HappyP4nda commited on
Commit
d8caf2d
·
verified ·
1 Parent(s): 95e0e65

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +53 -0
README.md CHANGED
@@ -1,3 +1,56 @@
1
  ---
2
  license: apache-2.0
 
 
 
 
 
 
 
 
3
  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
  license: apache-2.0
3
+ library_name: diffusers
4
+ pipeline_tag: image-to-video
5
+ base_model: Wan-AI/Wan2.2-TI2V-5B-Diffusers
6
+ tags:
7
+ - video-generation
8
+ - reinforcement-learning
9
+ - physics
10
+ - diffusion
11
  ---
12
+
13
+ # PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models
14
+
15
+ [![arXiv](https://img.shields.io/badge/arXiv-PhysRVG-red)](https://arxiv.org/abs/2601.11087)
16
+ [![Project Page](https://img.shields.io/badge/Project_Page-PhysRVG-blue)](https://lucaria-academy.github.io/PhysRVG/)
17
+ [![GitHub](https://img.shields.io/badge/Code-GitHub-black?logo=github)](https://github.com/ant-research/PhysRVG)
18
+
19
+ 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.
20
+
21
+ > 📌 Demos, training, and inference code are in the [**GitHub repository**](https://github.com/ant-research/PhysRVG). This page only provides the checkpoints.
22
+
23
+ ## Contents
24
+
25
+ ```
26
+ PhysRVG/
27
+ ├── dit # PhysRVG DiT weights (used with --resume_from_checkpoint)
28
+ ├── lora # LoRA weights for memory-efficient fine-tuning / inference
29
+ ├── sam2.1-hiera-large # SAM 2 model used to compute the verifiable reward
30
+ └── Wan2.2-TI2V-5B-Diffusers # base text/image-to-video diffusion model
31
+ ```
32
+
33
+ ## Usage
34
+
35
+ Download the weights into the `./models` directory of the [code repository](https://github.com/ant-research/PhysRVG):
36
+
37
+ ```bash
38
+ huggingface-cli download HappyP4nda/PhysRVG --local-dir ./models
39
+ ```
40
+
41
+ Then run inference (see the GitHub README for setup):
42
+
43
+ ```bash
44
+ python inference.py --video_path data/example_videos/2/video.mp4
45
+ ```
46
+
47
+ ## Citation
48
+
49
+ ```bibtex
50
+ @article{PhysRVG2026,
51
+ title={PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models},
52
+ 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},
53
+ journal={ECCV 2026},
54
+ year={2026}
55
+ }
56
+ ```