o n c a t} \left(\mathcal {Q} \left(Z _ {1}, f _ {1}\right), \mathcal {Q} \left(Z _ {2}, f _ {2}\right), \dots , \mathcal {Q} \left(Z _ {n}, f _ {n}\right)\right) \tag {3}
+$$
+
+where  $\hat{f}$  is the discretized latent vector,  $\mathcal{Q}$  is the code index lookup operation, and  $Z_{i}$  is  $i$ -th subcodebook. Compared to conventional quantization methods, the proposed MCQ effectively scales up the vocabulary size. For instance, by increasing the number of sub-codebooks from 1 to 4, and suppose each sub-codebook contains 16k code entries, the theoretical vocabulary size exponentially increases from  $2^{14}$  to  $2^{56}$  (i.e., there are up to  $2^{14\times 4}$  possible combinations of codes for each token). As the size of each individual codebook remains constant, it circumvents the optimization problem associated with large codebooks. Besides, the dimensionality of the latent codes also scales proportionally with the number of codebooks (i.e., increasing from 16-d to 64-d in this case), which further enhances the representational capacity of discrete representations.
+
+Discussions. MCQ shares a similar concept with residual quantization (RQ) [18] in using multiple codes to quantize a token, but differs fundamentally in design philosophy: RQ follows a coarse-to-fine quantization order, whereas MCQ adopts a divide-and-conquer strategy. This distinction gives MCQ unique advantages when operating in high-dimensional latent spaces, where codes tend to become increasingly sparse. For instance, with a latent dimension of 64-d, we observe that MCQ's quantization loss is 15 to 45 times lower than that of RQ. This is because MCQ partitions the original latent space into multiple low-dimensional subspaces for quantization. Our ablation study in Table 7 further confirms the superiority of MCQ in unified tokenizer training.
+
+Attention projection. Existing VQ methods usually employ linear or convolutional projection layers for token factorization. But as shown in Figure 3, this over-simplified design fails to preserve rich semantics when compressing the feature dimensions, leading to degraded understanding performance. To alleviate this problem, we suggest adapting the multi-head attention modules for factorization. Specifically, instead of concatenating features from multiple heads after the attention calculation, we replace the concatenation operation with average pooling to realize channel compression. Figure 6 provides a detailed illustration of the adaptation. Despite its simplicity, we find this design effectively strengthens the representational power of factorized tokens and stabilizes training.
+
+# 3.4 Unified MLLM
+
+We proceed to develop a unified multimodal model with UniTok. Particularly, we leverage the unified framework introduced in Liquid [59], which models (discrete-valued) vision and language sequences with a universal next-token prediction loss. But instead of learning the visual codebook from scratch, we reuse code embeddings of UniTok by projecting them to the MLLM token space with an MLP projector. Notably, despite UniTok encodes an image into  $H \times W \times K$  codes (where  $K$  represents the number of sub-codebooks), we simplify this for MLLM input by merging every  $K$  consecutive codes into a single visual token. Similarly, when it comes to visual token prediction, we make each token autoregressively predict the next  $K$  codes, using a depth transformer head as implemented in RQ-Transformer [18] and VILA-U [61]. This design maintains efficiency for visual generation in the context of multi-codebooks.
+
+# 4 Experiments
+
+# 4.1 Implementation Details
+
+Tokenizer Setup. Leading VQVAE tokenizers predominantly adopt the CNN architecture, while ViT is preferred in CLIP training for its scalability. To take advantage of both, we choose a hybrid architecture, ViTamin-L/16 [5], to instantiate UniTok. We configure UniTok with eight sub-codebooks, each containing 4,096 code entries and a latent dimension set to 8-d (the global latent dimension is thus 64-d). The discriminator is initialized with pretrained DINOv2-S [34]. We train the tokenizer for one epoch on the public dataset DataComp-1B [9] consisting of 1.28B image-text pairs, with all images resized to  $256 \times 256$  resolution and a global batch size of 16k. The learning rate is set to 1e-3 for the tokenizer and 2e-4 for the discriminator. Besides, we prepare two settings for evaluation: one with pretrained CLIP weight initialization and one with random initialization (the default setting).
+
+MLLM Setup. We instantiate a unified MLLM described in Section 3.4 with the Llama-2-7B base model [52]. Following Liquid, we first pretrain the model on a mix of multimodal data, which is composed of 10M language data from DCLM [22], 30M internal MidJourney-style synthetic data, and 30M re-captioned image-text pairs from COYO [32] and Laion [41]. Subsequently, we finetune the model on 1.5M text-to-image data and 1.5M multimodal instruction tuning data introduced in Mini-Gemini [23]. Specifically, the learning rate is set to 5e-5 in the pretraining stage and 2e-5 in the finetuning stage. For visual understanding evaluation, we report results on standard VQA benchmarks including VQAv2 [13], GQA [14], TextVQA [42], POPE [24], MME [64], and MM-Vet [70]. For visual generation evaluation, we report results on GenAI-Bench [26] and MJHQ-30K [20].
+
+# 4.2 Tokenizer Comparison
+
+We benchmark UniTok on ImageNet using two primary metrics: Fréchet Inception Distance (FID) to evaluate reconstruction quality, and top-1 zero-shot accuracy to assess image-text alignment. The results are presented in Table 1. To provide a fair comparison with tokenizers trained on small datasets, we also train a version of UniTok on OpenImages [17] solely with reconstruction supervision. It can be seen that UniTok excels in reconstruction quality compared to both unified and domain-specific tokenizers, recording an impressive  $0.38\mathrm{rFID}$  on ImageNet with  $16\times$  downsampling ratio. As a discrete tokenizer, UniTok even surpasses the continuous VAE tokenizer from Stable Diffusion v2.1 [40], showcasing the superiority of the proposed multi-codebook quantization. For the perception performance, we observe that randomly initialized UniTok demonstrates suboptimal zero-shot classification accuracy. This is expected as current training schedule (i.e., one epoch on 1.28B samples) is insufficient for CLIP training to fully converge. It can be seen that initializing the model with pretrained CLIP weights largely alleviates the problem, boosting the zero-shot accuracy from  $70.8\%$  to  $78.6\%$ . In complement to quantitative results, we provide examples of reconstructed images in Figure 4.
+
+Table 1: Comparison on ImageNet reconstruction FID and zero-shot classification accuracy. rFID is measured at  $256\times 256$  resolution with  $16\times$  downsample ratio.  $\dagger$  indicates model using pretrained CLIP weights for initialization. \* indicates model trained on OpenImages.
+
+<table><tr><td>Method</td><td>#Tokens</td><td>rFID ↓</td><td>Accuracy</td></tr><tr><td colspan="4">VQVAE Model</td></tr><tr><td>VQ-GAN* [8]</td><td>256</td><td>4.98</td><td>-</td></tr><tr><td>RQ-VAE [18]</td><td>256</td><td>1.30</td><td>-</td></tr><tr><td>VAR* [48]</td><td>680</td><td>0.90</td><td>-</td></tr><tr><td>UniTok*</td><td>256</td><td>0.33</td><td>-</td></tr><tr><td colspan="4">CLIP Model</td></tr><tr><td>CLIP [38]</td><td>256</td><td>-</td><td>76.2</td></tr><tr><td>SigLIP [71]</td><td>256</td><td>-</td><td>80.5</td></tr><tr><td>ViTamin [5]</td><td>256</td><td>-</td><td>81.2</td></tr><tr><td colspan="4">Unified Model</td></tr><tr><td>TokenFlow†[37]</td><td>680</td><td>1.37</td><td>-</td></tr><tr><td>VILA-U†[61]</td><td>256</td><td>1.80</td><td>73.3</td></tr><tr><td>UniTok</td><td>256</td><td>0.41</td><td>70.8</td></tr><tr><td>UniTok†</td><td>256</td><td>0.38</td><td>78.6</td></tr></table>
+
+# 4.3 Class-Conditional Image Generation
+
+Recent studies on diffusion models indicate that injecting semantics into VAE training leads to a better-structured latent space, considerably enhancing guidance-free generation performance [63, 4, 19]. To evaluate whether UniTok possesses similar properties, we test it within the LlamaGen framework for class-conditional image generation. As shown in Table 2, UniTok reduces the FID by 12.11 compared to the VQGAN baseline in CFG-free generation, under the same generator setup. This implies that UniTok learns a more structured code distribution, benefiting autoregressive modeling.
+
+![](images/73210aef10fb2b885d793ed79c8965106e0e16728a202b9fd0102279b2d71574.jpg)
+
+![](images/5323887858f10c132d21133378866105238b6f77266eb6d93c71305f42e0c479.jpg)
+
+![](images/5ccecd61b1b0444eb5f362dcbaec910c171d28b57d68939f02c703ec04064d38.jpg)  
+Original Images
+
+![](images/2c9622fd6ad934e2de058f49c3cf584e96a048c8fb753e14e2fb02a94c22792a.jpg)
+
+![](images/bcebc10c5f3469aafe8ef8fa5b077d81f4389dbacacd1c0e3c16107c6aa41118.jpg)
+
+![](images/9f8f9dff8b814187481c22c2c81b80aa4ff6ad5cf18a94aee4e5913b9accdb30.jpg)  
+Figure 4: Qualitative results on image reconstruction in a resolution of  $256 \times 256$ .
+
+![](images/e7be833009d85fd3bff50e3f18e5edc98d7cd5795a12587558328a73c714be69.jpg)  
+Reconstructed Images
+
+![](images/481f5f1c989f47709c98e94d98ead8a016e3281f54e4c07e284facaa2780fe40.jpg)
+
+![](images/3bd9335edf05aa99d97d9ebb3ed3e8eb27c1b6d3a88c8d9dd12f5cf49c56321a.jpg)
+
+![](images/6443277a0e6980cb5ff5b4d58eb0a508cb9adc1ed891a0e7d116ab318053459c.jpg)
+
+Table 2: Class-conditional image generation results on ImageNet  ${256} \times  {256}$  . †: VQGAN tokenizer from LlamaGen. ‡: Images are generated at  ${384} \times  {384}$  resolution and then resized to  ${256} \times  {256}$  for evaluation. 'Pre.': precision; 'Rec.': recall; 'CFG': classifier-free-guidance.  
+
+<table><tr><td rowspan="2">Tokenizer</td><td rowspan="2">rFID</td><td rowspan="2">Generator</td><td rowspan="2">#Params.</td><td colspan="4">Generation w/o CFG</td><td colspan="4">Generation w/ CFG</td></tr><tr><td>gFID↓</td><td>IS↑</td><td>Pre.</td><td>Rec.</td><td>gFID↓</td><td>IS↑</td><td>Pre.</td><td>Rec.</td></tr><tr><td colspan="12">Diffusion Models</td></tr><tr><td>SD-VAE [40]</td><td>0.61</td><td>DiT [35]</td><td>675M</td><td>9.62</td><td>121.5</td><td>0.67</td><td>0.67</td><td>2.27</td><td>278.2</td><td>0.83</td><td>0.57</td></tr><tr><td>VAVAE [63]</td><td>0.28</td><td>LightningDiT [63]</td><td>675M</td><td>2.17</td><td>205.6</td><td>0.77</td><td>0.65</td><td>1.35</td><td>295.3</td><td>0.79</td><td>0.65</td></tr><tr><td colspan="12">Masked Generative Models</td></tr><tr><td>LFQ [67]</td><td>0.9</td><td>MAGVIT-v2 [67]</td><td>307M</td><td>3.07</td><td>213.1</td><td>-</td><td>-</td><td>1.91</td><td>324.3</td><td>-</td><td>-</td></tr><tr><td>TiTok-L [69]</td><td>2.21</td><td>MaskGIT [3]</td><td>177M</td><td>3.15</td><td>173.0</td><td>-</td><td>-</td><td>2.77</td><td>199.8</td><td>-</td><td>-</td></tr><tr><td colspan="12">Autoregressive Models</td></tr><tr><td>VQGAN†</td><td>2.19</td><td>LlamaGen‡[43]</td><td>1.4B</td><td>14.65</td><td>86.3</td><td>0.63</td><td>0.68</td><td>2.34</td><td>253.9</td><td>0.81</td><td>0.60</td></tr><tr><td>UniTok (Ours)</td><td>0.41</td><td>LlamaGen [43]</td><td>1.4B</td><td>2.51</td><td>216.7</td><td>0.82</td><td>0.57</td><td>2.77</td><td>227.5</td><td>0.81</td><td>0.57</td></tr></table>
+
+# 4.4 Unified Understanding and Generation
+
+Understanding Performance. We evaluate the understanding performance of UniTok on diverse VQA benchmarks in Table 3. Our unified MLLM showcases clear advantages when compared to other unified models that also utilize a discrete visual tokenizer. Specifically, UniTok significantly outperforms the Chameleon model, which relies on a traditional VQVAE tokenizer, by  $7.2\%$  higher accuracy on VQAv2. Additionally, it surpasses VILA-U, another model with a unified tokenizer, by  $3.3\%$  in accuracy on the TextVQA benchmark and by a notable margin of 112 points on the MME-Perception scores. Furthermore, we can see that UniTok largely narrows the performance gap with MLLMs that incorporate continuous visual tokenizers. These strong results confirm the candidacy of UniTok as a unified visual tokenizer for multimodal models.
+
+Generation Performance. Table 4 presents the text-to-image generation performance of our unified MLLM on the GenEval benchmark. We show that UniTok not only outperforms most of the unified MLLMs, but also demonstrates competitive performance against domain experts (diffusion models) trained on billions of images. Besides, UniTok achieves non-trivial improvements over Liquid while using exactly the same set of text-to-image training data, highlighting the importance of a unified tokenizer. We also provide results on GenAI-Bench in Table 10 and Table 11 in Appendix.
+
+We further evaluate the quality of images generated by our model on the MJHQ-30K benchmark, details of which are presented in Table 5. Notably, as this benchmark primarily relies on the FID score
+
+Table 3: Comparison with unified multi-modal large language models on VQA benchmarks.  
+
+<table><tr><td>Method</td><td>LLM</td><td>Token Type</td><td>Res.</td><td>VQAv2</td><td>GQA</td><td>TextVQA</td><td>POPE</td><td>MME</td><td>MM-Vet</td></tr><tr><td>Emu [45]</td><td>Llama-13B</td><td>Continuous</td><td>224</td><td>52.0</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>LaVIT [15]</td><td>Llama-7B</td><td>Continuous</td><td>224</td><td>66.0</td><td>46.8</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>DreamLLM [7]</td><td>Vicuna-7B</td><td>Continuous</td><td>224</td><td>72.9</td><td>-</td><td>41.8</td><td>-</td><td>-</td><td>26.6</td></tr><tr><td>Unified-IO 2 [29]</td><td>6.8B from scratch</td><td>Continuous</td><td>384</td><td>79.4</td><td>-</td><td>-</td><td>87.7</td><td>-</td><td>-</td></tr><tr><td>Janus [58]</td><td>DeepSeek-1.3B</td><td>Continuous</td><td>384</td><td>77.3</td><td>59.1</td><td>-</td><td>87.0</td><td>1338</td><td>34.3</td></tr><tr><td>CM3Leon [68]</td><td>7B from scratch</td><td>Discrete</td><td>256</td><td>47.6</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>LWM [27]</td><td>Llama-2-7B</td><td>Discrete</td><td>256</td><td>55.8</td><td>44.8</td><td>18.8</td><td>75.2</td><td>-</td><td>-</td></tr><tr><td>Show-o [62]</td><td>Phi-1.5-1.3B</td><td>Discrete</td><td>256</td><td>59.3</td><td>48.7</td><td>-</td><td>73.8</td><td>948</td><td>-</td></tr><tr><td>Chameleon [46]</td><td>34B from scratch</td><td>Discrete</td><td>512</td><td>69.6</td><td>-</td><td>-</td><td>-</td><td>-</td><td></td></tr><tr><td>Liquid [59]</td><td>Gemma-7B</td><td>Discrete</td><td>512</td><td>71.3</td><td>58.4</td><td>42.4</td><td>81.1</td><td>1119</td><td>-</td></tr><tr><td>VILA-U [61]</td><td>Llama-2-7B</td><td>Discrete</td><td>256</td><td>75.3</td><td>58.3</td><td>48.3</td><td>83.9</td><td>1336</td><td>27.7</td></tr><tr><td>UniTok</td><td>Llama-2-7B</td><td>Discrete</td><td>256</td><td>76.8</td><td>61.1</td><td>51.6</td><td>83.2</td><td>1448</td><td>33.9</td></tr></table>
+
+Table 4: Comparison with other visual generation methods on the GenEval benchmark.  
+
+<table><tr><td>Method</td><td>Type</td><td>#Data</td><td>Single Obj.</td><td>Two Obj.</td><td>Counting</td><td>Colors</td><td>Position</td><td>Color Attri.</td><td>Overall↑</td></tr><tr><td>SD v2.1 [39]</td><td>Diffusion</td><td>2000M</td><td>0.98</td><td>0.51</td><td>0.44</td><td>0.85</td><td>0.07</td><td>0.17</td><td>0.50</td></tr><tr><td>SD-XL [36]</td><td>Diffusion</td><td>2000M</td><td>0.98</td><td>0.74</td><td>0.39</td><td>0.85</td><td>0.15</td><td>0.23</td><td>0.55</td></tr><tr><td>DALL-E 3 [2]</td><td>Diffusion</td><td>-</td><td>0.96</td><td>0.87</td><td>0.47</td><td>0.83</td><td>0.43</td><td>0.45</td><td>0.67</td></tr><tr><td rowspan="2">Show-o [62]</td><td rowspan="2">Discrete Diff.</td><td>36M</td><td>0.95</td><td>0.52</td><td>0.49</td><td>0.82</td><td>0.11</td><td>0.28</td><td>0.53</td></tr><tr><td>2.0B</td><td>0.98</td><td>0.80</td><td>0.66</td><td>0.84</td><td>0.31</td><td>0.50</td><td>0.68</td></tr><tr><td>LWM [27]</td><td>Autoregressive</td><td>-</td><td>0.93</td><td>0.41</td><td>0.46</td><td>0.79</td><td>0.09</td><td>0.15</td><td>0.47</td></tr><tr><td>Janus [58]</td><td>Autoregressive</td><td>-</td><td>0.97</td><td>0.68</td><td>0.30</td><td>0.84</td><td>0.46</td><td>0.42</td><td>0.61</td></tr><tr><td>Liquid [59]</td><td>Autoregressive</td><td>30M</td><td>0.98</td><td>0.73</td><td>0.32</td><td>0.76</td><td>0.17</td><td>0.37</td><td>0.55</td></tr><tr><td>UniTok</td><td>Autoregressive</td><td>30M</td><td>0.99</td><td>0.71</td><td>0.36</td><td>0.79</td><td>0.26</td><td>0.45</td><td>0.59</td></tr></table>
+
+for evaluation, high-resolution images are preferred because they potentially capture more fine-grained details. Despite this makes FID across different resolutions less comparable, we show that our model achieves impressive performance even at the smallest resolution, showcasing its ability to generate high-quality, detail-rich images.
+
+We present some examples of the images generated by our model in Figure 5, using text prompts sampled from MJHQ-30K. The visualization results demonstrate our model is capable of synthesizing photorealistic and visually appealing images. Moreover, the model is able to comprehend a wide spectrum
+
+of concepts, such as 'Vincent van Gogh painting style' and 'bitcoin', and flexibly combine these concepts to synthesize creative images.
+
+Table 5: Results on MJHQ-30K.  
+
+<table><tr><td>Method</td><td>Type</td><td>Res.</td><td>FID↓</td></tr><tr><td>SD-XL [36]</td><td>Diffusion</td><td>1024</td><td>9.55</td></tr><tr><td>PixArt [6]</td><td>Diffusion</td><td>1024</td><td>6.14</td></tr><tr><td>Playground [20]</td><td>Diffusion</td><td>1024</td><td>4.48</td></tr><tr><td>Liquid [59]</td><td>Autoregressive</td><td>512</td><td>5.47</td></tr><tr><td>Janus [58]</td><td>Autoregressive</td><td>384</td><td>10.10</td></tr><tr><td>LWM [27]</td><td>Autoregressive</td><td>256</td><td>17.77</td></tr><tr><td>Show-o [62]</td><td>Discrete Diff.</td><td>256</td><td>15.18</td></tr><tr><td>VILA-U [61]</td><td>Autoregressive</td><td>256</td><td>12.81</td></tr><tr><td>UniTok</td><td>Autoregressive</td><td>256</td><td>7.46</td></tr></table>
+
+# 4.5 Ablation Studies
+
+Impact of Supervision Types. To ablate the impact of contrastive and reconstruction losses in UniTok training, we conduct experiments on tokenizers trained with different supervision types, as shown in Table 6. It is worth noting that all the tokenizers are vector-quantized even though some do not have reconstruction supervision. First, we show that reconstruction-oriented tokenizer significantly lags behind tokenizers with contrastive supervision in visual understanding performance. This observation evidences the limitations of traditional VQVAE. Second, we demonstrate that reconstruction and contrastive training objectives do not inherently conflict, or can be addressed by enhancing discrete feature space. With multi-codebook quantization, the jointly trained tokenizer not only exhibits understanding performance on par with the tokenizer trained solely with contrastive loss, but also slightly improves generation performance over the reconstruction-oriented tokenizer.
+
+MCQ v.s. RQ. Following discussions in Section 3.3, we provide an apple-to/apple comparison between multi-codebook quantization and residual quantization in Table 7. For fair comparisons, we
+
+![](images/9b74248716873f198241c048072f2a0cd3390a148e305cde73b90e1a78220dca.jpg)  
+Figure 5: Images generated in a resolution of  $256 \times 256$  with our unified MLLM.
+
+Table 6: Impact of different supervision types on downstream generation and understanding performance. The rFID and gFID are measured on the ImageNet  $(256\times 256)$  validation set. LlamaGen-L [43] is adopted as the generator for gFID evaluation.  
+
+<table><tr><td rowspan="2">Supervision</td><td colspan="2">Generation</td><td colspan="6">Understanding</td></tr><tr><td>rFID ↓</td><td>gFID ↓</td><td>VQAv2</td><td>GQA</td><td>SciQA</td><td>TextVQA</td><td>POPE</td><td>MME</td></tr><tr><td>Contrastive</td><td>-</td><td>-</td><td>68.95</td><td>56.89</td><td>65.64</td><td>49.89</td><td>82.34</td><td>1373</td></tr><tr><td>Reconstruction</td><td>0.82</td><td>3.59</td><td>56.33</td><td>47.53</td><td>63.26</td><td>43.65</td><td>77.09</td><td>902</td></tr><tr><td>Recon. + Contra.</td><td>0.72</td><td>3.26</td><td>69.14</td><td>56.06</td><td>65.25</td><td>49.22</td><td>81.42</td><td>1333</td></tr></table>
+
+directly implement RQ on our codebase, keeping all the training settings the same as UniTok. Both tokenizers are trained on a 512M subset of DataComp-1B. Notably, unlike MCQ, RQ by default uses a shared large codebook. To keep the global codebook size the same as UniTok, we set the codebook size of RQ to 32768. It can be seen that RQ demonstrates inferior reconstruction performance and lower classification accuracy compared to MCQ under the high bottleneck dimension (64-d) setting.
+
+Number of Sub-Codebooks. To gain deeper insights into multi-codebook quantization, we evaluate how tokenizer performance changes with the number of sub-codebooks in Table 8. Specifically, the size of a codebook is denoted as  $A \times B$ , where  $A$  is the number of sub-codebook and  $B$  is the size of sub-codebook. For rFID evaluation, we train the tokenizer solely with reconstruction loss on OpenImages [17], and evaluated it on ImageNet (256 × 256) validation set. While for ImageNet zero-shot accuracy evaluation, the tokenizer is trained on DataComp-1B 128m subset using only contrastive loss. Given a constant global codebook size, we see that increasing the number of subcodebooks consistently improves reconstruction FID and classification accuracy. This indicates that MCQ generally benefits vector-quantized models, independent of the training objectives.
+
+Table 7: MCQ v.s. RQ.  
+
+<table><tr><td>Method</td><td>Code Shape</td><td>Code Dim.</td><td>rFID↓</td><td>Accuracy</td></tr><tr><td>RQ</td><td>16×16×8</td><td>64</td><td>3.46</td><td>58.8</td></tr><tr><td>MCQ</td><td>16×16×8</td><td>64</td><td>0.55</td><td>63.7</td></tr><tr><td colspan="5">Codebook / Vocabulary</td></tr><tr><td colspan="5">1×16384 / 214</td></tr><tr><td colspan="5">2×8192 / 226</td></tr><tr><td colspan="5">4×4096 / 248</td></tr><tr><td colspan="5">8×2048 / 288</td></tr><tr><td colspan="5">Accuracy</td></tr><tr><td colspan="5">41.0%</td></tr><tr><td colspan="5">43.9%</td></tr><tr><td colspan="5">44.7%</td></tr><tr><td colspan="5">46.1%</td></tr></table>
+
+Table 8: Ablation on number of sub-codebooks.
+
+CLIP Weight Initialization. We notice that higher ImageNet accuracy does not guarantee superior downstream performance. In Table 9, we ablate the impact of CLIP weight initialization on visual understanding performance. Specifically, we adopt the classic LLaVA framework for evaluation,
+
+replacing the original CLIP tokenizer with UniTok while keeping all other the training settings unchanged. One tokenizer is initialized with the pretrained ViTamin-L-256 [5] weights, while the other is randomly initialized. To our surprise, UniTok that is trained from scratch surpasses the one initialized with pretrained CLIP weights, despite the latter actually achieves better zero-shot classification accuracy. This suggests downstream VQA performance may not be highly correlated with ImageNet classification accuracy. More importantly, it also implies that CLIP weight initialization may serve as a negative prior for unified tokenizers, as the unified visual feature space could drastically differ from CLIP feature space.
+
+Table 9: Comparison of different initialization methods under the LLaVA framework.  $\dagger$  indicates the model uses CLIP weights for initialization. We highlight the default setting of UniTok in gray.  
+
+<table><tr><td>Tokenizer</td><td>VQAv2</td><td>GQA</td><td>TextVQA</td><td>POPE</td><td>MME</td></tr><tr><td>UniTok†</td><td>69.9</td><td>56.2</td><td>49.3</td><td>81.2</td><td>1331</td></tr><tr><td>UniTok</td><td>72.4</td><td>58.2</td><td>51.6</td><td>82.4</td><td>1392</td></tr></table>
+
+# 5 Limitations and Conclusion
+
+This paper studies unified visual tokenization for generation and understanding, which serves as the cornerstone of unified multimodal large language models. We investigate the training paradigm of unified tokenizers and identify that the current challenge in unification mainly arises from the limited representational power of discrete tokens. To address this limitation, we introduce multi-codebook quantization and attention projection to build a unified tokenizer called UniTok. We show that UniTok excels in downstream visual generation and understanding tasks. The ablation study further reveals that discriminative and generative representation learning does not inherently conflict. We hope our findings could inspire future research in this domain.
+
+However, due to limited computational resources, UniTok is only trained for one epoch, which is not sufficient for CLIP-based semantic representation learning. We believe extending the training schedule could further benefit the tokenizer, especially in understanding performance.
+
+Acknowledgments This work has been supported by the National Key R&D Program of China (Grant No. 2022YFB3608300), Hong Kong Research Grant Council - Early Career Scheme (Grant No. 27209621), General Research Fund Scheme (Grant No. 17202422, 17212923, 17215025) Themebased Research (Grant No. T45-701/22-R) and Shenzhen Science and Technology Innovation Commission (SGDX20220530111405040). We sincerely thank Seng Ye for the insightful discussions and valuable contributions to this project.
+
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+
+# NeurIPS Paper Checklist
+
+# 1. Claims
+
+Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
+
+Answer: [Yes].
+
+Justification: We have included the motivation, empirical findings, technical innovations, and major achievements of our work in the abstract and introduction.
+
+Guidelines:
+
+- The answer NA means that the abstract and introduction do not include the claims made in the paper.  
+- The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.  
+- The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.  
+- It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.
+
+# 2. Limitations
+
+Question: Does the paper discuss the limitations of the work performed by the authors?
+
+Answer: [Yes]
+
+Justification: Please see 'Limitations and Conclusion' section.
+
+Guidelines:
+
+- The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.  
+- The authors are encouraged to create a separate "Limitations" section in their paper.  
+- The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.  
+- The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.  
+- The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.  
+- The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.  
+- If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.  
+- While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
+
+# 3. Theory assumptions and proofs
+
+Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?
+
+Answer: [NA]
+
+Justification: This paper does not include theoretical results.
+
+# Guidelines:
+
+- The answer NA means that the paper does not include theoretical results.  
+- All the theorems, formulas, and proofs in the paper should be numbered and cross-referenced.  
+- All assumptions should be clearly stated or referenced in the statement of any theorems.  
+- The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.  
+- Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.  
+- Theorems and Lemmas that the proof relies upon should be properly referenced.
+
+# 4. Experimental result reproducibility
+
+Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
+
+# Answer: [Yes]
+
+Justification: We have included the implementation details of our tokenizer and MLLM (such as training data, hyperparameter configurations, testing framework, etc) in the 'Experiments' part.
+
+# Guidelines:
+
+- The answer NA means that the paper does not include experiments.  
+- If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.  
+- If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.  
+- Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general, releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.  
+- While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example  
+(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.  
+(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.  
+(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).  
+(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
+
+# 5. Open access to data and code
+
+Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?
+
+Answer: [Yes]
+
+Justification: Most code and data for this paper are open-source, except the unified MLLM training data, which includes some internal, non-public data. However, our main focus - tokenizer training - uses only publicly available data.
+
+Guidelines:
+
+- The answer NA means that paper does not include experiments requiring code.  
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+- The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.  
+- The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.  
+- At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).  
+- Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.
+
+# 6. Experimental setting/details
+
+Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
+
+Answer: [Yes]
+
+Justification: Please see our code for the full set of hyperparameter configurations.
+
+Guidelines:
+
+- The answer NA means that the paper does not include experiments.  
+- The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.  
+- The full details can be provided either with the code, in appendix, or as supplemental material.
+
+# 7. Experiment statistical significance
+
+Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
+
+Answer: [No]
+
+Justification: We mainly evaluate our methods using ImageNet FID and zero-shot accuracy, metrics known for low variance. Following prior work, we omit error bars in our experiments.
+
+Guidelines:
+
+- The answer NA means that the paper does not include experiments.  
+- The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.
+
+- The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).  
+- The method for calculating the error bars should be explained (closed form formula call to a library function, bootstrap, etc.)  
+- The assumptions made should be given (e.g., Normally distributed errors).  
+- It should be clear whether the error bar is the standard deviation or the standard error of the mean.  
+- It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a  $96\%$  CI, if the hypothesis of Normality of errors is not verified.  
+- For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).  
+- If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
+
+# 8. Experiments compute resources
+
+Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
+
+Answer: [Yes]
+
+Justification: It takes roughly 50 hours with the equivalent computing power of 256 A100 GPUs to train the tokenizer.
+
+Guidelines:
+
+- The answer NA means that the paper does not include experiments.  
+- The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.  
+- The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.  
+- The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).
+
+# 9. Code of ethics
+
+Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines?
+
+Answer: [Yes]
+
+Justification: I have read and confirmed that this paper complies with the NeurIPS Code of Ethics.
+
+Guidelines:
+
+The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.  
+- If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.  
+- The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).
+
+# 10. Broader impacts
+
+Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
+
+Answer: [Yes]
+
+Justification: Please see Appendix.
+
+Guidelines:
+
+- The answer NA means that there is no societal impact of the work performed.
+
+- If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.  
+- Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.  
+- The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.  
+- The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.  
+- If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).
+
+# 11. Safeguards
+
+Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
+
+Answer: [No]
+
+Justification: We will add safeguards in our future release.
+
+Guidelines:
+
+- The answer NA means that the paper poses no such risks.  
+- Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.  
+- Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.  
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+
+# 12. Licenses for existing assets
+
+Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
+
+Answer: [Yes]
+
+Justification: We strictly followed the licenses to use other assets (like DataComp) with credits.
+
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+
+- The answer NA means that the paper does not use existing assets.  
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+
+# 13. New assets
+
+Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
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+Answer: [NA]
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+Guidelines:
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+
+# 14. Crowdsourcing and research with human subjects
+
+Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
+
+Answer: [NA]
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+
+# 15. Institutional review board (IRB) approvals or equivalent for research with human subjects
+
+Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
+
+Answer: [NA]
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+Justification: This paper does not involve crowdsourcing nor research with human subjects.
+
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+- Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
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+
+# 16. Declaration of LLM usage
+
+Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
+
+Answer: [NA]
+
+Justification: The core method development in this research does not involve LLMs as any important, original, or non-standard components.
+
+Guidelines:
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+- Please refer to our LLM policy (https://neurips.cc/Conferences/2025/LLM) for what should or should not be described.
+
+# A Attention Projection Modules
+
+Figure 6 illustrates the adaptations we made to the tradition MHA module, which enables channel compression and expansion in token factorization.
+
+![](images/b328deb3bbde08dc3d61670755469c8ab73cb3e926e54a645e099e9b951f739f.jpg)  
+Channel Compression Block
+
+![](images/19e71bb401230a41d2981c126a0ee85491ba0778e220f5f22159dfce56886cd5.jpg)  
+Channel Expansion Block  
+Figure 6: Modified attention blocks for factorization. Modules in yellow indicate a change in the number of channels.  $C$  and  $c$  stand for the channel dimension,  $h$  is the number of heads in the multi-head attention module.  $C = h \times c$ .
+
+# B More Generation Results
+
+We provide the results on GenAI-Bench in Table 10 and Table 11. UniTok consistently delivers superior generation performance on this benchmark.
+
+Table 10: Comparison with other visual generation methods on GenAI-Bench (basic prompts).  
+
+<table><tr><td rowspan="2">Method</td><td rowspan="2">Type</td><td rowspan="2">#Training Images</td><td rowspan="2">Attribute↑</td><td rowspan="2">Scene↑</td><td colspan="3">Relation↑</td><td rowspan="2">Overall↑</td></tr><tr><td>Spatial</td><td>Action</td><td>Part</td></tr><tr><td>SD v2.1 [39]</td><td>Diffusion</td><td>2000M</td><td>0.80</td><td>0.79</td><td>0.76</td><td>0.77</td><td>0.80</td><td>0.78</td></tr><tr><td>SD-XL [36]</td><td>Diffusion</td><td>2000M</td><td>0.84</td><td>0.84</td><td>0.82</td><td>0.83</td><td>0.89</td><td>0.83</td></tr><tr><td>Midjourney v6</td><td>Diffusion</td><td>-</td><td>0.88</td><td>0.87</td><td>0.87</td><td>0.87</td><td>0.91</td><td>0.87</td></tr><tr><td>DALL-E 3 [2]</td><td>Diffusion</td><td>-</td><td>0.91</td><td>0.90</td><td>0.92</td><td>0.89</td><td>0.91</td><td>0.90</td></tr><tr><td>Show-o [62]</td><td>Discrete Diff.</td><td>36M</td><td>0.72</td><td>0.72</td><td>0.70</td><td>0.70</td><td>0.75</td><td>0.70</td></tr><tr><td>LWM [27]</td><td>Autoregressive</td><td>-</td><td>0.63</td><td>0.62</td><td>0.65</td><td>0.63</td><td>0.70</td><td>0.63</td></tr><tr><td>VILA-U [61]</td><td>Autoregressive</td><td>15M</td><td>0.78</td><td>0.78</td><td>0.77</td><td>0.78</td><td>0.79</td><td>0.76</td></tr><tr><td>Liquid [59]</td><td>Autoregressive</td><td>30M</td><td>0.84</td><td>0.86</td><td>0.81</td><td>0.83</td><td>0.91</td><td>0.83</td></tr><tr><td>UniTok</td><td>Autoregressive</td><td>30M</td><td>0.85</td><td>0.87</td><td>0.86</td><td>0.86</td><td>0.89</td><td>0.85</td></tr></table>
+
+Table 11: Comparison with other visual generation methods on GenAI-Bench (advanced prompts).  
+
+<table><tr><td rowspan="2">Method</td><td rowspan="2">Type</td><td rowspan="2">#Training Images</td><td rowspan="2">Count↑</td><td rowspan="2">Differ↑</td><td rowspan="2">Compare↑</td><td colspan="2">Logical↑</td><td rowspan="2">Overall↑</td></tr><tr><td>Negate</td><td>Universal</td></tr><tr><td>SD v2.1 [39]</td><td>Diffusion</td><td>2000M</td><td>0.68</td><td>0.70</td><td>0.68</td><td>0.54</td><td>0.64</td><td>0.62</td></tr><tr><td>SD-XL [36]</td><td>Diffusion</td><td>2000M</td><td>0.71</td><td>0.73</td><td>0.69</td><td>0.50</td><td>0.66</td><td>0.63</td></tr><tr><td>Midjourney v6</td><td>Diffusion</td><td>-</td><td>0.78</td><td>0.78</td><td>0.79</td><td>0.50</td><td>0.76</td><td>0.69</td></tr><tr><td>DALL-E 3 [2]</td><td>Diffusion</td><td>-</td><td>0.82</td><td>0.78</td><td>0.82</td><td>0.48</td><td>0.80</td><td>0.70</td></tr><tr><td>Show-o [62]</td><td>Discrete Diff.</td><td>36M</td><td>0.70</td><td>0.62</td><td>0.71</td><td>0.51</td><td>0.65</td><td>0.60</td></tr><tr><td>LWM [27]</td><td>Autoregressive</td><td>-</td><td>0.59</td><td>0.58</td><td>0.54</td><td>0.49</td><td>0.52</td><td>0.53</td></tr><tr><td>VILA-U [61]</td><td>Autoregressive</td><td>15M</td><td>0.70</td><td>0.71</td><td>0.74</td><td>0.53</td><td>0.66</td><td>0.64</td></tr><tr><td>Liquid [59]</td><td>Autoregressive</td><td>30M</td><td>0.76</td><td>0.73</td><td>0.74</td><td>0.46</td><td>0.74</td><td>0.65</td></tr><tr><td>UniTok</td><td>Autoregressive</td><td>30M</td><td>0.76</td><td>0.76</td><td>0.79</td><td>0.46</td><td>0.73</td><td>0.67</td></tr></table>
+
+# C Impact Statement
+
+This paper presents work whose goal is to advance the field of Machine Learning. There are many potential societal consequences of our work, none which we feel must be specifically highlighted here.

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NeurIPS/2025/UniTransfer_ Video Concept Transfer via Progressive Spatio-Temporal Decomposition/full.md

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+# UniTransfer: Video Concept Transfer via Progressive Spatial and Timestep Decomposition
+
+Guojun Lei $^{1*}$ , Rong Zhang $^{2*}$ , Tianhang Liu $^{1}$ , Hong Li $^{4}$ , Zhiyuan Ma $^{3\dagger}$ , Chi Wang $^{5}$ , Weiwei Xu $^{1\dagger}$
+
+$^{1}$  State Key Lab of CAD&CG, Zhejiang University,  $^{2}$  Zhejiang Gongshang University,  $^{3}$  Huazhong University of Science and Technology,  $^{4}$  Beihang University,
+
+<sup>5</sup> Ningbo Global Innovation Center, Zhejiang University  
+guojunlei@zju.edu.cn, zhangrong@zjgsu.edu.cn, link0502@buaa.edu.cn, tianhang@zju.edu.cn, mzyth@hust.edu.cn, wangchi1995@zju.edu.cn, xww@cad.zju.edu.cn
+
+[Web Page]
+
+![](images/3c08302cac3a9ed546184d51e5c481406d2f0bceb14e0fcc1d926a8a750bf02d.jpg)
+
+![](images/6672b18abf6bca75915dbfb0f32fb6460e1df5960f98776f103bdb2e4a1161b3.jpg)
+
+![](images/417652e5526cc510813911d955192f98aee27d7f4f381b2216bc6464eb9761be.jpg)  
+Figure 1: Results of our UniTransfer. These qualitative results exhibit the superior performance of our approach in transferring various reference components, including characters, garments, backgrounds, and motions, to synthesize the new target videos.
+
+![](images/be441be8280de8fc038521d219ef128645be60d6c7b2fa1e48a1c713ea897934.jpg)
+
+# Abstract
+
+Recent advancements in video generation models have enabled the creation of diverse and realistic videos, with promising applications in advertising and film production. However, as one of the essential tasks of video generation models, video concept transfer remains significantly challenging. Existing methods generally model video as an entirety, leading to limited flexibility and precision when solely editing specific regions or concepts. To mitigate this dilemma, we propose a novel architecture UniTransfer, which introduces both spatial and diffusion timestep decomposition in a progressive paradigm, achieving precise and controllable video concept transfer. Specifically, in terms of spatial decomposition, we decouple videos into three key components: the
+
+foreground subject, the background, and the motion flow. Building upon this decomposed formulation, we further introduce a dual-to-single-stream DiT-based architecture for supporting fine-grained control over different components in the videos. We also introduce a self-supervised pretraining strategy based on random masking to enhance the decomposed representation learning from large-scale unlabeled video data. Inspired by the Chain-of-Thought reasoning paradigm, we further revisit the denoising diffusion process and propose a Chain-of-Prompt (CoP) mechanism to achieve the timestep decomposition. We decompose the denoising process into three stages of different granularity and leverage large language models (LLMs) for stage-specific instructions to guide the generation progressively. We also curate an animal-centric video dataset called OpenAnimal to facilitate the advancement and benchmarking of research in video concept transfer. Extensive experiments demonstrate that our method achieves high-quality and controllable video concept transfer across diverse reference images and scenes, surpassing existing baselines in both visual fidelity and editability. Web Page: https://yu-shaonian.github.io/UniTransfer-Web/
+
+# 1 Introduction
+
+Recent years, the rapid advancement of generative AI technologies [13, 36, 33] has greatly reformed the community of video editing, opening up new potentials for diverse, fine-grained, and user-controllable content manipulation tasks. Video Concept Transfer (VCT) is one of the most important downstream tasks in video editing, aiming to substitute various user-specified target concepts in a video, such as objects, characters, backgrounds, or subject motions, to enable personalized content manipulation. Its applications span across diverse areas such as film production, game development, virtual reality, etc., drawing increasing attention from both academia and industry.
+
+Despite its potential, achieving high-quality video concept transfer is still challenging as it requires seamless integration of different components, preservation of the identity of the target object, and visual fidelity of the generated videos. Some recent approaches rely on text-based guidance to control the transfer [10, 4, 41]. However, the inherent ambiguity of natural language descriptions often results in imprecise manipulation of object attributes and behaviors, restricting their application scenarios. In contrast, image-guided methods offer a more accurate way to encode the appearance and identity of target objects. For example, methods likeAnimateAnyone2[15] and MIMO [29] have demonstrated the ability to replace human subjects in videos with specific reference images. However, these existing approaches mainly focus on human transfer and struggle to generalize to broader editing tasks [25] involving arbitrary objects, backgrounds, garments or motion patterns. VideoSwap [11] and AnyV2V [20] rely on personalized modeling or image editing techniques to address this dilemma. But the ability of their foundation models limits their scalability and flexibility in complex videos.
+
+This paper focuses on image-based video concept transfer, including animals, characters, backgrounds, and motions. This task inherently involves manipulations and integrations of different components within a video, which often exhibit substantial variation in terms of visual appearance, motion pattern, or semantic attributes. However, most existing approaches [23, 41, 11, 20, 28] generally model the video as a unified whole without considering the heterogeneous nature of its constituents, which may introduce undesired artifacts. MIMO [29] introduced spatial decomposed modeling to VCT by decomposing a video into three predefined components: human, scene, and occlusion. It helps improve the quality of video character replacement. However, MIMO only decomposes videos in the spatial dimension, which is not sufficient for high-quality video generation, as it takes all the timesteps in the denoising process equally. Motivated by ProSpect [53], diffusion models actually generate images in the progressive order of "layout  $\rightarrow$  content  $\rightarrow$  texture", and this work also exhibits that different stages in the diffusion models require guidance at different granularities.
+
+In this work, we propose UniTransfer, a Diffusion Transformer (DiT) based video concept transfer framework via progressive decomposition of both the spatial dimension and the denoising process. In the spatial dimension, we decompose videos into three core components: foreground, background, and motion dynamics, enabling our model to adapt to general concept transfer flexibly. To achieve this, we allow the model to learn the decomposition from coarse foreground masks to detailed ones. Specifically, we first introduce a random masking-based self-supervised pretraining strategy to
+
+strengthen the decomposed representation learning, which enables the model to capture disentangled features without requiring fine-grained annotations. Then we design a dual-to-single-stream Dit architecture to realize further decomposition with delicate semantic annotations. In this stage, individual branches are responsible for encoding different video components, and their features are later integrated into a unified representation through a single-stream network. This design enhances the model's capacity to manipulate different objects and maintain the temporal consistency.
+
+In the denoising process, we decompose the timestep into coarse-grained, mid-grained, and fine-grained stages instead of modeling it equally. Inspired by the Chain-of-Thought (CoT) strategy, we develop a Chain-of-Prompt (CoP) mechanism and leverage Large Language Models (LLMs) to produce hierarchical prompts at different granularities and utilize them to guide the generation, enabling progressive refinement from noise to detailed textures. The main contributions are summarized as follows:
+
+- We propose a DiT-based image-guided video concept transfer framework UniTransfer, which incorporates progressive spatial and timestep decomposition.  
+- We introduce a self-supervised pretraining strategy based on randomized masking to enhance the disentangled representation learning and design a dual-to-single-stream architecture to achieve spatial decomposition.  
+- We further introduce an LLMs-guided chain-of-prompt mechanism to achieve the timestep decomposition. This progressive prompting strategy guides the generation process with stage-specific instructions, improving the VCT generation quality.  
+- We collect an animal-centric video dataset called OpenAnimal to facilitate the training and benchmarking of research in video concept transfer. Extensive experiments demonstrate that our method outperforms state-of-the-art methods in various video concept transfer scenarios.
+
+# 2 Related Work
+
+Text-driven Video Editing. Video editing has witnessed remarkable advances in conditional content synthesis and manipulation through diffusion-based architectures [27]. Recently, video editing typically relies on textual prompts to control object attributes or behaviors [10, 4, 1]. Video-P2P [23, 32] achieves preservation of motion dynamics through attention modulation. RF-Edit [41] preserves structural integrity and temporal consistency by rectified flow ODE solving with reduced error. MotionDirector [55] requires separate training for each motion type. When adapting to new scenarios, it only supports text-guided scene transitions, which significantly limits its flexibility. VMC [18] also needs separate training for different scenes. However, due to the inherent ambiguity and underspecification of natural language, such methods often struggle to achieve fine-grained and accurate control over video content.
+
+Image-guided Video Editing. To overcome these limitations, researchers introduce additional reference images to guide the editing process of the video subject. MIMO [29] and MovieCharacter [34] decompose the video into elements such as foregrounds and preprocessed backgrounds, and perform character transfer through video composition techniques.AnimateAnyone2 [15] proposes a framework to animate characters while considering environmental affordances. Despite their effectiveness, they are designed for character video editing, which limits the application scenarios.
+
+In contrast, VideoSwap [11] pioneers a more general approach to image-guided concept transfer through semantic correspondence learning, enabling more versatile and accurate video edits beyond character animation. Yet critically, its reliance on multi-image concept anchoring introduces semantic abstraction, achieving category-level consistency (e.g., kitten, airplane) but failing to preserve instance-specific attributes. AnyV2V [20] leverages arbitrary image editing tools [3, 42, 7] to modify the first frame of a video and then propagates the modifications to subsequent frames, enabling instance-based object-driven editing and identity manipulation. However, the two-stage pipeline heavily depends on the results of off-the-shelf image editing techniques, and the temporal consistency can not be guaranteed. To address these limitations, we propose a novel framework that does not rely on external image editing tools. Instead, our method decomposes the video into three disentangled components: foreground, background, and motion. Integrating with a carefully-tailored network architecture in conjunction with a large language model (LLM)-based chain-of-thought reasoning mechanism, our method can achieve precise and flexible video concept transfer.
+
+![](images/ea4f934ee795c8870fc38f922e5c7acb6558768a593f50a0992ff1c5434fc613.jpg)  
+Figure 2: Our progressive spatial and timestep decomposition modeling.
+
+Chain-of-Thought Prompting [19] aims to substantially improve the reasoning capabilities of large language models (LLMs) [6]. It provides a framework for complex multi-step inference through explicit intermediate reasoning steps. The CoT concept evolves into CoX [44], where X denotes swappable nodes (e.g. intermediates [19, 56, 21, 40], augmentation [12, 49, 9, 52], feedback [46, 24, 2, 8], and models [47, 5, 45]) for task-specific adaptation. We further propose Chain-of-Prompt (CoP), which shifts the CoX paradigm to the iterative denoising steps of diffusion models. This systematic approach decomposes the video generation into sequential coarse-to-fine steps, where hierarchical prompt guidance progressively refines temporal features through successive diffusion iterations.
+
+# 3 Method
+
+In this section, we present UniTransfer, a DiT-based image-guided video concept transfer framework with progressive decomposed video modeling. Our goal is to generate high-quality videos with user-specified concepts in the referenced images, including objects, characters, animals, backgrounds, or motion dynamics. To achieve this, we decompose the video both in the spatial and the timestep dimension. The overview of the proposed framework is illustrated in Figure 2.
+
+# 3.1 Spatial Decomposition
+
+Existing video generation methods typically treat the video as a holistic entity and attempt to model it under the guidance of text prompts or reference images. Although such strategies are effective for general video synthesis, they fall short in the context of video concept transfer, which requires compositional control over different parts of the video, including foreground objects, background scenes, and motion dynamics. Encoding the entire video into a single latent space makes it difficult to independently manipulate these components during generation. As a result, existing image-based video object transfer methods are often limited to transferring only the main subject, instead of manipulating different components, including background appearance or motion dynamics.
+
+To achieve more flexible and controllable video concept transfer, we propose to spatially disentangle the video generation process. Specifically, a video can be decomposed into three distinct components: the foreground  $M$ , the background  $B$ , and the corresponding motion flow  $F$ . In general, the foreground component may consist of different objects (e.g., characters, animals, or objects). Under this setting, we redefine the video denoising process as follows:
+
+$$
+\mathcal {L} (\theta) = \mathbb {E} _ {x _ {0}, \epsilon , \mathcal {U}, t} \left[ \| \epsilon - \hat {\epsilon} _ {\theta} (x _ {t}, \tau , \mathcal {U}, t) \| _ {2} ^ {2} \right], \tag {1}
+$$
+
+![](images/a2448abffcb7cd62965d6ff982a0c091d1d69901b3ddea6b8610ab6b544d98f3.jpg)  
+Figure 3: The architecture of our UniTransfer.
+
+where  $\tau$  is the condition of text prompts,  $\mathcal{U} = (M,B,F)$ . This decomposition enables us to treat each component independently and recombine them flexibly in the generation pipeline, laying the foundation for video concept transfer.
+
+Building upon this decomposed formulation, we propose a progressive learning scheme that enables the model to effectively capture and utilize the decomposed components for video generation. To achieve this, we design two modules: (1) a random masking-based self-supervised learning mechanism to model the coarse relationships between the foregrounds and the backgrounds without delicate semantic annotations. (2) a carefully designed dual-to-single stream architecture, UniTransfer, tailored to learn detailed interactions of three different components. In the following, we will introduce them respectively.
+
+# 3.1.1 The Architecture of UniTransfer
+
+In this stage, we propose a DiT-based architecture named UniTransfer to decompose the video into three components: the foreground appearance, the background scene, and the motion dynamics to enable flexible and fine-grained control over VCT. Unlike previous approaches that treat the video as a monolithic entity, UniTransfer is designed to disentangle and recompose these components in a controllable diffusion-based generation process.
+
+In the training process, given the input video  $V$ , we first randomly sample a single frame to serve as a reference image  $I$ . A pretrained semantic segmentation model is then utilized to predict a foreground mask  $M \in \{0,1\}^{H \times W}$ , which partitions the reference frame into a foreground region  $F = I \odot M$  and a background region  $B = I \odot (1 - BBox(M))$ , where  $\odot$  represents element-wise multiplication and  $BBox(M)$  means all the pixels in the bounding box are setting to 1. With the unaligned masks, we force the model to learn shape-agnostic interactions between the foreground and the background. Meanwhile, motion dynamics are extracted from the entire video using a pretrained optical flow model RAFT [39], which captures the temporal dynamics independent of appearance content. UniTransfer takes  $F, B, O$  along with the video description  $\tau$  as guidance and iteratively denoises from a randomly sampled Gaussian noise map to generate a new video  $\hat{V}$ . Figure 3 illustrates the framework of the network, which is in a dual-to-single-stream paradigm consisting of three collaborative branches: a foreground branch, a background branch, and a fusion branch.
+
+The foreground and background branches are built based on the CogVideoX architecture[48]. Each of them is composed of an image VAE encoder to project  $F$  and  $B$  into latent codes  $z_{f}$  and  $z_{b}$ , a text encoder to provide shared high-level text embedding  $z_{\tau}$ , and a stack of  $N$  DiT blocks for temporal feature modeling. In the background branch, a random Gaussian noise vector  $z_{t}$  of the timestep  $t$  is concatenated with  $z_{b}$  and  $z_{\tau}$ , and the combined representation is passed through the DiT blocks  $h_{b}^{i}$ , for  $i = 1, \dots, N$ . In contrast, the foreground branch is treated as a conditional stream with no noise input. It adopts a symmetric structure and produces intermediate feature maps for each DiT block  $h_{f}^{i}$ , for  $i = 1, \dots, N$ . Afterwards, we design a feature injection module that introduces foreground features into the background stream inspired by ControlNet [51]. Specifically, the foreground feature map is processed by a zero-initialized convolutional projection layer and then element-wise added to the corresponding layer in the background branch at each DiT block. It allows the model to inject spatially aligned foreground appearance into the generative path.
+
+Moreover, the enhanced background features are combined with the motion dynamics in the fusion branch, which consists of a motion encoder to project the optical flow to a latent code  $z_{o}$ , and  $N$
+
+DiT blocks for further feature fusion. The flow encoder adopts four symmetrically arranged stages, aligning with the structure in 3D VAE encoder [48]. Inspired by Tora[54], we also design an adaptive norm layer before adding it to the dit block. To enable the model to handle the misalignments between the motion and the input images and improve the generalization ability, we inject random noise into the input optical flow.  $z_{o}$  is fused with the background stream through element-wise addition in each fusion DiT block  $h^{i}$ , for  $i = 1,\dots ,N$ . The model outputs the predicted noise at a given denoising timestep  $t$  as follows:
+
+$$
+\hat {\epsilon} _ {\theta} = h \left[ Z C o n v \left(h _ {f} \left(z _ {f} \odot z _ {\tau}\right)\right) \oplus h _ {b} \left(z _ {t} \odot z _ {b} \odot z _ {\tau}\right) \oplus z _ {o} \right] \tag {2}
+$$
+
+where  $ZConv$  represents the zero-initialized convolution.  $\odot$  and  $\oplus$  represent the concatenation operation and element-wise addition, respectively.
+
+# 3.1.2 Self-Supervised Pre-Training via Random Masking
+
+Learning robust video representations for concept-aware generation typically requires large-scale datasets with annotated semantic masks or motions. However, existing video datasets rarely provide sufficient annotations. Relying on small-scale annotated datasets is not enough to directly learn spatially decoupled representations for video concept transfer. To address this limitation, we follow LMD [26] to introduce a random masking strategy before training with fine-grained annotations. It leverages large-scale unlabeled video datasets for learning initial disentangled representations. In this stage, we only learn the foreground and background decomposition for coarse initialization. In this stage, the binary foreground mask  $M \in \{0,1\}^{H \times W}$  is generated through random masking to arbitrarily partition the reference image  $I$  into two regions  $F$  and  $B$ . The mask is initialized as an all-zero matrix and is iteratively updated by randomly drawing rectangles of random size and position on it. The pixels inside each rectangle are set to 1, and the process continues until the foreground coverage exceeds  $50\%$  of the image area. This masking strategy ensures spatial diversity and balance between foreground and background regions. Then the reference frame is partitioned into a foreground region  $F = I \odot M$  and a background region  $B = I \odot (1 - \text{Dilate}(M))$ , where  $\text{Dilate}(M)$  represents the morphological dilation operation to enable the model to learn the boundary interactions. Then  $F$  and  $B$  are fed into the foreground branch and a background branch, which inject the features into the denoising network independently. This allows the model to learn how to reconstruct coherent video content from partial and spatially isolated cues, without requiring ground-truth foreground-background labels. Through extensive self-supervised training on large-scale data, our model acquires strong prior knowledge, enabling efficient adaptation to downstream tasks via precise supervised fine-tuning.
+
+# 3.2 Timestep Decomposition
+
+Revisiting the preliminary of diffusion models reveals another limitation in current video generation pipelines: the same conditioning prompt is applied across all timesteps during the denoising process. It is difficult to generate grained texture corresponding to the grained prompts at the initial stage. Inspired from ProSpect [53], the role of each timestep varies significantly in the generation process. In the early stages of denoising, the model focuses primarily on recovering the global structure and semantic layout of the video. In contrast, the later stages are responsible for learning fine-grained details such as textures, colors, and subtle appearance attributes. Applying a static, single-level prompt across all timesteps ignores this progression in modeling focus. For instance, using overly complex or fine-grained textual prompts during early timesteps may misguide the model, leading to the omission of some semantic attributes or misalignments between the generated content and the input prompt.
+
+To address this, we introduce a timestep decomposition mechanism named Chain-of-Prompt (CoP) inspired by the Chain-of-Thought reasoning paradigm in large language models. Specifically, we decompose the denoising timesteps into three granularity levels—coarse, medium, and fine—and leverage a large language model (LLM) to automatically generate three levels of prompts that progressively reflect the abstraction and detail required at different denoising stages. These prompts are then injected into the diffusion model in a stage-aware manner, ensuring that early timesteps receive high-level, structural guidance, while later steps benefit from detailed, appearance-level
+
+![](images/94ff65bb3bd4b84bb24668b29c24e296a062a60e951ff3090b855adf3c1beb3d.jpg)  
+Figure 4: Comparison of video character transfer. Note that our backgrounds are better preserved. control. Then the denoising loss can be defined as follows:
+
+$$
+\mathcal {L} (\theta) = \left\{ \begin{array}{l l} \| \epsilon - \hat {\epsilon} _ {\theta} \left(z _ {t}, \tau_ {c r s}, \mathcal {U}, t\right) \| _ {2} ^ {2}, & t \in [ t _ {c}, T - 1 ] \\ \| \epsilon - \hat {\epsilon} _ {\theta} \left(z _ {t}, \tau_ {m i d}, \mathcal {U}, t\right) \| _ {2} ^ {2}, & t \in [ t _ {f}, t _ {c}) \\ \| \epsilon - \hat {\epsilon} _ {\theta} \left(z _ {t}, \tau_ {f i n e}, \mathcal {U}, t\right) \| _ {2} ^ {2}, & t \in [ 0, t _ {f}) \end{array} \right. \tag {3}
+$$
+
+where  $\tau_{crs},\tau_{mid},\tau_{fine}$  are coarse-grained, mid-grained and fine-grained text prompts, respectively.  $t_c$  and  $t_f$  are the end points of the corresponding stages.  $T$  is the total timestep of the diffusion process. In our paper, as the dataset provides detailed video descriptions, we utilize them as the fine-grained prompts. Then we employ an LLM Qwen-QWQ-32B [38] to summarize  $\tau_{fine}$  to  $\tau_{crs},\tau_{mid}$ . This decomposition strategy helps to align the complexity of text guidance with the focus of the generation phase, improving both semantic consistency and visual fidelity in the final video output.
+
+# 4 Experiments
+
+To better demonstrate the strengths of our approach, we conduct experiments to validate foreground transfer, background transfer, and optical flow transfer across videos. The implementation details can be referred to the appendix.
+
+# 4.1 Animal-centric Dataset
+
+To promote the broader applicability of video editing models, we introduced a new dataset OpenAnimal, which focused on single-animal video sequences across a wide range of species and diverse motion patterns. While following a similar structure to human-centric datasets (e.g., TikTok, UBC), OpenAnimal is specifically tailored for animal subjects with 10000 video clips.
+
+# 4.2 Comparison and Analysis
+
+As current state-of-the-art methods are primarily trained on human datasets, they often struggle to generalize to non-human scenarios, such as animal motion transfer or object-level editing in nature-based scenes. For fair comparison, we conducted qualitative and quantitative experiments on UBC[50] and TikTok[17] to evaluate the video character transfer performance. Besides, to further evaluate our model's ability to generalize to other concepts, we also demonstrate qualitative results of animal transfer, cloth transfer, background transfer, and motion transfer.
+
+# 4.2.1 Video Character Transfer
+
+To evaluate the effectiveness of our method in video character transfer, we compare it with state-of-the-art character animation and video editing baselines, includingAnimate Anyone [14],Champ [57],
+
+Table 1: Quantitative comparison of video quality for video character transfer.  
+
+<table><tr><td rowspan="2"></td><td colspan="5">TikTok</td><td colspan="5">UBC</td></tr><tr><td>LPIPS↓</td><td>PSNR↑</td><td>SSIM↑</td><td>FID↓</td><td>FVD↓</td><td>LPIPS↓</td><td>PSNR↑</td><td>SSIM↑</td><td>FID↓</td><td>FVD↓</td></tr><tr><td>MRAA</td><td>0.513</td><td>25.31</td><td>0.425</td><td>134.23</td><td>634</td><td>0.589</td><td>23.32</td><td>0.537</td><td>89.13</td><td>438</td></tr><tr><td>DisCo</td><td>0.674</td><td>21.56</td><td>0.532</td><td>112.24</td><td>571</td><td>0.467</td><td>23.45</td><td>0.412</td><td>94.15</td><td>483</td></tr><tr><td>MagicAnimate</td><td>0.259</td><td>24.56</td><td>0.657</td><td>89.23</td><td>428</td><td>0.143</td><td>27.08</td><td>0.742</td><td>48.19</td><td>391</td></tr><tr><td>Animate Anyone</td><td>0.198</td><td>25.89</td><td>0.713</td><td>79.18</td><td>398</td><td>0.132</td><td>26.54</td><td>0.798</td><td>44.18</td><td>378</td></tr><tr><td>Champ</td><td>0.241</td><td>26.57</td><td>0.787</td><td>67.14</td><td>401</td><td>0.159</td><td>27.03</td><td>0.811</td><td>41.02</td><td>324</td></tr><tr><td>UniAnimate</td><td>0.191</td><td>24.34</td><td>0.724</td><td>58.19</td><td>378</td><td>0.127</td><td>27.09</td><td>0.798</td><td>43.24</td><td>334</td></tr><tr><td>Ours</td><td>0.152</td><td>26.78</td><td>0.803</td><td>46.74</td><td>345</td><td>0.125</td><td>27.12</td><td>0.814</td><td>39.73</td><td>312</td></tr></table>
+
+Table 2: Video consistency quality comparison. SubC: Subject Consistency; BkgC: Background Consistency; MoS: Motion Smoothness; AesQ: Aesthetic Quality; DyaD: Dynamic Degree.  
+
+<table><tr><td rowspan="2"></td><td colspan="5">TikTok</td><td colspan="5">UBC</td></tr><tr><td>SubC↑</td><td>BkgC↑</td><td>MoS↑</td><td>AesQ↑</td><td>DyaD↑</td><td>SubC↑</td><td>BkgC↑</td><td>MoS↑</td><td>AesQ↑</td><td>DyaD↑</td></tr><tr><td>Video-P2P</td><td>0.842</td><td>0.853</td><td>0.782</td><td>0.443</td><td>0.710</td><td>0.813</td><td>0.867</td><td>0.734</td><td>0.497</td><td>0.371</td></tr><tr><td>ControlVideo</td><td>0.712</td><td>0.419</td><td>0.747</td><td>0.519</td><td>0.823</td><td>0.814</td><td>0.773</td><td>0.895</td><td>0.624</td><td>0.241</td></tr><tr><td>RF-Editor</td><td>0.911</td><td>0.873</td><td>0.581</td><td>0.423</td><td>0.765</td><td>0.825</td><td>0.648</td><td>0.789</td><td>0.434</td><td>0.627</td></tr><tr><td>MotionClone</td><td>0.784</td><td>0.865</td><td>0.891</td><td>0.651</td><td>0.830</td><td>0.924</td><td>0.943</td><td>0.871</td><td>0.613</td><td>0.679</td></tr><tr><td>CogVideoX</td><td>0.927</td><td>0.904</td><td>0.926</td><td>0.557</td><td>0.842</td><td>0.911</td><td>0.917</td><td>0.911</td><td>0.663</td><td>0.902</td></tr><tr><td>Mofa-Video</td><td>0.923</td><td>0.917</td><td>0.962</td><td>0.593</td><td>0.837</td><td>0.923</td><td>0.879</td><td>0.957</td><td>0.612</td><td>0.829</td></tr><tr><td>Ours</td><td>0.945</td><td>0.931</td><td>0.962</td><td>0.651</td><td>0.903</td><td>0.939</td><td>0.942</td><td>0.971</td><td>0.664</td><td>0.911</td></tr></table>
+
+UniAnimate [43], AnyV2V [20], Video-P2P [23], etc.. Qualitative results are presented in Figure 4. Given an input video, these existing approaches typically modify or replace objects based on textual or image-based prompts. As illustrated in Figure 4, the text-guided transfer methods fail to transfer the reference image to the video, while the image-guided baselines struggle to preserve the structural features or maintain the appearance of the backgrounds. In contrast, our approach leverages reference image guidance and incorporates a progressive spatial and timestep decomposition, which aligns more naturally with real-world editing workflows. Our approach can achieve better preservation of the reference appearance, higher visual quality of the video, and greater inter-frame consistency than all baselines. These improvements are attributed to our novel decomposition strategy, which enables fine-grained control and more structured video generation.
+
+Besides, we perform further quantitative evaluation from two perspectives: video quality and temporal consistency. As shown in Table 1 and Table 2, our method achieves superior performance across multiple metrics, including FID, LPIPS, and subject consistency, aesthetic quality, etc. from vbench[16], significantly outperforming the compared baselines. This demonstrates the robustness and generalization ability of our approach in complex video character transfer tasks.
+
+# 4.2.2 Adaptation to Various Video Concept Transfer Tasks.
+
+Our proposed decomposition-based modeling and random masking pre-training mechanism enable our framework to effectively isolate and manipulate individual visual factors within a video, which can generalize beyond conventional foreground transfer. In this section, we provide more diverse qualitative results of a wide range of video concept transfer tasks, including motion transfer, background transfer, animal transfer, and regional foreground transfer, such as clothing replacement. These results demonstrate the flexibility and adaptability of our approach in handling diverse transformations, which are typically challenging for conventional video editing methods.
+
+Motion transfer. Our framework supports motion transfer by applying the motion dynamics extracted from a driving video onto a reference image, similar to pioneer works[37]. To evaluate the effectiveness of our method on this task, we conduct comparisons with state-of-the-art motion-guided video generation approaches, including AnyV2V [20], MotionClone [22], MotionI2V [37], AnyV2V [20] and Mofa-Video [31]. The results are shown in Figure 5. As illustrated, MotionI2V struggles with maintaining the video consistency as time progresses, often introducing noticeable artifacts or drift in later frames. AnyV2V fails to preserve the appearance of the reference image. MotionClone primarily relies on text guidance for controlling video appearance, which limits its ability to precisely align the generated content with the reference image. Mofa-Video may generate blurry results. In contrast, our method produces videos that not only maintain high visual fidelity but also exhibit coherent and realistic motion consistent with the driving video. This advantage stems from our explicit spatial decomposition and motion-conditioned generation design, which enables better disentanglement and control over appearance and temporal dynamics.
+
+![](images/870cd7dd582e9212ca9d451b5f0b6e7aa5b08b8721c1da42a0bd4393af4c6961.jpg)  
+Figure 5: Comparison of video motion transfer.
+
+Table 3: Ablation study.  
+
+<table><tr><td rowspan="2"></td><td colspan="5">Visual Quality</td><td colspan="2">Video Smoothness</td></tr><tr><td>LPIPS ↓</td><td>PSNR ↑</td><td>SSIM ↑</td><td>FID ↓</td><td>FVD ↓</td><td>SubC ↑</td><td>BkgC ↑</td></tr><tr><td>Early Motion Injection</td><td>0.234</td><td>25.16</td><td>0.743</td><td>56.79</td><td>374</td><td>0.871</td><td>0.736</td></tr><tr><td>w/o Flow Noise Injection</td><td>0.293</td><td>24.31</td><td>0.659</td><td>69.31</td><td>391</td><td>0.915</td><td>0.893</td></tr><tr><td>w/o Self-Supervised Pre-training</td><td>0.498</td><td>19.87</td><td>0.546</td><td>101.34</td><td>487</td><td>0.735</td><td>0.892</td></tr><tr><td>w/o dual-stream decomposition</td><td>0.341</td><td>23.12</td><td>0.694</td><td>78.13</td><td>423</td><td>0.894</td><td>0.639</td></tr><tr><td>w/o CoP Timestep Decomposition</td><td>0.192</td><td>25.87</td><td>0.712</td><td>51.32</td><td>357</td><td>0.881</td><td>0.924</td></tr><tr><td>Full Model</td><td>0.152</td><td>26.78</td><td>0.803</td><td>46.74</td><td>345</td><td>0.945</td><td>0.931</td></tr></table>
+
+Foreground transfer. Our framework enables flexible foreground transfer, including part-level object replacement, such as garment transfer. It poses significant challenges due to the need to selectively modify localized visual regions while preserving the subject's identity and overall video harmonization. This is particularly important in fashion, virtual try-on, and personalization applications. As shown in Figure 7, our model successfully replaces the clothing items in the target videos, with the referenced garments, without disrupting the consistency of facial features, body motion, or background. These results highlight the strength of our decomposition framework in enabling precise and high-quality object-level modifications within complex video generation tasks.
+
+Background transfer. In most video editing approaches, modifications are typically limited to the main subject or local elements. In contrast, our method supports background replacement by leveraging the spatial disentanglement. As illustrated in Figure 7, our framework enables users to seamlessly replace the entire background of a video. Our approach ensures that background changes—such as scene transitions from indoor to outdoor—are consistent across all frames, while the foreground subject remains temporally coherent and unaffected in terms of identity and motion.
+
+# 4.3 Ablation Study.
+
+To verify the effectiveness of the components in our video generation pipeline, we designed several sets of ablation experiments on TikTok[17]. Specifically, we evaluate the following configurations: (1) Early Motion Injection: Instead of injecting motion in the intermediate network layers, we input motion information alongside the foreground and background features in the early layers, and apply attention-based fusion within the denoising branch. (2) Feature Fusion (w/o dual-stream decomposition): We directly concatenate foreground and background features and feed them into the denoising module. (3) Without Self-Supervised Pre-training. (4) Without CoP Timestep Decomposition. (5) Without Flow Noise Injection. The ablation study results are shown in Table 3. From the first two rows, we can see the superiority of the design of our motion branch, where the added noise improves the generalization ability and the injection strategy provides more accurate guidance. The third row emphasizes that the self-supervised training is critical for enhancing the
+
+decomposed representation learning. The last two rows illustrate that our modeling of the spatial and timestep decomposition both are essential for flexible and high-quality video concept manipulation.
+
+# 5 Conclusion
+
+In this work, we present a novel framework for controllable video concept transfer by introducing a progressive spatial and timestep decomposition modeling strategy. Unlike existing methods that treat the video as a holistic entity, our approach explicitly disentangles the video into foreground, background, and motion components, and further decomposes the denoising process into hierarchical stages via chain-of-prompt guidance. This design allows for more fine-grained control over video synthesis, enabling diverse video concept transfer tasks, such as character transfer, motion transfer, background transfer, and garment-level editing. We also incorporate a self-supervised pretraining scheme based on random masking to support robust learning under no additional supervision, which effectively bootstraps spatial disentanglement using large-scale unlabeled video data. Besides, we curate a new dataset featuring animal subjects to advance research in video generation. Extensive experiments on different transfer tasks demonstrate that our method significantly outperforms state-of-the-art baselines in terms of visual quality, consistency, and controllability.
+
+# Acknowledgments and Disclosure of Funding
+
+Weiwei Xu is partially supported by NSFC (No. 92570206, No.62421003). Rong Zhang is partially supported by NSFC (No. 62302449) and Zhejiang Provincial Natural Science Foundation of China (No. LQ23F020009). Zhiyuan Ma is supported by the National Natural Science Foundation of China (No. 62406161), China Postdoctoral Science Foundation (No. 2023M741950), and the Postdoctoral Fellowship Program of CPSF (No. GZB20230347). This paper is partially supported by the Yongjiang Innovation Project No. 2025Z062.
+
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+
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+
+# 7. Experiment statistical significance
+
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+
+# 8. Experiments compute resources
+
+Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
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+Answer: [Yes]
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+
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+
+# 10. Broader impacts
+
+Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
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+
+Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
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+
+# Guidelines:
+
+- The answer NA means that the paper does not use existing assets.  
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+# 13. New assets
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+
+# 14. Crowdsourcing and research with human subjects
+
+Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
+
+Answer: [NA]
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+
+# 15. Institutional review board (IRB) approvals or equivalent for research with human subjects
+
+Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
+
+Answer: [NA]
+
+Justification: The paper does not involve crowdsourcing nor research with human subjects.
+
+Guidelines:
+
+- The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.  
+- Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
+
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+- For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
+
+# 16. Declaration of LLM usage
+
+Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
+
+Answer: [NA]
+
+Justification: The core method development in this research does not involve LLMs as any important, original, or non-standard components.
+
+Guidelines:
+
+- The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.  
+- Please refer to our LLM policy (https://neurips.cc/Conferences/2025/LLM) for what should or should not be described.
+
+# A Video Diffusion Model Preliminary
+
+Video diffusion models generalize the concept of image diffusion probabilistic models [13] to the temporal domain. Formally, let  $z_0 \in \mathbb{V}^{f \times h \times w \times c}$  represent a video latent variable, where  $f$  denotes the number of frames, with  $h \times w$  dimension,  $c$  channels. The forward diffusion process is defined as a Markov chain that gradually corrupts the original video into Gaussian noise:
+
+$$
+z _ {t} = \sqrt {\bar {\alpha} _ {t}} z _ {t - 1} + \sqrt {1 - \bar {\alpha} _ {t}} \epsilon , \quad \epsilon \sim \mathcal {N} (0, \mathbf {I}), \tag {4}
+$$
+
+where  $t \in \{1, \dots, T\}$  indexes the diffusion timestep,  $\bar{\alpha}_t$  controls the noise intensity, and  $\epsilon$  represents standard Gaussian noise. In the reverse process, a denoising network is used to estimate the noise from  $z_{t-1}$  to  $z_t$ , typically called a Diffusion Model  $\theta$ . The training objective minimizes the following loss function:
+
+$$
+\mathcal {L} (\theta) = \mathbb {E} _ {z _ {0}, \epsilon , \mathcal {C}, t} \left[ \| \epsilon - \hat {\epsilon} _ {\theta} (z _ {t}, \mathcal {C}, t) \| _ {2} ^ {2} \right], \tag {5}
+$$
+
+where  $\mathcal{C}$  represents conditioning information such as text prompts or reference images.
+
+# B Implementation Details
+
+In this paper, we focus on image-guided video concept transfer, where the foregrounds, backgrounds, or motion dynamics can be manipulated. We perform experiments including character, animal, object, background and motion transfer to evaluate our approach. The weights of the model are initialized from a pretrained CogVideoX-5B [48]. The experiments are conducted on a server equipped with  $8 \times$  NVIDIA Tesla H100 80G GPUs. In our paper, we generate 49-frame videos at the resolution of  $720 \times 480$ .
+
+Our training pipeline consists of two stages: a large-scale self-supervised pretraining phase and a supervised fine-tuning phase. In the pre-training phase, we trained our model on the OpenVid dataset[30], a large-scale collection of approximately 12 million videos covering a wide range of categories including humans, animals, vehicles, and diverse backgrounds. The training used a learning rate of  $1 \times 10^{-4}$  and ran for 200,000 iterations. The OpenVid dataset comprises approximately 12 million samples.
+
+In the supervised training phase, we adapt the pretrained model to specific video concept transfer scenarios. For the human-centric editing task, we fine-tune the model on a combination of the TikTok [17] and UBC Fashion [50] datasets, which include rich annotations for character-level transfer tasks. The model is finetuned for 10,000 iterations with a learning rate of  $5 \times 10^{-5}$ . For the animal-centric task, we performed an additional 10,000 fine-tuning iterations on OpenAnimal using the same learning rate.
+
+# C More Details about Self-Supervised Pretraining
+
+In practice, obtaining large amounts of high-quality annotated data remains challenging. Although efficient segmentation tools like SAM2[35] are available, they still require extensive manual interactions, such as point prompts, bounding boxes, or object-specific filtering. To address this limitation and reduce the reliance on manual annotations, we introduce a self-supervised pretraining approach, the detailed algorithmic pipeline of which is outlined below Algorithm 1 and Figure 6.
+
+# D More Details about CoP Guidance
+
+The detailed algorithmic pipeline of our Chain-of-Prompt (CoP) guidance is demonstrated in Algorithm 2.
+
+To assess the effectiveness of CoP, we conducted ablation experiments comparing it with static prompts, including: (i) All Coarse Prompt: using a coarse prompt across all timesteps, and (ii) All Detailed Prompt: using a detailed prompt across all timesteps. The results demonstrate that the allocoarse setting tends to produce results lacking fine-grained appearance fidelity and exhibiting texture degradation, while the all-detailed setting often introduces misguidance during early denoising steps, leading to distorted structures. In contrast, our CoP strategy, which progressively schedules coarse-to-fine prompts, strikes a balance between structural stability and appearance precision, achieving better performance. The quantitative evaluation can be referred to in Table 4.
+
+![](images/c381fbb8aae59c318b46198f911ceccd358f996e7ec4d118ec1e1299dc83347f.jpg)  
+Figure 6: Self-supervised Pretraining.
+
+# E More Results
+
+Our framework enables flexible foreground and background transfer, including part-level object replacement, such as garment transfer. The results of different transfer tasks are shown in Figure 10, Figure 11, Figure 12, Figure 7. Our curated animal-centric dataset OpenAnimal is demonstrated in Figure 13.
+
+![](images/35d93655e468110916dbde259db5f3480766a21f013e70f57be8753da8447053.jpg)  
+Figure 7: More UniTransfer results of different transfer tasks.
+
+# F Limitation
+
+Although our model can achieve subject transfer and background replacement in videos, there are still cases where the subject and background appear with artifacts. This issue might be due to current segmentation models cannot fully separate foreground and background elements, leading to imperfect composite results in the final output. In the future, we plan to address this by leveraging large-scale models for enhanced video scene understanding, further improving the quality of generated videos.
+
+For scenes involving multiple moving entities, we can fuse their individual optical flows into a composite representation and sequentially condition the network on each object through multiple inference passes. For interacting subjects, our method remains effective when the occlusion or overlap between them is relatively small, as each subject's region can still be identified and modeled with reasonable independence. However, cases with large overlapping areas or complex physical interactions pose challenges to our models due to ambiguity in motion attribution and visual entanglement. Joint modeling of interacting subjects is an interesting direction for future work.
+
+Algorithm 1: The pipeline of our self-supervised pretraining.  
+Input: image: input image coverage: target coverage ratio (default: 0.5) min_block_size: minimum block size (default: 320) max_block_size: maximum block size (default: 640) Output: foreground: processed image with coverage background: inverse masked image   
+Function random_white_blocks(image, coverage, min_block_size, max_block_size) if image is None then raise ValueError("Input image is empty"); if image is grayscale then result  $\leftarrow$  convert image to BGR color space; else result  $\leftarrow$  copy of image;  $(h,w)\gets$  height and width of result; total_area  $\leftarrow h\times w$  covered_area  $\leftarrow 0$  target_area  $\leftarrow$  total_area  $\times$  coverage; mask  $\leftarrow$  zero matrix of size  $(h,w)$  . result_2  $\leftarrow$  gray matrix (127.5) with same size as result; max_iter  $\leftarrow$  500; while covered_area  $<  <$  target_area and max_iter  $>0$  do max_iter  $\leftarrow$  max_iter-1; block_size  $\leftarrow$  random integer between min_block_size and max_block_size;  $x\gets$  random integer between 0 and  $w -$  block_size;  $y\gets$  random integer between 0 and  $h -$  block_size; if mask[y:y+block_size,x:x+block_size] contains any 1 then continue; >Special overlapping effect result_2[y:y+block_size-5,x :  $x +$  block_size-5]  $\leftarrow$  result[y:y+block_size-5,x:x+block_size-5]; result[y:y+block_size+5,x:x+block_size+5]  $\leftarrow$  [127.5,127.5,127.5]; mask[y:y+block_size,x:x+block_size]  $\leftarrow$  1; covered_area  $\leftarrow$  covered_area + block_size  $\times$  block_size; if covered_area  $>1.1\times$  target_area then break; return foreground, background;
+
+Table 4: CoP mechanism with different level prompts.  
+
+<table><tr><td rowspan="2"></td><td colspan="5">Visual Quality</td><td colspan="2">Video Smoothness</td></tr><tr><td>LPIPS ↓</td><td>PSNR ↑</td><td>SSIM ↑</td><td>FID ↓</td><td>FVD ↓</td><td>SubC ↑</td><td>BkgC ↑</td></tr><tr><td>All Coarse Prompt</td><td>0.174</td><td>23.15</td><td>0.701</td><td>72.46</td><td>433</td><td>0.714</td><td>0.865</td></tr><tr><td>All Detailed Prompt</td><td>0.165</td><td>24.62</td><td>0.734</td><td>49.26</td><td>367</td><td>0.832</td><td>0.901</td></tr><tr><td>Full Model</td><td>0.152</td><td>26.78</td><td>0.803</td><td>46.74</td><td>345</td><td>0.945</td><td>0.931</td></tr></table>
+
+# G Social Impact
+
+Our research decouples video generation into foreground, background, and their corresponding motion. This technology will enhance the efficiency of video production, drive innovation in industries such as film and gaming, and enable more immersive entertainment and educational experiences. However, as this technology becomes more widespread, society may face ethical and legal challenges, including concerns over the authenticity of video content, characters, and backgrounds. Therefore, establishing appropriate regulatory frameworks to ensure responsible use of this technology is a critical task that demands our attention.
+
+Algorithm 2: Chain-of-Prompt-Guided Video Denoising  
+Input: initial_noisy视频：Initial noisy video  
+base_prompt: Original text description  
+total_steps: Total denoising steps (default: 50)  
+T1, T2: Stage transition steps  
+Output: generated视频：Final generated video  
+Function hierarchical_denoising(initial_noisy视频，base_prompt，total_steps = 50)  
+// Phase partitioning (all steps)  $T_{1} \leftarrow 35$  // First phase  
+ $T_{2} \leftarrow 15$  // Second phase  
+// Generate hierarchical prompts using LLM  
+prompts  $\leftarrow$  QwQ32B_GenerateHierarchicalPrompts(base_prompt);  
+// Returns: {'stage1': coarse, 'stage2': detailed, 'stage3': fine}  
+current Video  $\leftarrow$  initial_noisy Video;  
+for step  $\leftarrow$  total_steps to 1 do  
+if step  $\geq T_{1}$  then  
+    guidance_prompt  $\leftarrow$  prompts['stage1']; // Coarse prompt  
+    guidance_weight  $\leftarrow 1.5$  // Strong guidance  
+else if step  $\geq T_{2}$  then  
+    guidance_prompt  $\leftarrow$  prompts['stage2']; // Detailed prompt  
+    guidance_weight  $\leftarrow 2.0$   
+else  
+    guidance_prompt  $\leftarrow$  prompts['stage3']; // Fine prompt  
+    guidance_weight  $\leftarrow 1.0$   
+// Execute denoising step current Video  $\leftarrow$   
+DenoiseStep(current Video, guidance_prompt, guidance_weight, step);  
+return current Video;
+
+![](images/e7219758d52cccfb32850423cbd14b7758571405e2ef65ec2d49d125e2a4e299.jpg)  
+Figure 8: More results compared with other methods.
+
+![](images/580a61e3cd806b6891643912a3371836b379e107cbb060f267510cf78d903732.jpg)  
+Figure 9: More Visual Results (Left is reference video, middle is reference image, right is output).
+
+![](images/974aacfccffbc2ec842676859042a3db7bc76e1663c34fd877501906507b31b2.jpg)  
+Figure 10: More Visual Results.(Left is reference video, middle is reference image, right is output)
+
+![](images/096e4478110851bc18ad429237c77365c98dcc875cc077b3c20849a82dbfe46e.jpg)  
+Figure 11: More Visual Results.(Left is reference video, middle is reference image, right is output)
+
+![](images/9d93a947d659a1ee3b85481f5d9a208ee1a8115039a76dc1c24c01dd359b281b.jpg)  
+Figure 12: Animal Motion Transfer.(Left is reference video, right is output)
+
+![](images/267018cf69e84d845e7bba8fee97409ceef3af9d379c7aa0c9f04fe262dc77c1.jpg)  
+Figure 13: OpenAnimals Datasets.

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