1 citations · 2 across the 4 of their papers we have counts for
4 papers
TeMuDance: Contrastive Alignment-Based Textual Control for Music-Driven Dance Generation
Xinran Liu, Diptesh Kanojia, Wenwu Wang +1
Existing music-driven dance generation approaches have achieved strong realism and effective audio-motion alignment. However, they generally lack semantic controllability, making i…
DGFM: Full Body Dance Generation Driven by Music Foundation Models
Xinran Liu, Zhenhua Feng, Diptesh Kanojia +1
In music-driven dance motion generation, most existing methods use hand-crafted features and neglect that music foundation models have profoundly impacted cross-modal content gener…
GCDance: Genre-Controlled Music-Driven 3D Full Body Dance Generation
Xinran Liu, Xu Dong, Shenbin Qian +3
Music-driven dance generation is a challenging task as it requires strict adherence to genre-specific choreography while ensuring physically realistic and precisely synchronized da…
Interpretable Long-term Action Quality Assessment
Xu Dong, Xinran Liu, Wanqing Li +2
Long-term Action Quality Assessment (AQA) evaluates the execution of activities in videos. However, the length presents challenges in fine-grained interpretability, with current AQ…