docs/usage.md

RememBench usage guide

Dataset overview

Download and load

pip install huggingface_hub
hf download evanbuzzZ/RememBench --repo-type dataset --local-dir RememBench
cd RememBench
pip install -r requirements.txt

from datasets import load_dataset

t2v = load_dataset("evanbuzzZ/RememBench", "t2v", split="test")
i2v = load_dataset("evanbuzzZ/RememBench", "i2v", split="test")

sample = t2v[0]
prompts = sample["prompts"]
seed = int(sample["seed"])

Seeds are decimal strings. Some T2V seeds exceed the exact-integer range of JavaScript numbers. Convert them directly to an integer in your generation code; do not pass them through a floating-point value.

Check the downloaded data and inspect one sample:

python scripts/validate.py
python scripts/load_example.py --subset i2v --index 0

For reproducible experiments, pin the Hugging Face repository commit with revision= or --revision.

T2V inputs

Each row of data/t2v/test.jsonl contains:

Field	Meaning
scene_id, source_index	Stable scene identifier and original candidate index
target_description	Target content described by the prompt
prompts	Four prompts: appears, disappears, remains out of sight, reappears
prompt_start_seconds	Nominal segment starts: [0.0, 3.6, 6.2, 11.2]
seed	Exact noise seed, stored as a decimal string
num_frames, fps, width, height	Paper setting: 379 frames, 24 fps, 1376 × 768
num_chunks, denoising_steps_per_chunk	Paper setting: 23 chunks, four denoising steps per chunk

The prompt schedule specifies intended action timing, not the exact generated action frames. The five-second third segment keeps the target out of sight longer than the largest sliding-window baseline's approximately three seconds of recent context. The paper retained 100 prompts from a 466-scene candidate pool after screening an H3-AR rollout for compliance. Evaluation uses the same retained prompts and seeds across methods and budgets.

I2V inputs and initial frames

Each row of data/i2v/test.jsonl contains:

Field	Meaning
sample_id, scene_id	Unique scene–trajectory identifier and source DL3DV scene ID
scene_type	indoor or outdoor
prompt	Caption describing visible scene content without prescribing camera motion
image_path	Relative path populated by the image preparation script
trajectory_path	Relative path to a bundled NumPy archive
setting, rotation_degrees, direction, translation	Camera-motion setting; direction is left or right
seed	Paper generation seed, "0"
num_frames, fps, width, height	Paper setting: 253 frames, 16 fps, 832 × 480
num_chunks, latent_frames, denoising_steps_per_chunk	16 chunks, 64 latent frames, four denoising steps per chunk
pose_samples, intrinsics_samples	Lengths of the original camera arrays

sources/i2v_images.jsonl contains one record per unique scene, with its upstream repository, archive path, retrieval revision, preprocessing rule, and SHA-256 of the expected RGB pixels. Initial frames are taken from the first image member in lexicographic archive-path order, center-cropped to the target aspect ratio, then resized to 832 × 480 using Lanczos.

Obtain access to DL3DV-ALL-960P under its own terms. If you already have the scene archives or extracted scene folders:

python scripts/prepare_i2v_images.py --source-dir /path/to/DL3DV-ALL-960P
python scripts/validate.py --require-images

Alternatively, retrieve the required first frames through your authorized Hugging Face account:

hf auth login
python scripts/prepare_i2v_images.py --download

The download option uses HTTP range requests to read the first image from each source archive, without downloading all of its video frames. Use --limit 1 to try one scene first. Existing prepared images are validated and skipped. The script refuses a pixel-hash mismatch, rather than silently substituting a different input.

Camera representation

Each .npz file has poses and intrinsics, copied without changing their numerical values from the final experiment inputs:

• poses: float32 camera-to-world matrices of shape (T, 4, 4). Camera axes are x right, y down, z forward. The source world frame is z up; the first camera is at [0, 0, 1.5], facing world +y. Translation values retain the source trajectory's depth-derived scale.
• intrinsics: float32 rows [fx, fy, cx, cy] in pixels at 832 × 480. Intrinsics are constant over each trajectory. Some source arrays have a different number of rows from poses; use the first intrinsics row, as in the paper's implementation.

The source pose sequence contains 256 or 257 samples, not the 253 decoded video frames. In the paper's LingBot-World-Infinity setup, rotation is interpolated with SLERP and translation linearly at 64 equally spaced positions over the full pose sequence, including both endpoints. The backbone then uses relative consecutive poses with translation normalized by the largest displacement, following its camera-conditioning implementation. Preserve the complete source sequence rather than truncating it to the decoded frame count.

For 90° and 180° rotation, the camera turns away and reverses. For 360°, it completes a continuous full turn. Translation moves into the scene and returns. All source trajectories end at their initial position and orientation.

Evaluation

The paper uses RAVEN-adapted MiniMax-H3 (H3-AR) for T2V and LingBot-World-Infinity for I2V. Compare methods using the same prompts and noise seed; I2V methods also share the conditioning frame and input trajectory. Retrieval methods retain two recent chunks and one or two chunks of far memory. Base retains additional recent chunks to match the total active visual KV budget, with the same backbone-specific attention sink.

T2V departure and revisit frames are manually annotated. The departure frame is shared across methods within a scene; the revisit is marked separately for each generated rollout. I2V uses the conditioning frame as departure and selects each rollout's revisit from its own Pi3X reconstruction. For rotation, find the frame whose viewing direction differs most from the initial direction; for translation, find the frame farthest from the initial position. From that turnaround onward, select the frame with the smallest unsigned viewing-direction angle to the initial frame. Reconstruct all decoded frames, with inputs resized to approximately 255,000 pixels and dimensions rounded to multiples of 14.

Metric	Definition	Aggregation
CLIP ↑	Cosine similarity of L2-normalized CLIP ViT-H/14 LAION-2B embeddings of departure and revisit	Median over scenes
LPIPS ↓	AlexNet, version 0.1, on native-resolution image pairs scaled to [-1, 1]	Median over scenes
TempSSIM ↑	Grayscale SSIM over consecutive frames, 11 × 11 Gaussian window, sigma 1.5	Mean within each video, then median over scenes
Drift ↓	Cosine distance between adjacent chunks, each represented by the normalized average of CLIP embeddings from four evenly spaced frames	Mean within each video, then median over scenes

H3-AR decodes an initial five-frame chunk followed by 17-frame chunks; LingBot-World-Infinity decodes an initial 13-frame chunk followed by 16-frame chunks. Scores use original rollouts before compression for the Video Viewer.

evaluation/t2v_reference_pairs.jsonl contains 600 annotations and CLIP/LPIPS measurements: 100 scenes × three methods × two comparison budgets. Frame indices are zero-based. These annotations describe the paper's specific generated rollouts; they are not reusable ground-truth frame indices for a new model or rerun. Original rollout videos are not included in this input package. source_arm identifies the original run, and comparison_budget_chunks identifies the matched-budget comparison; Base has no retrieved far memory.

evaluation/t2v_reference_summary.json reproduces the paper's median T2V CLIP and LPIPS values:

Method	CLIP, 1 chunk	LPIPS, 1 chunk	CLIP, 2 chunks	LPIPS, 2 chunks
Base	0.755	0.659	0.751	0.652
MoC	0.832	0.594	0.841	0.569
MosaiChunk	0.899	0.567	0.936	0.500

Provenance, limitations, and licensing

sources/input_provenance.json records hashes of the source manifests, prompts, trajectories, seeds, and annotations used for this export. checksums.sha256 covers the distributed release files.

Prompt compliance screening used one H3-AR rollout per candidate; success rates can differ with another model. T2V revisit annotation is manual, and I2V revisit selection depends on reconstructed poses. Report the evaluated cohort and any generation failures rather than silently changing the test set. The dataset measures revisits in the supplied scenarios and trajectories; it is not an exhaustive measure of video quality or world understanding.

Publication licensing for the RememBench-authored material is pending. DL3DV content remains subject to the DL3DV license and Terms of Use; this repository does not grant additional rights to it. The initial-frame pixels are obtained from the upstream source by the user and are not redistributed here. The four-stage T2V design extends the appear–disappear–reappear design of Ring Forcing; construction details and citations are in the linked paper.

Please cite the MosaiChunk paper when using RememBench and acknowledge DL3DV when using the I2V split. The paper is linked above.