activity
20182024
most citedVoice2Mesh: Cross-Modal 3D Face Model Generation from Voices

2 citations · 2 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2022

Cross-Modal Perceptionist: Can Face Geometry be Gleaned from Voices?

Cho-Ying Wu, Chin-Cheng Hsu, Ulrich Neumann

This work digs into a root question in human perception: can face geometry be gleaned from one's voices? Previous works that study this question only adopt developments in image sy…

cs.GR20212 cited

Voice2Mesh: Cross-Modal 3D Face Model Generation from Voices

Cho-Ying Wu, Ke Xu, Chin-Cheng Hsu +1

This work focuses on the analysis that whether 3D face models can be learned from only the speech inputs of speakers. Previous works for cross-modal face synthesis study image gene…

cs.CV2021

Accurate 3D Facial Geometry Prediction by Multi-Task, Multi-Modal, and Multi-Representation Landmark Refinement Network

Cho-Ying Wu, Qiangeng Xu, Ulrich Neumann

This work focuses on complete 3D facial geometry prediction, including 3D facial alignment via 3D face modeling and face orientation estimation using the proposed multi-task, multi…

cs.CV2019

Grid-GCN for Fast and Scalable Point Cloud Learning

Qiangeng Xu, Xudong Sun, Cho-Ying Wu +2

Due to the sparsity and irregularity of the point cloud data, methods that directly consume points have become popular. Among all point-based models, graph convolutional networks (…

eess.IV2019

Salient Building Outline Enhancement and Extraction Using Iterative L0 Smoothing and Line Enhancing

Cho-Ying Wu, Ulrich Neumann

In this paper, our goal is salient building outline enhancement and extraction from images taken from consumer cameras using L0 smoothing. We address weak outlines and over-smoothi…

cs.CV2019

Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion

Yiqi Zhong, Cho-Ying Wu, Suya You +1

In this paper, we propose our Correlation For Completion Network (CFCNet), an end-to-end deep learning model that uses the correlation between two data sources to perform sparse de…