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| <div align="center"> | |
| <h1>[ICLR26] Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling </h1> | |
| <a href="https://www.arxiv.org/abs/2507.07982"> | |
| <img src='https://img.shields.io/badge/arxiv-geometryforcing-darkred' alt='Paper PDF'></a> | |
| <a href="https://geometryforcing.github.io/"> | |
| <img src='https://img.shields.io/badge/Project-Website-orange' alt='Project Page'></a> | |
| [Haoyu Wu](https://cintellifusion.github.io/)$^{1*}$, [Diankun Wu](https://github.com/diankun-wu) $^{2*}$, Tianyu He $^{1β }$, Junliang Guo $^{1}$, Yang Ye $^{1}$, Yueqi Duan $^{2}$, Jiang Bian $^{1}$ | |
| $^1$ Microsoft Research $^2$ Tsinghua University | |
| ($^*$ Equal Contribution. β Project Lead) | |
| </div> | |
| ## π― Overview | |
|  | |
| **Geometry Forcing (GF) Overview.** | |
| (a) Our proposed GF paradigm enhances video diffusion models by aligning with geometric features from VGGT. | |
| (b) Compared to DFoT, our method generates more temporally and geometrically consistent videos. | |
| (c) While baseline features fail to reconstruct meaningful 3D geometry, GF-learned features enable accurate 3D reconstruction. | |
| ## π News | |
| - [2026/01/26] Our Paper is accepted to [ICLR 2026](https://iclr.cc/) ! | |
| - [2025/10/8] We release the evaluation code for reprojection error and revisit error. | |
| - [2025/9/24] We release code and checkpoint. | |
| - [2025/9/22] [Geometry Forcing](https://geometryforcing.github.io/) is accepted to [NeurIPS 2025 NextVid Workshop](https://what-makes-good-video.github.io/) as an Oral! | |
| - [2025/7/10] We release the paper and the project. | |
| ## πͺ Get Started | |
| ### Setup Environments | |
| ```shell | |
| conda create -n geometryforcing python=3.10 -y | |
| conda activate geometryforcing | |
| pip install -r requirements.txt | |
| ``` | |
| ### Connect to Weights & Biases: | |
| We use Weights & Biases for logging. [Sign up](https://wandb.ai/login?signup=true) if you don't have an account, and *modify `wandb.entity` in `config.yaml` to your user/organization name*. | |
| ### Download Checkpoints and Data | |
| 1. Download pretrained checkpiont using huggingface: | |
| ```shell | |
| bash scripts/hf_download_checkpoints.sh | |
| ``` | |
| 2. Download pretrained checkpiont using modelscope: | |
| ```shell | |
| bash scripts/ms_download_checkpoints.sh | |
| ``` | |
| 3. Download and process RealEstate10k dataset to `data/real-estate-10k` | |
| The structure of RealEstate10K is exactly the same with DFoT. Please download RealEstate10k from dataset of DFoT from here [huggingface dataset](https://huggingface.co/kiwhansong/DFoT/tree/main/datasets). The structure should like this [wiki from DFoT](https://github.com/kwsong0113/diffusion-forcing-transformer/wiki/Dataset) | |
| ``` | |
| data/ | |
| βββ {dataset_name}/ | |
| β βββ training/ | |
| β β βββ video_xxx.mp4 | |
| β β βββ ... | |
| β βββ validation/ | |
| β β βββ video_xxx.mp4 | |
| β β βββ ... | |
| β βββ test/ | |
| β β βββ video_xxx.mp4 | |
| β β βββ ... | |
| β βββ metadata/ | |
| β β βββ training.pt | |
| β β βββ validation.pt | |
| β β βββ test.pt | |
| ``` | |
| ### Generating Videos with Pretrained Models | |
| 1. Single Image to Long Video (256 Frames): | |
| ```shell | |
| bash scripts/eval_geometry_forcing.sh | |
| ``` | |
| 2. Single Image to Rotation Video (16 Frames): | |
| ```shell | |
| bash scripts/eval_geometry_forcing_rotation.sh | |
| ``` | |
| ### Training Geometry Forcing | |
| To train Geometry Forcing, run the following command: | |
| ```shell | |
| bash scripts/train_geometry_forcing.sh | |
| ``` | |
| ### Evaluation for Reprojection Error and Revisit Error | |
| To evaluate the reprojection error and revisit error, please follow the instructions in [README_EVAL.md](README_EVAL.md). | |
| ## π Citation | |
| If you find our work useful for your research, please consider citing our paper: | |
| ``` | |
| @article{wu2025geometryforcing, | |
| title={Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling}, | |
| author={Wu, Haoyu and Wu, Diankun and He, Tianyu and Guo, Junliang and Ye, Yang and Duan, Yueqi and Bian, Jiang}, | |
| journal={arXiv preprint arXiv:2507.07982}, | |
| year={2025} | |
| } | |
| ``` | |
| # Research template attribution | |
| This repo is forked from [Boyuan Chen](https://boyuan.space/)'s research template | |
| [repo](https://github.com/buoyancy99/research-template). By its license, | |
| you must keep the above sentence in `README.md` and the `LICENSE` file to | |
| credit the author. | |