Summary of Stableanimator: High-quality Identity-preserving Human Image Animation, by Shuyuan Tu et al.
StableAnimator: High-Quality Identity-Preserving Human Image Animation
by Shuyuan Tu, Zhen Xing, Xintong Han, Zhi-Qi Cheng, Qi Dai, Chong Luo, Zuxuan Wu
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel video diffusion framework, called StableAnimator, is introduced to synthesize high-quality videos while preserving identity consistency. This end-to-end model conditions animations on a reference image and a sequence of poses without requiring post-processing. The framework consists of carefully designed modules for training and inference that strive for ID consistency. A Face Encoder refines face embeddings by interacting with image embeddings, and a novel distribution-aware ID Adapter prevents interference caused by temporal layers while preserving ID via alignment. During inference, an optimization based on the Hamilton-Jacobi-Bellman (HJB) equation is proposed to enhance face quality. The HJB equation can be integrated into the diffusion denoising process, constraining the denoising path and benefiting ID preservation. Experimental results demonstrate the effectiveness of StableAnimator both qualitatively and quantitatively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to create videos that look like real people is introduced. This method makes sure that the identity (who someone is) stays the same throughout the video. The method, called StableAnimator, uses a special kind of computer model to create the video. It takes a reference image and a sequence of poses as input and produces a high-quality video without needing any extra processing. The model has special parts that help keep the identity consistent, like a “face encoder” that refines how the face looks. The result is a more realistic and engaging video. |
Keywords
» Artificial intelligence » Alignment » Diffusion » Encoder » Inference » Optimization
