CoDance：一種用於魯棒多主體動畫的解綁-重綁範式

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

人物影像動畫技術在多領域的重要性日益凸顯，這源於對強健且靈活的多主體渲染需求的增長。現有方法雖在單人動畫表現出色，卻難以處理任意主體數量、多樣角色類型，以及參考圖像與驅動姿態間的空間錯位問題。我們認為這些侷限性源於過度僵化的空間綁定機制（強制要求姿態與參考圖像嚴格像素級對齊）以及無法穩定地將運動重新關聯至目標主體。為解決這些挑戰，我們提出CoDance——創新的「解綁-重綁」框架，能基於單一潛在錯位的姿態序列，實現任意主體數量、類型及空間配置的動畫生成。具體而言，解綁模塊採用新型姿態偏移編碼器，通過對姿態及其潛在特徵引入隨機擾動，打破姿態與參考圖像間的剛性空間綁定，從而迫使模型學習位置無關的運動表徵。為確保精準控制與主體關聯，我們設計重綁模塊，利用文字提示的語義引導與主體遮罩的空間指引，將學習到的運動定向映射至目標角色。此外，為實現全面評估，我們建立新的多主體基準數據集CoDanceBench。在CoDanceBench與現有數據集上的大量實驗表明，CoDance實現了頂尖性能，並在多樣主體與空間佈局中展現出卓越的泛化能力。程式碼與權重將開源釋出。

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.