DisPose: Disentangling Pose Guidance for Controllable Human Image Animation

• URL: http://arxiv.org/abs/2412.09349v2
• Date: Fri, 13 Dec 2024 03:30:44 GMT
• Title: DisPose: Disentangling Pose Guidance for Controllable Human Image Animation
• Authors: Hongxiang Li, Yaowei Li, Yuhang Yang, Junjie Cao, Zhihong Zhu, Xuxin Cheng, Long Chen,
• Abstract summary: We present DisPose to mine more generalizable and effective control signals without additional dense input. DisPose disentangles the sparse skeleton pose in human image animation into motion field guidance and keypoint correspondence. To seamlessly integrate into existing models, we propose a plug-and-play hybrid ControlNet.
• Score: 13.366879755548636
• License:
• Abstract: Controllable human image animation aims to generate videos from reference images using driving videos. Due to the limited control signals provided by sparse guidance (e.g., skeleton pose), recent works have attempted to introduce additional dense conditions (e.g., depth map) to ensure motion alignment. However, such strict dense guidance impairs the quality of the generated video when the body shape of the reference character differs significantly from that of the driving video. In this paper, we present DisPose to mine more generalizable and effective control signals without additional dense input, which disentangles the sparse skeleton pose in human image animation into motion field guidance and keypoint correspondence. Specifically, we generate a dense motion field from a sparse motion field and the reference image, which provides region-level dense guidance while maintaining the generalization of the sparse pose control. We also extract diffusion features corresponding to pose keypoints from the reference image, and then these point features are transferred to the target pose to provide distinct identity information. To seamlessly integrate into existing models, we propose a plug-and-play hybrid ControlNet that improves the quality and consistency of generated videos while freezing the existing model parameters. Extensive qualitative and quantitative experiments demonstrate the superiority of DisPose compared to current methods. Code: \href{https://github.com/lihxxx/DisPose}{https://github.com/lihxxx/DisPose}.

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