5 citations · 8 across the 5 of their papers we have counts for
5 papers
HoloGest: Decoupled Diffusion and Motion Priors for Generating Holisticly Expressive Co-speech Gestures
Yongkang Cheng, Shaoli Huang
Animating virtual characters with holistic co-speech gestures is a challenging but critical task. Previous systems have primarily focused on the weak correlation between audio and…
Conditional GAN for Enhancing Diffusion Models in Efficient and Authentic Global Gesture Generation from Audios
Yongkang Cheng, Mingjiang Liang, Shaoli Huang +3
Audio-driven simultaneous gesture generation is vital for human-computer communication, AI games, and film production. While previous research has shown promise, there are still li…
RopeTP: Global Human Motion Recovery via Integrating Robust Pose Estimation with Diffusion Trajectory Prior
Mingjiang Liang, Yongkang Cheng, Hualin Liang +2
We present RopeTP, a novel framework that combines Robust pose estimation with a diffusion Trajectory Prior to reconstruct global human motion from videos. At the heart of RopeTP i…
ReinDiffuse: Crafting Physically Plausible Motions with Reinforced Diffusion Model
Gaoge Han, Mingjiang Liang, Jinglei Tang +3
Generating human motion from textual descriptions is a challenging task. Existing methods either struggle with physical credibility or are limited by the complexities of physics si…
ExpGest: Expressive Speaker Generation Using Diffusion Model and Hybrid Audio-Text Guidance
Yongkang Cheng, Mingjiang Liang, Shaoli Huang +3
Existing gesture generation methods primarily focus on upper body gestures based on audio features, neglecting speech content, emotion, and locomotion. These limitations result in…