End-to-end recipe for generating the RBench robotics video-generation benchmark
with Cosmos3-Super using the native Cosmos Framework PyTorch entrypoint
(python -m cosmos_framework.scripts.inference).
RBench is an Image-to-Video (I2V) benchmark of 650 cases across 9 categories
(common_manipulation, dual_arm, humanoid, long-horizon_planning,
multi-entity_collaboration, quad, single_arm, spatial_relationship,
visual_reasoning). Each case conditions on a single image and generates a clip.
- Image-to-Video (I2V): condition on the per-case image
(
imgs/<image_path>); generate 121 frames (1 conditioning + 120 generated).
Generation is at 24 FPS, 720p, 16:9, and the raw output is kept (no staging).
run_with_cosmos_framework.ipynb— main notebook (demo case + full-sweep cell).assets/prompts/*.json— 9 category files, 650 entries total, each withjson_upsampled_promptandnegative_prompt.
The condition images come from the Hugging Face dataset
DAGroup-PKU/RBench,
cloned via git clone (Git LFS, ~22 GB). Only the condition images are read
from the dataset; the prompts come from the local assets/prompts/ files.
| Setting | Value |
|---|---|
| num_frames | 121 |
| fps | 24 |
| resolution | 720 |
| num_steps | 50 |
| guidance | 6.0 |
| shift | 10.0 |
| seed | 0 |
- 4-GPU Linux node (configurable via
COSMOS3_NUM_GPUS, default 4) uv >= 0.11.3git,git-lfs- Hugging Face access to the Cosmos3 model family