11 citations · 13 across the 8 of their papers we have counts for
8 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…
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…
ChoreoGraph: Music-conditioned Automatic Dance Choreography over a Style and Tempo Consistent Dynamic Graph
Ho Yin Au, Jie Chen, Junkun Jiang +1
To generate dance that temporally and aesthetically matches the music is a challenging problem, as the following factors need to be considered. First, the aesthetic styles and mess…
A Dual-Masked Auto-Encoder for Robust Motion Capture with Spatial-Temporal Skeletal Token Completion
Junkun Jiang, Jie Chen, Yike Guo
Multi-person motion capture can be challenging due to ambiguities caused by severe occlusion, fast body movement, and complex interactions. Existing frameworks build on 2D pose est…
SFSegNet: Parse Freehand Sketches using Deep Fully Convolutional Networks
Junkun Jiang, Ruomei Wang, Shujin Lin +1
Parsing sketches via semantic segmentation is attractive but challenging, because (i) free-hand drawings are abstract with large variances in depicting objects due to different dra…