CoDance: 一种面向鲁棒多主体动画的解绑-重绑新范式

CoDance: An Unbind-Rebind Paradigm for Robust Multi-Subject Animation

January 16, 2026

作者: Shuai Tan, Biao Gong, Ke Ma, Yutong Feng, Qiyuan Zhang, Yan Wang, Yujun Shen, Hengshuang Zhao

cs.AI

摘要

人物图像动画在各领域的重要性日益凸显，这源于对鲁棒且灵活的多主体渲染技术的需求。现有方法虽在单人动画方面表现优异，但难以处理任意主体数量、多样角色类型以及参考图像与驱动姿态间的空间错位问题。我们认为这些局限源于过于僵化的空间绑定机制——它强制要求姿态与参考图像严格像素对齐，且无法将运动准确重绑定至目标主体。为应对这些挑战，我们提出CoDance这一新型"解绑-重绑"框架，能够基于单组可能存在错位的姿态序列，对任意数量、类型及空间配置的主体进行动画生成。具体而言，解绑模块采用新型姿态偏移编码器，通过对姿态及其潜在特征引入随机扰动，打破姿态与参考图像间的刚性空间绑定，从而迫使模型学习位置无关的运动表征。为实现精准控制与主体关联，我们设计重绑模块，利用文本提示的语义引导和主体掩码的空间引导，将习得的运动定向至目标角色。此外，为支持全面评估，我们构建了新型多主体基准数据集CoDanceBench。在CoDanceBench和现有数据集上的大量实验表明，CoDance实现了最先进的性能，在不同主体和空间布局上展现出卓越的泛化能力。代码与权重将开源发布。

English

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.