5 citations · 8 across the 4 of their papers we have counts for
4 papers
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…