MultiAnimate: Pose-Guided Image Animation Made Extensible

• URL: http://arxiv.org/abs/2602.21581v1
• Date: Wed, 25 Feb 2026 05:06:58 GMT
• Title: MultiAnimate: Pose-Guided Image Animation Made Extensible
• Authors: Yingcheng Hu, Haowen Gong, Chuanguang Yang, Zhulin An, Yongjun Xu, Songhua Liu,
• Abstract summary: A Pose-guided human image animation aims to synthesize realistic videos of a reference character driven by a sequence of poses.<n>We propose a multi-character image animation framework built upon modern Diffusion Transformers for video generation.<n>We show that our framework achieves state-of-the-art performance in multi-character image animation, surpassing existing diffusion-based baselines.
• Score: 44.163219649465866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pose-guided human image animation aims to synthesize realistic videos of a reference character driven by a sequence of poses. While diffusion-based methods have achieved remarkable success, most existing approaches are limited to single-character animation. We observe that naively extending these methods to multi-character scenarios often leads to identity confusion and implausible occlusions between characters. To address these challenges, in this paper, we propose an extensible multi-character image animation framework built upon modern Diffusion Transformers (DiTs) for video generation. At its core, our framework introduces two novel components-Identifier Assigner and Identifier Adapter - which collaboratively capture per-person positional cues and inter-person spatial relationships. This mask-driven scheme, along with a scalable training strategy, not only enhances flexibility but also enables generalization to scenarios with more characters than those seen during training. Remarkably, trained on only a two-character dataset, our model generalizes to multi-character animation while maintaining compatibility with single-character cases. Extensive experiments demonstrate that our approach achieves state-of-the-art performance in multi-character image animation, surpassing existing diffusion-based baselines.

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