papers

Publications (7)

cs.CV2026

TalkingEyes: Pluralistic Speech-Driven 3D Eye Gaze Animation

Yixiang Zhuang, Chunshan Ma, Yao Cheng +3

Although significant progress has been made in the field of speech-driven 3D facial animation recently, the speech-driven animation of an indispensable facial component, eye gaze,…

cs.CV2024

Dialogue Director: Bridging the Gap in Dialogue Visualization for Multimodal Storytelling

Min Zhang, Zilin Wang, Liyan Chen +2

Recent advances in AI-driven storytelling have enhanced video generation and story visualization. However, translating dialogue-centric scripts into coherent storyboards remains a…

cs.CV2024

DNPM: A Neural Parametric Model for the Synthesis of Facial Geometric Details

Haitao Cao, Baoping Cheng, Qiran Pu +6

Parametric 3D models have enabled a wide variety of computer vision and graphics tasks, such as modeling human faces, bodies and hands. In 3D face modeling, 3DMM is the most widely…

cs.HC2023

DisPad: Flexible On-Body Displacement of Fabric Sensors for Robust Joint-Motion Tracking

Xiaowei Chen, Xiao Jiang, Jiawei Fang +5

The last few decades have witnessed an emerging trend of wearable soft sensors; however, there are important signal-processing challenges for soft sensors that still limit their pr…

cs.CV2024

Learn2Talk: 3D Talking Face Learns from 2D Talking Face

Yixiang Zhuang, Baoping Cheng, Yao Cheng +6

Speech-driven facial animation methods usually contain two main classes, 3D and 2D talking face, both of which attract considerable research attention in recent years. However, to…

physics.soc-ph2021

Modeling Spatial Nonstationarity via Deformable Convolutions for Deep Traffic Flow Prediction

Wei Zeng, Chengqiao Lin, Kang Liu +2

Deep neural networks are being increasingly used for short-term traffic flow prediction, which can be generally categorized as convolutional (CNNs) or graph neural networks (GNNs).…

cs.CV2020

Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual Analytics

Wei Zeng, Chengqiao Lin, Juncong Lin +4

Deep learning methods are being increasingly used for urban traffic prediction where spatiotemporal traffic data is aggregated into sequentially organized matrices that are then fe…