5 papers
LaMoGen: Language to Motion Generation Through LLM-Guided Symbolic Inference
Junkun Jiang, Ho Yin Au, Jingyu Xiang +1
Human motion is highly expressive and naturally aligned with language, yet prevailing methods relying heavily on joint text-motion embeddings struggle to synthesize temporally accu…
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…