arXiv Computer Vision
Gaussian-Mixture Latent Flow for Stochastic 3D Human Motion Prediction
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EquiFusion: Kinematics-Agnostic Human Motion Prediction via Equivariant Latent Diffusion
arXiv:2607. 10984v1 Announce Type: cross Abstract: Existing Stochastic 3D Human Motion Prediction models are fundamentally constrained by hard-coding the skeleton kinematics, severely limiting generalization, preventing cross-dataset training, and requiring complex data retargeting.
arXiv Computer Vision
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Spatiotemporally Decoupled Autoregressive Diffusion Model for Human Motion Generation
arXiv:2608.23279v1 Announce Type: new Abstract: Text-driven human motion synthesis has made substantial development with two core modules of motion representation and generative architecture. For rep...
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Uni-HOI:A Unified framework for Learning the Joint distribution of Text and Human-Object Interaction
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Residual Flow Matching with Dynamic Cross-Interaction for 3D Multi-Person Motion Prediction
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From Diffusion to Flow: Efficient Motion Generation in MotionGPT3
The paper compares diffusion and rectified flow objectives within the MotionGPT3 framework for text-driven motion generation. Experiments on HumanML3D show that rectified flow converges faster, achieves strong test performance earlier, and matches or exceeds diffusion quality while requiring fewer sampling steps. The study isolates the generative objective’s impact, demonstrating that rectified flow’s benefits transfer to continuous-latent motion generation.
arXiv Machine Learning
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Where Will They Go? Modelling Multimodal Pedestrian Manoeuvres from Ego-centric Videos
arXiv:2606. 18824v1 Announce Type: cross Abstract: Pedestrian trajectory prediction from an ego-centric camera is challenging since it depends on complex interactions with vehicles and scene context, as well as the intention of the pedestrian.