MotionWeaver: Holistic 4D-Anchored Framework for Multi-Humanoid Image Animation

• URL: http://arxiv.org/abs/2602.13326v1
• Date: Wed, 11 Feb 2026 03:03:44 GMT
• Title: MotionWeaver: Holistic 4D-Anchored Framework for Multi-Humanoid Image Animation
• Authors: Xirui Hu, Yanbo Ding, Jiahao Wang, Tingting Shi, Yali Wang, Guo Zhi Zhi, Weizhan Zhang,
• Abstract summary: MotionWeaver is an end-to-end framework for multi-humanoid image animation.<n>We introduce unified motion representations that extract identity-agnostic motions and explicitly bind them to corresponding characters.<n>We also propose a holistic 4D-anchored paradigm that constructs a shared 4D space to fuse motion representations with video latents.
• Score: 22.502601281241724
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Character image animation, which synthesizes videos of reference characters driven by pose sequences, has advanced rapidly but remains largely limited to single-human settings. Existing methods struggle to generalize to multi-humanoid scenarios, which involve diverse humanoid forms, complex interactions, and frequent occlusions. We address this gap with two key innovations. First, we introduce unified motion representations that extract identity-agnostic motions and explicitly bind them to corresponding characters, enabling generalization across diverse humanoid forms and seamless extension to multi-humanoid scenarios. Second, we propose a holistic 4D-anchored paradigm that constructs a shared 4D space to fuse motion representations with video latents, and further reinforces this process with hierarchical 4D-level supervision to better handle interactions and occlusions. We instantiate these ideas in MotionWeaver, an end-to-end framework for multi-humanoid image animation. To support this setting, we curate a 46-hour dataset of multi-human videos with rich interactions, and construct a 300-video benchmark featuring paired humanoid characters. Quantitative and qualitative experiments demonstrate that MotionWeaver not only achieves state-of-the-art results on our benchmark but also generalizes effectively across diverse humanoid forms, complex interactions, and challenging multi-humanoid scenarios.

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