pretty_name: RememBench
language:
- en
task_categories:
- text-to-video
- image-to-video
tags:
- arxiv:2610.02153
- benchmark
- video-generation
- long-horizon
- memory
size_categories:
- n<1K
configs:
- config_name: t2v
data_files:
- split: test
path: data/t2v/test.jsonl
- config_name: i2v
data_files:
- split: test
path: data/i2v/test.jsonl
RememBench
A benchmark for long-horizon revisit consistency in autoregressive video generation, introduced in MosaiChunk.
Project page · Paper · Code · Checkpoints · Video Viewer
Data
| Subset | Scenes | Inputs | Contents |
|---|---|---|---|
t2v |
100 | 100 | Four-segment prompts, timing, and seeds |
i2v |
150 | 750 | Initial-frame sources, scene prompts, camera trajectories, and seeds |
Both subsets use the test split. I2V contains 50 indoor and 100 outdoor scenes. All scenes have 90°, 180°, and 360° rotations; outdoor scenes also have rotation with translation.
DL3DV initial frames are retrieved using the provided preparation script and are not bundled here.
Usage
from datasets import load_dataset
t2v = load_dataset("evanbuzzZ/RememBench", "t2v", split="test")
i2v = load_dataset("evanbuzzZ/RememBench", "i2v", split="test")
Seeds are stored as strings; use int(sample["seed"]) when generating videos.
Download, image preparation, data formats, and evaluation
CLIP and LPIPS measure departure–revisit consistency. Included T2V frame annotations describe the paper's rollouts; new rollouts require their own revisit annotations.
License
The release license is pending. DL3DV images remain subject to the DL3DV Terms of Use.