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This paper introduces MultiAnimate, a unified framework designed for the concurrent animation of multiple characters while maintaining identity consistency and spatial relationships. By integrating an identity-specific reference net, an identity-aware pose encoder, and an interaction guider module, MultiAnimate effectively addresses the limitations of existing single-reference character animation methods. Experimental results show that MultiAnimate outperforms previous approaches, particularly in scenarios with complex inter-character interactions and motion sequences.
MultiAnimate achieves unprecedented control over multi-character animations, outperforming traditional methods that rely on single reference images.
Recent advances in generative models and technological innovations have significantly addressed the fundamental challenges of character image animation. However, existing approaches predominantly focus on character animation from a single reference image, substantially limiting their applicability in scenarios such as multiple character interaction animation. To fill this gap, this paper introduces MultiAnimate, a comprehensive framework that enables concurrent animation of multiple characters within a shared environment while preserving both identity consistency and spatial relationships. The framework achieves these objectives through multiple well-designed mechanisms. First, we incorporate an identity-specific reference net that enables appearance extraction from multiple reference images, distinguishing MultiAnimate from existing approaches constrained to single reference inputs. Second, we implement an identity-aware pose encoder to address the character-pose binding challenge, wherein an attention mechanism enables the network to accurately differentiate and process multiple pose sequences during generation. Third, we introduce an interaction guider module that enhances the framework's capability to handle complex inter-character interactions by leveraging character-specific mask information, serving as an optional component that refines the pose sequences. Extensive experiments and ablation analyses demonstrate our framework's superiority in multiple character animation, particularly in scenarios involving complex motion sequences.
