Update README.md

README.md

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+# MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Model.
+
+[Muyao Niu](https://myniuuu.github.io/), 
+[Xiaodong Cun](https://vinthony.github.io/academic/), 
+[Xintao Wang](https://xinntao.github.io/), 
+[Yong Zhang](https://yzhang2016.github.io/), 
+[Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en), 
+[Yinqiang Zheng](https://scholar.google.com/citations?user=JD-5DKcAAAAJ&hl=en)
+
+[![Project page](https://img.shields.io/badge/Project-Page-brightgreen)](https://myniuuu.github.io/MOFA_Video)
+
+
+## Introduction
+<p align="center">
+  <img src="assets/figures/project-mofa.png">
+</p>
+We introduce MOFA-Video, a method designed to adapt motions from different domains to the frozen Video Diffusion Model. By employing <u>sparse-to-dense (S2D) motion generation</u> and <u>flow-based motion adaptation</u>, MOFA-Video can effectively animate a single image using various types of control signals, including trajectories, keypoint sequences, AND their combinations.
+<p align="center">
+  <img src="assets/figures/pipeline.png">
+</p>
+During the training stage, we generate sparse control signals through sparse motion sampling and then train different MOFA-Adapters to generate video via pre-trained SVD. During the inference stage, different MOFA-Adapters can be combined to jointly control the frozen SVD.
+
 ---
-title: MOFA Demo
-emoji: 🐠
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 4.32.2
-app_file: app.py
-pinned: false
----
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-# MOFA-Video
-Official Pytorch implementation for MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Model.
+please check the gallery of our [project page](https://myniuuu.github.io/MOFA_Video) for many visual results!
+
+## 📰 **TODO**
+- [ ] Gradio demo and checkpoints for trajectory-based image animation (By this weekend)
+- [ ] Inference scripts and checkpoints for keypoint-based facial image animation
+- [ ] inference Gradio demo for hybrid image animation
+- [ ] Training codes 
+
+
+# Acknowledgements
+We appreciate the Gradio code of [DragNUWA](https://arxiv.org/abs/2308.08089).