5 papers
Learning Context-Adaptive Motion Priors for Masked Motion Diffusion Models with Efficient Kinematic Attention Aggregation
Junkun Jiang, Jie Chen, Ho Yin Au +1
Vision-based motion capture solutions often struggle with occlusions, which result in the loss of critical joint information and hinder accurate 3D motion reconstruction. Other wea…
SOSControl: Enhancing Human Motion Generation through Saliency-Aware Symbolic Orientation and Timing Control
Ho Yin Au, Junkun Jiang, Jie Chen
Traditional text-to-motion frameworks often lack precise control, and existing approaches based on joint keyframe locations provide only positional guidance, making it challenging…
Deep Compositional Phase Diffusion for Long Motion Sequence Generation
Ho Yin Au, Jie Chen, Junkun Jiang +1
Recent research on motion generation has shown significant progress in generating semantically aligned motion with singular semantics. However, when employing these models to creat…
Every Angle Is Worth A Second Glance: Mining Kinematic Skeletal Structures from Multi-view Joint Cloud
Junkun Jiang, Jie Chen, Ho Yin Au +3
Multi-person motion capture over sparse angular observations is a challenging problem under interference from both self- and mutual-occlusions. Existing works produce accurate 2D j…
Exploring Latent Cross-Channel Embedding for Accurate 3D Human Pose Reconstruction in a Diffusion Framework
Junkun Jiang, Jie Chen
Monocular 3D human pose estimation poses significant challenges due to the inherent depth ambiguities that arise during the reprojection process from 2D to 3D. Conventional approac…