# Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation

**Authors:** Li Hu, Xin Gao, Peng Zhang, Ke Sun, Bang Zhang, Liefeng Bo  
**arXiv:** [2311.17117](https://arxiv.org/abs/2311.17117)  
**Date:** 2023-11-28  
**Summary page:** https://www.summarizepaper.com/en/paper/2311.17117v1/

## Key points

- The paper addresses challenges in generating character videos from still images through driving signals
- Diffusion models have robust generative capabilities but struggle with consistency and detailed information in image-to-video synthesis for character animation
- The authors propose a framework that leverages diffusion models and introduces ReferenceNet to preserve intricate appearance features from the reference image
- An efficient pose guider is introduced to direct the character's movements, ensuring controllability and continuity
- An effective temporal modeling approach is employed for smooth inter-frame transitions between video frames
- The proposed method demonstrates superior performance compared to other image-to-video methods in character animation
- It achieves state-of-the-art results on benchmarks for fashion video and human dance synthesis
- The framework enables animating arbitrary characters by expanding training data, enhancing its performance compared to existing methods.

## Summary

The paper titled "Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation" addresses the challenges in generating character videos from still images through driving signals. While diffusion models have gained popularity in visual generation research due to their robust generative capabilities, maintaining consistency with detailed information from the character remains a significant problem in image-to-video synthesis, particularly in character animation. To tackle these challenges, the authors propose a novel framework that leverages the power of diffusion models. They introduce ReferenceNet, a design that merges detail features via spatial attention to preserve the consistency of intricate appearance features from the reference image. Additionally, an efficient pose guider is introduced to direct the character's movements, ensuring controllability and continuity. To ensure smooth inter-frame transitions between video frames, an effective temporal modeling approach is employed. One notable aspect of this approach is its ability to animate arbitrary characters by expanding the training data. The results obtained using this method demonstrate superior performance compared to other image-to-video methods in character animation. Furthermore, the proposed method is evaluated on benchmarks for fashion video and human dance synthesis, achieving state-of-the-art results. Overall, this paper presents a comprehensive solution for consistent and controllable image-to-video synthesis in character animation. By leveraging diffusion models and incorporating innovative techniques such as ReferenceNet and pose guiding, the proposed framework demonstrates impressive results in preserving appearance details and ensuring smooth transitions between frames. It also enables animating arbitrary characters by expanding training data which further enhances its performance compared to existing methods for character animation synthesis.