2 citations · 2 across the 9 of their papers we have counts for
6 papers · 1 filter
DyStream: Streaming Dyadic Talking Heads Generation via Flow Matching-based Autoregressive Model
Bohong Chen, Haiyang Liu
Generating realistic, dyadic talking head video requires ultra-low latency. Existing chunk-based methods require full non-causal context windows, introducing significant delays. Th…
Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation
Xiaochuan Li, Guoguang Du, Runze Zhang +11
Scaling laws have validated the success and promise of large-data-trained models in creative generation across text, image, and video domains. However, this paradigm faces data sca…
Livatar-1: Real-Time Talking Heads Generation with Tailored Flow Matching
Haiyang Liu, Xiaolin Hong, Xuancheng Yang +5
We present Livatar, a real-time audio-driven talking heads videos generation framework. Existing baselines suffer from limited lip-sync accuracy and long-term pose drift. We addres…
Intentional Gesture: Deliver Your Intentions with Gestures for Speech
Pinxin Liu, Haiyang Liu, Luchuan Song +2
When humans speak, gestures help convey communicative intentions, such as adding emphasis or describing concepts. However, current co-speech gesture generation methods rely solely…
Video Motion Graphs
Haiyang Liu, Zhan Xu, Fa-Ting Hong +3
We present Video Motion Graphs, a system designed to generate realistic human motion videos. Using a reference video and conditional signals such as music or motion tags, the syste…
GestureLSM: Latent Shortcut based Co-Speech Gesture Generation with Spatial-Temporal Modeling
Pinxin Liu, Luchuan Song, Junhua Huang +3
Generating full-body human gestures based on speech signals remains challenges on quality and speed. Existing approaches model different body regions such as body, legs and hands s…