EverybodyDance: Bipartite Graph-Based Identity Correspondence for Multi-Character Animation
–Neural Information Processing Systems
Consistent pose-driven character animation has achieved remarkable progress in single-character scenarios. However, extending these advances to multi-character settings is non-trivial, especially when position swap is involved. Beyond mere scaling, the core challenge lies in enforcing correct Identity Correspondence (IC) between characters in reference and generated frames. To address this, we introduce EverybodyDance, a systematic solution targeting IC correctness in multi-character animation. EverybodyDance is built around the Identity Matching Graph (IMG), which models characters in the generated and reference frames as two node sets in a weighted complete bipartite graph.
Neural Information Processing Systems
Jun-15-2026, 01:08:20 GMT
- Genre:
- Research Report > Experimental Study (1.00)
- Industry:
- Information Technology (0.46)
- Technology:
- Information Technology
- Graphics > Animation (1.00)
- Artificial Intelligence
- Vision (1.00)
- Machine Learning > Neural Networks (0.93)
- Information Technology