Improve model card for AniCrafter

This PR significantly enhances the model card for AniCrafter by adding the `library_name: diffusers` and relevant `tags` to the metadata, ensuring proper discoverability and categorization on the Hub.

It also comprehensively updates the content, including:
- Direct links to the paper, project page, and GitHub repository.
- A detailed introduction based on the paper abstract.
- Visual demonstrations (demo videos/gifs).
- Information on new features and code release status.
- Step-by-step environment setup and detailed usage examples for inference.
- The official citation and project acknowledgements.

These improvements will make the model more accessible, understandable, and user-friendly for the community.

README.md

@@ -1,15 +1,174 @@
 ---
-license: apache-2.0
 base_model:
 - Wan-AI/Wan2.1-I2V-14B-720P
+license: apache-2.0
 pipeline_tag: image-to-video
+library_name: diffusers
+tags:
+- human-animation
+- 3d-gaussian-splatting
+- smplx
 ---
 
-## Paper
+# AniCrafter: Customizing Realistic Human-Centric Animation via Avatar-Background Conditioning in Video Diffusion Models
 
-[https://arxiv.org/abs/2505.20255](https://arxiv.org/abs/2505.20255)
+[📄 Paper](https://huggingface.co/papers/2505.20255) | [🏠 Project Page](https://myniuuu.github.io/AniCrafter) | [💻 Code](https://github.com/myniuuu/AniCrafter)
 
+Recent advances in video diffusion models have significantly improved character animation techniques. However, current approaches rely on basic structural conditions such as DWPose or SMPL-X to animate character images, limiting their effectiveness in open-domain scenarios with dynamic backgrounds or challenging human poses. In this paper, we introduce **AniCrafter**, a diffusion-based human-centric animation model that can seamlessly integrate and animate a given character into open-domain dynamic backgrounds while following given human motion sequences. Built on cutting-edge Image-to-Video (I2V) diffusion architectures, our model incorporates an innovative ''avatar-background'' conditioning mechanism that reframes open-domain human-centric animation as a restoration task, enabling more stable and versatile animation outputs. Experimental results demonstrate the superior performance of our method.
 
-## Introduction
+## TL;DR
 
 We leverage **"3DGS Avatar + Background Video"** as guidance for the video diffusion model to **insert and animate anyone into any scene following given motion sequence**.
+
+<table align="center">
+  <tr>
+    <td align="center" width="13%">
+      <img src="https://github.com/myniuuu/AniCrafter/blob/main/assets/character_image/000000.jpg?raw=true"/>
+      <br />
+    </td>
+    <td align="center" width="29%">
+      <img src="https://github.com/myniuuu/AniCrafter/blob/main/assets/demo_videos/0.gif?raw=true"/>
+      <br />
+    </td>
+    <td align="center" width="29%">
+      <img src="https://github.com/myniuuu/AniCrafter/blob/main/assets/demo_videos/1.gif?raw=true"/>
+      <br />
+    </td>
+    <td align="center" width="29%">
+      <img src="https://github.com/myniuuu/AniCrafter/blob/main/assets/demo_videos/2.gif?raw=true"/>
+      <br />
+    </td>
+  </tr>
+</table>
+
+<table align="center">
+  <tr>
+    <td align="center" width="13%">
+      <img src="https://github.com/myniuuu/AniCrafter/blob/main/assets/character_image/000001.jpg?raw=true"/>
+      <br />
+    </td>
+    <td align="center" width="29%">
+      <img src="https://github.com/myniuuu/AniCrafter/blob/main/assets/demo_videos/3.gif?raw=true"/>
+      <br />
+    </td>
+    <td align="center" width="29%">
+      <img src="https://github.com/myniuuu/AniCrafter/blob/main/assets/demo_videos/4.gif?raw=true"/>
+      <br />
+    </td>
+    <td align="center" width="29%">
+      <img src="https://github.com/myniuuu/AniCrafter/blob/main/assets/demo_videos/5.gif?raw=true"/>
+      <br />
+    </td>
+  </tr>
+</table>
+
+## 🔥🔥🔥 New Features/Updates
+
+- (2024.07.03) We have released the cross-character inference script to replace the person in the source video!
+- (2025.07.02) Our [Project Page](https://myniuuu.github.io/AniCrafter) 🏠 is online!
+- (2025.07.01) We have released the model and inference script to insert and animate the character into the background video following SMPLX motion sequences! 
+- If you find this work interesting, please do not hesitate to give a ⭐!
+
+## 📰 CODE RELEASE
+
+- [x] (2024.07.01) Release model checkpoint and cross-character inference script.
+- [x] (2024.07.03) Release the complete cross-character inference script including data preprocessing (mask parsing + SMPLX estimation + background inpainting).
+- [ ] Release training codes.
+
+## ⚙️ Environment Setup
+
+### 🌍 Virtual Enviroment
+
+```bash
+conda create -n anicrafter python=3.10
+conda activate anicrafter
+bash install_cu124.sh
+```
+
+### 📦 Download Checkpoints
+
+```bash
+huggingface-cli download Wan-AI/Wan2.1-I2V-14B-720P --local-dir ./Wan2.1-I2V-14B-720P
+huggingface-cli download MyNiuuu/Anicrafter_release --local-dir ./Anicrafter_release
+mv ./Anicrafter_release/gfpgan ./gfpgan
+mv ./Anicrafter_release/pretrained_models ./pretrained_models
+```
+
+## 🏃 Cross-Character Inference from Background Video and Motions
+
+Run the following commands to insert and animate the character into the background video following SMPLX motion sequences. The pipeline consists of following key functions:
+- Reconstructing 3DGS Avatar from single image using [LHM](https://github.com/aigc3d/LHM)
+- Animating the 3DGS Avatar according to the SMPLX sequences to obtain the spatial aligned avatar renderings
+- Combine avatar rendering and background video to form the "Avatar + Background" condition
+- Run the diffusion model to obtain the final animation results 
+
+```bash
+python run_pipeline.py \
+--ckpt_path ./pretrained_models/anicrafter \
+--wan_base_ckpt_path ./Wan2.1-I2V-14B-720P \
+--character_image_path ./demo/character_images/000000.jpg \
+--scene_path ./demo/videos/scene_000000 \
+--save_root ./infer_result
+```
+
+## 🏃 Cross-Character Inference from in-the-wild Videos 
+Run the following commands to replace the person in the source video with our complete data preprocessing pipeline, which contains the following components:
+
+- Parsing human masks
+- Estimating SMPLX parameters and rendering SMPLX mesh videos
+- Background inpainting based on the human masks
+- Reconstructing 3DGS Avatar from single image using [LHM](https://github.com/aigc3d/LHM)
+- Animating the 3DGS Avatar according to the SMPLX sequences to obtain the spatial aligned avatar renderings
+- Combine avatar rendering and background video to form the "Avatar + Background" condition
+- Run the diffusion model to obtain the final animation results 
+
+### ⚙️ Additional Environment Setup
+
+```bash
+cd engine/pose_estimation
+pip install mmcv==1.3.9
+pip install -v -e third-party/ViTPose
+pip install ultralytics
+pip install av
+cd ../..
+pip install numpy==1.23.5
+
+
+mkdir weights
+cd weights
+wget https://github.com/sczhou/ProPainter/releases/download/v0.1.0/cutie-base-mega.pth
+wget https://github.com/sczhou/ProPainter/releases/download/v0.1.0/i3d_rgb_imagenet.pt
+wget https://github.com/sczhou/ProPainter/releases/download/v0.1.0/ProPainter.pth
+wget https://github.com/sczhou/ProPainter/releases/download/v0.1.0/raft-things.pth
+wget https://github.com/sczhou/ProPainter/releases/download/v0.1.0/recurrent_flow_completion.pth
+cd ..
+
+# or you can mannually download from https://github.com/sczhou/ProPainter/releases/tag/v0.1.0
+```
+
+### 💻 Start Inference
+
+```bash
+# Mask + SMPLX + Inpainting + Avatar Recon + Rendering + Diffusion
+# You could change the hyper-parameters of the inpainting algorithm to obtain optimal results
+
+python run_pipeline_with_preprocess.py \
+--video_root ./demo/origin_videos/raw_video \
+--ckpt_path ./pretrained_models/anicrafter \
+--wan_base_ckpt_path ./Wan2.1-I2V-14B-720P \
+--character_image_path ./demo/character_images/000000.jpg \
+--save_root ./infer_result_replace
+```
+
+## Citation
+```bibtex
+@article{niu2025anicrafter,
+  title={AniCrafter: Customizing Realistic Human-Centric Animation via Avatar-Background Conditioning in Video Diffusion Models},
+  author={Niu, Muyao and Cao, Mingdeng and Zhan, Yifan and Zhu, Qingtian and Ma, Mingze and Zhao, Jiancheng and Zeng, Yanhong and Zhong, Zhihang and Sun, Xiao and Zheng, Yinqiang},
+  journal={arXiv preprint arXiv:2505.20255},
+  year={2025}
+}
+```
+
+## Acknowledgements
+We sincerely appreciate the code release of the following projects: [LHM](https://github.com/aigc3d/LHM), [Unianimate-DiT](https://github.com/ali-vilab/UniAnimate-DiT), [Diffusers](https://github.com/huggingface/diffusers), and [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)