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Human Motion Synthesis in 3D Scenes via Unified Scene Semantic Occupancy

arXiv — cs.CV•Wednesday, November 12, 2025 at 5:00:00 AM

The recent publication of the SSOMotion framework marks a significant advancement in human motion synthesis within 3D environments. By integrating semantic understanding with traditional scene structure analysis, SSOMotion offers a more comprehensive approach to motion synthesis. This framework employs a bi-directional tri-plane decomposition to create a compact version of Scene Semantic Occupancy (SSO), effectively mapping scene semantics into a unified feature space. Extensive experiments conducted on cluttered scenes using datasets such as ShapeNet furniture, PROX, and Replica have validated its effectiveness and generalization ability, showcasing its cutting-edge performance. The availability of the code at GitHub will facilitate further exploration and application of this innovative framework, potentially influencing future developments in AI and computer vision.

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