ID-Animator: Zero-Shot Identity-Preserving Human Video Generation

• URL: http://arxiv.org/abs/2404.15275v3
• Date: Tue, 25 Jun 2024 16:57:27 GMT
• Title: ID-Animator: Zero-Shot Identity-Preserving Human Video Generation
• Authors: Xuanhua He, Quande Liu, Shengju Qian, Xin Wang, Tao Hu, Ke Cao, Keyu Yan, Jie Zhang,
• Abstract summary: ID-Animator is a zero-shot human-video generation approach that can perform personalized video generation given a single reference facial image without further training. Our method is highly compatible with popular pre-trained T2V models like animatediff and various community backbone models.
• Score: 16.438935466843304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating high-fidelity human video with specified identities has attracted significant attention in the content generation community. However, existing techniques struggle to strike a balance between training efficiency and identity preservation, either requiring tedious case-by-case fine-tuning or usually missing identity details in the video generation process. In this study, we present \textbf{ID-Animator}, a zero-shot human-video generation approach that can perform personalized video generation given a single reference facial image without further training. ID-Animator inherits existing diffusion-based video generation backbones with a face adapter to encode the ID-relevant embeddings from learnable facial latent queries. To facilitate the extraction of identity information in video generation, we introduce an ID-oriented dataset construction pipeline that incorporates unified human attributes and action captioning techniques from a constructed facial image pool. Based on this pipeline, a random reference training strategy is further devised to precisely capture the ID-relevant embeddings with an ID-preserving loss, thus improving the fidelity and generalization capacity of our model for ID-specific video generation. Extensive experiments demonstrate the superiority of ID-Animator to generate personalized human videos over previous models. Moreover, our method is highly compatible with popular pre-trained T2V models like animatediff and various community backbone models, showing high extendability in real-world applications for video generation where identity preservation is highly desired. Our codes and checkpoints are released at https://github.com/ID-Animator/ID-Animator.

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