CoDance: An Unbind-Rebind Paradigm for Robust Multi-Subject Animation
Character image animation is gaining significant importance across various domains, driven by the demand for robust and flexible multi-subject rendering. While existing methods excel in single-person animation, they struggle to handle arbitrary subject counts, diverse character types, and spatial misalignment between the reference image and the driving poses. We attribute these limitations to an overly rigid spatial binding that forces strict pixel-wise alignment between the pose and reference, and an inability to consistently rebind motion to intended subjects. To address these challenges, we propose CoDance, a novel Unbind-Rebind framework that enables the animation of arbitrary subject counts, types, and spatial configurations conditioned on a single, potentially misaligned pose sequence. Specifically, the Unbind module employs a novel pose shift encoder to break the rigid spatial binding between the pose and the reference by introducing stochastic perturbations to both poses and their latent features, thereby compelling the model to learn a location-agnostic motion representation. To ensure precise control and subject association, we then devise a Rebind module, leveraging semantic guidance from text prompts and spatial guidance from subject masks to direct the learned motion to intended characters. Furthermore, to facilitate comprehensive evaluation, we introduce a new multi-subject CoDanceBench. Extensive experiments on CoDanceBench and existing datasets show that CoDance achieves SOTA performance, exhibiting remarkable generalization across diverse subjects and spatial layouts. The code and weights will be open-sourced.
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Shuai Tan
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2026-01-23T09:39:02.018Z
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Shuai Tan
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Shuaishuai0219
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Biao Gong
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Ke Ma
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2026-01-20T09:44:22.904Z
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Ke Ma
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Yutong Feng
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2026-01-20T09:44:15.812Z
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Yutong Feng
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fengyutong
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Qiyuan Zhang
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2026-01-20T09:44:09.177Z
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Qiyuan Zhang
DonJoey
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DonJoey
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Yan Wang
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Yujun Shen
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2026-01-20T09:43:55.399Z
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Yujun Shen
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shen12313
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Hengshuang Zhao
admin_assigned
2026-01-20T09:43:49.910Z
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Hengshuang Zhao
Hengshuang
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Hengshuang
CoDance: An Unbind-Rebind Paradigm for Robust Multi-Subject Animation
Character image animation is gaining significant importance across various domains, driven by the demand for robust and flexible multi-subject rendering. While existing methods excel in single-person animation, they struggle to handle arbitrary subject counts, diverse character types, and spatial misalignment between the reference image and the driving poses. We attribute these limitations to an overly rigid spatial binding that forces strict pixel-wise alignment between the pose and reference, and an inability to consistently rebind motion to intended subjects. To address these challenges, we propose CoDance, a novel Unbind-Rebind framework that enables the animation of arbitrary subject counts, types, and spatial configurations conditioned on a single, potentially misaligned pose sequence. Specifically, the Unbind module employs a novel pose shift encoder to break the rigid spatial binding between the pose and the reference by introducing stochastic perturbations to both poses and their latent features, thereby compelling the model to learn a location-agnostic motion representation. To ensure precise control and subject association, we then devise a Rebind module, leveraging semantic guidance from text prompts and spatial guidance from subject masks to direct the learned motion to intended characters. Furthermore, to facilitate comprehensive evaluation, we introduce a new multi-subject CoDanceBench. Extensive experiments on CoDanceBench and existing datasets show that CoDance achieves SOTA performance, exhibiting remarkable generalization across diverse subjects and spatial layouts. The code and weights will be open-sourced.
CoDance introduces an Unbind-Rebind framework for animating multiple subjects with flexible spatial configurations, using pose shift encoding and semantic/textual guidance for motion reassignment.
Unbind-Rebind framework pose shift encoder stochastic perturbations location-agnostic motion representation semantic guidance spatial guidance subject masks CoDanceBench