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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.15415 |
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| _version_ | 1866908889654493184 |
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| author | Xie, Zhenyu Xia, Ji Kampffmeyer, Michael Hu, Panwen Ma, Zehua Zheng, Yujian Wang, Jing Chong, Zheng Zhang, Xujie Cheng, Xianhang Liang, Xiaodan Li, Hao |
| author_facet | Xie, Zhenyu Xia, Ji Kampffmeyer, Michael Hu, Panwen Ma, Zehua Zheng, Yujian Wang, Jing Chong, Zheng Zhang, Xujie Cheng, Xianhang Liang, Xiaodan Li, Hao |
| contents | Controllable character animation has advanced rapidly in recent years, yet multi-character animation remains underexplored. As the number of characters grows, multi-character reference encoding becomes more susceptible to latent identity entanglement, resulting in identity bleeding and reduced controllability. Moreover, learning precise and spatio-temporally consistent correspondences between reference identities and driving pose sequences becomes increasingly challenging, often leading to identity-pose mis-binding and inconsistency in generated videos. To address these challenges, we propose AnyCrowd, a Diffusion Transformer (DiT)-based video generation framework capable of scaling to an arbitrary number of characters. Specifically, we first introduce an Instance-Isolated Latent Representation (IILR), which encodes character instances independently prior to DiT processing to prevent latent identity entanglement. Building on this disentangled representation, we further propose Tri-Stage Decoupled Attention (TSDA) to bind identities to driving poses by decomposing self-attention into: (i) instance-aware foreground attention, (ii) background-centric interaction, and (iii) global foreground-background coordination. Furthermore, to mitigate token ambiguity in overlapping regions, an Adaptive Gated Fusion (AGF) module is integrated within TSDA to predict identity-aware weights, effectively fusing competing token groups into identity-consistent representations... |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15415 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AnyCrowd: Instance-Isolated Identity-Pose Binding for Arbitrary Multi-Character Animation Xie, Zhenyu Xia, Ji Kampffmeyer, Michael Hu, Panwen Ma, Zehua Zheng, Yujian Wang, Jing Chong, Zheng Zhang, Xujie Cheng, Xianhang Liang, Xiaodan Li, Hao Computer Vision and Pattern Recognition Controllable character animation has advanced rapidly in recent years, yet multi-character animation remains underexplored. As the number of characters grows, multi-character reference encoding becomes more susceptible to latent identity entanglement, resulting in identity bleeding and reduced controllability. Moreover, learning precise and spatio-temporally consistent correspondences between reference identities and driving pose sequences becomes increasingly challenging, often leading to identity-pose mis-binding and inconsistency in generated videos. To address these challenges, we propose AnyCrowd, a Diffusion Transformer (DiT)-based video generation framework capable of scaling to an arbitrary number of characters. Specifically, we first introduce an Instance-Isolated Latent Representation (IILR), which encodes character instances independently prior to DiT processing to prevent latent identity entanglement. Building on this disentangled representation, we further propose Tri-Stage Decoupled Attention (TSDA) to bind identities to driving poses by decomposing self-attention into: (i) instance-aware foreground attention, (ii) background-centric interaction, and (iii) global foreground-background coordination. Furthermore, to mitigate token ambiguity in overlapping regions, an Adaptive Gated Fusion (AGF) module is integrated within TSDA to predict identity-aware weights, effectively fusing competing token groups into identity-consistent representations... |
| title | AnyCrowd: Instance-Isolated Identity-Pose Binding for Arbitrary Multi-Character Animation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.15415 |