metadata
license: apache-2.0
library_name: diffusers
pipeline_tag: image-to-video
base_model: Wan-AI/Wan2.2-TI2V-5B-Diffusers
tags:
- video-generation
- reinforcement-learning
- physics
- diffusion
PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models
This repository hosts the model weights for PhysRVG (ECCV 2026). PhysRVG leverages a unified reinforcement learning framework with verifiable rewards to improve rigid-body motion generation in video synthesis.
📌 Demos, training, and inference code are in the GitHub repository. This page only provides the checkpoints.
Contents
PhysRVG/
├── dit # PhysRVG DiT weights (used with --resume_from_checkpoint)
├── lora # LoRA weights for memory-efficient fine-tuning / inference
├── sam2.1-hiera-large # SAM 2 model used to compute the verifiable reward
└── Wan2.2-TI2V-5B-Diffusers # base text/image-to-video diffusion model
Usage
Download the weights into the ./models directory of the code repository:
huggingface-cli download HappyP4nda/PhysRVG --local-dir ./models
Then run inference (see the GitHub README for setup):
python inference.py --video_path data/example_videos/2/video.mp4
Citation
@article{PhysRVG2026,
title={PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models},
author={Zhang, Qiyuan and Gong, Biao and Tan, Shuai and Zhang, Zheng and Shen, Yujun and Zhu, Xing and Li, Yuyuan and Yao, Kelu and Shen, Chunhua and Zou, Changqing},
journal={ECCV 2026},
year={2026}
}