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---
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
[![arXiv](https://img.shields.io/badge/arXiv-PhysRVG-red)](https://arxiv.org/abs/2601.11087)
[![Project Page](https://img.shields.io/badge/Project_Page-PhysRVG-blue)](https://lucaria-academy.github.io/PhysRVG/)
[![GitHub](https://img.shields.io/badge/Code-GitHub-black?logo=github)](https://github.com/ant-research/PhysRVG)
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**](https://github.com/ant-research/PhysRVG). 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](https://github.com/ant-research/PhysRVG):
```bash
huggingface-cli download HappyP4nda/PhysRVG --local-dir ./models
```
Then run inference (see the GitHub README for setup):
```bash
python inference.py --video_path data/example_videos/2/video.mp4
```
## Citation
```bibtex
@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}
}
```