StableAnimator: High-Quality Identity-Preserving Human Image Animation
Shuyuan Tu, Zhen Xing, Xintong Han, Zhi-Qi Cheng, Qi Dai, Chong Luo,, Zuxuan Wu

TL;DR
StableAnimator is an innovative end-to-end video diffusion framework that ensures high-quality, identity-preserving human image animation by integrating novel modules and optimization techniques, significantly improving ID consistency in generated videos.
Contribution
It introduces a novel ID-preserving video diffusion framework with a distribution-aware ID Adapter and HJB-based optimization for enhanced identity consistency.
Findings
Effective identity preservation demonstrated on multiple benchmarks.
High-quality video synthesis without post-processing.
Quantitative and qualitative improvements over existing methods.
Abstract
Current diffusion models for human image animation struggle to ensure identity (ID) consistency. This paper presents StableAnimator, the first end-to-end ID-preserving video diffusion framework, which synthesizes high-quality videos without any post-processing, conditioned on a reference image and a sequence of poses. Building upon a video diffusion model, StableAnimator contains carefully designed modules for both training and inference striving for identity consistency. In particular, StableAnimator begins by computing image and face embeddings with off-the-shelf extractors, respectively and face embeddings are further refined by interacting with image embeddings using a global content-aware Face Encoder. Then, StableAnimator introduces a novel distribution-aware ID Adapter that prevents interference caused by temporal layers while preserving ID via alignment. During inference, we…
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Taxonomy
TopicsHuman Motion and Animation · Human Pose and Action Recognition · Generative Adversarial Networks and Image Synthesis
MethodsAdapter · Diffusion
