arXiv Computer Vision
Spatiotemporally Decoupled Autoregressive Diffusion Model for Human Motion Generation
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ARDY: Autoregressive Diffusion with Hybrid Representation for Interactive Human Motion Generation
arXiv:2607. 08741v1 Announce Type: cross Abstract: Generating realistic 3D human motions in real-time within interactive applications is key for animation, simulation, and humanoid robotics.
arXiv Computer Vision
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PRISM: Streaming Human Motion Generation with Per-Joint Latent Decomposition
arXiv:2603.08590v4 Announce Type: replace Abstract: Text-to-motion generation has advanced with larger corpora and stronger generators, yet many models still rely on holistic frame- or clip-level lat...
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From Diffusion to Flow: Efficient Motion Generation in MotionGPT3
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Spatial Temporal Synergy: Balancing Change and Invariance in Text Driven 3D Human Motion Editing
Text-driven human motion editing aims to modify existing motion sequences according to natural language instructions while maintaining the structural consistency of the original motion. Existing diffusion-based approaches struggle to balance text-responsive "change" and inertial "invariance".
arXiv Machine Learning
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MUGEN: A Unified Framework for Efficient Motion Understanding and Generation
arXiv:2607. 27581v1 Announce Type: new Abstract: Grounding human motion in language, and language in motion, is a central step toward physical AI systems that can understand, generate, and communicate human behavior.
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A Scalable Whole-body Motion Transfer via Implicit Kinodynamic Motion Retargeting
arXiv:2509. 15443v2 Announce Type: replace-cross Abstract: Human-to-humanoid imitation learning presents a promising pathway to address the severe data scarcity bottleneck in robotics by utilizing abundant, large-scale human motion collections.