24 citations · 47 across the 18 of their papers we have counts for
19 papers
Pantheon360: Taming Digital Twin Generation via 3D-Aware 360° Video Diffusion
Ting-Hsuan Chen, Ying-Huan Chen, Tao Tu +10
Generating complete digital twins from videos requires precise camera control, global scene coverage, and strict spatial-temporal consistency constraints that remain challenging fo…
No Calibration, No Depth, No Problem: Cross-Sensor View Synthesis with 3D Consistency
Cho-Ying Wu, Zixun Huang, Xinyu Huang +1
We present the first study of cross-sensor view synthesis across different modalities. We examine a practical, fundamental, yet widely overlooked problem: getting aligned RGB-X dat…
Boosting Generalizability towards Zero-Shot Cross-Dataset Single-Image Indoor Depth by Meta-Initialization
Cho-Ying Wu, Yiqi Zhong, Junying Wang +1
Indoor robots rely on depth to perform tasks like navigation or obstacle detection, and single-image depth estimation is widely used to assist perception. Most indoor single-image…
InSpaceType: Dataset and Benchmark for Reconsidering Cross-Space Type Performance in Indoor Monocular Depth
Cho-Ying Wu, Quankai Gao, Chin-Cheng Hsu +3
Indoor monocular depth estimation helps home automation, including robot navigation or AR/VR for surrounding perception. Most previous methods primarily experiment with the NYUv2 D…
InSpaceType: Reconsider Space Type in Indoor Monocular Depth Estimation
Cho-Ying Wu, Quankai Gao, Chin-Cheng Hsu +3
Indoor monocular depth estimation has attracted increasing research interest. Most previous works have been focusing on methodology, primarily experimenting with NYU-Depth-V2 (NYUv…
Meta-Optimization for Higher Model Generalizability in Single-Image Depth Prediction
Cho-Ying Wu, Yiqi Zhong, Junying Wang +1
Model generalizability to unseen datasets, concerned with in-the-wild robustness, is less studied for indoor single-image depth prediction. We leverage gradient-based meta-learning…
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…
Toward Practical Monocular Indoor Depth Estimation
Cho-Ying Wu, Jialiang Wang, Michael Hall +2
The majority of prior monocular depth estimation methods without groundtruth depth guidance focus on driving scenarios. We show that such methods generalize poorly to unseen comple…
Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry
Cho-Ying Wu, Qiangeng Xu, Ulrich Neumann
This work studies learning from a synergy process of 3D Morphable Models (3DMM) and 3D facial landmarks to predict complete 3D facial geometry, including 3D alignment, face orienta…
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…
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…
Geometry-Aware Instance Segmentation with Disparity Maps
Cho-Ying Wu, Xiaoyan Hu, Michael Happold +2
Most previous works of outdoor instance segmentation for images only use color information. We explore a novel direction of sensor fusion to exploit stereo cameras. Geometric infor…
Scene Completeness-Aware Lidar Depth Completion for Driving Scenario
Cho-Ying Wu, Ulrich Neumann
This paper introduces Scene Completeness-Aware Depth Completion (SCADC) to complete raw lidar scans into dense depth maps with fine and complete scene structures. Recent sparse dep…
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 (…
Nonconvex Approach for Sparse and Low-Rank Constrained Models with Dual Momentum
Cho-Ying Wu, Jian-Jiun Ding
In this manuscript, we research on the behaviors of surrogates for the rank function on different image processing problems and their optimization algorithms. We first propose a no…
Occluded Face Recognition Using Low-rank Regression with Generalized Gradient Direction
Cho-Ying Wu, Jian-Jiun Ding
In this paper, a very effective method to solve the contiguous face occlusion recognition problem is proposed. It utilizes the robust image gradient direction features together wit…
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…
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…
Efficient Multi-Domain Dictionary Learning with GANs
Cho Ying Wu, Ulrich Neumann
In this paper, we propose the multi-domain dictionary learning (MDDL) to make dictionary learning-based classification more robust to data representing in different domains. We use…