94 citations · 184 across the 12 of their papers we have counts for
10 papers · 1 filter
Robust Dynamic Radiance Fields
Yu-Lun Liu, Chen Gao, Andreas Meuleman +6
Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene. Existing methods, however, assume that accurate camera pose…
Physically Plausible Animation of Human Upper Body from a Single Image
Ziyuan Huang, Zhengping Zhou, Yung-Yu Chuang +2
We present a new method for generating controllable, dynamically responsive, and photorealistic human animations. Given an image of a person, our system allows the user to generate…
NeighborTrack: Improving Single Object Tracking by Bipartite Matching with Neighbor Tracklets
Yu-Hsi Chen, Chien-Yao Wang, Cheng-Yun Yang +4
We propose a post-processor, called NeighborTrack, that leverages neighbor information of the tracking target to validate and improve single-object tracking (SOT) results. It requi…
SearchTrack: Multiple Object Tracking with Object-Customized Search and Motion-Aware Features
Zhong-Min Tsai, Yu-Ju Tsai, Chien-Yao Wang +3
The paper presents a new method, SearchTrack, for multiple object tracking and segmentation (MOTS). To address the association problem between detected objects, SearchTrack propose…
Hybrid Neural Fusion for Full-frame Video Stabilization
Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang +2
Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries, resulting in smaller field of views. In this work, we pre…
Domain-Specific Mappings for Generative Adversarial Style Transfer
Hsin-Yu Chang, Zhixiang Wang, Yung-Yu Chuang
Style transfer generates an image whose content comes from one image and style from the other. Image-to-image translation approaches with disentangled representations have been sho…
Learning to See Through Obstructions with Layered Decomposition
Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang +2
We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions, or adherent raindrops, from a short sequence of images captur…
Learning to See Through Obstructions
Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang +2
We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequence of images captured by a mo…
Illumination-Adaptive Person Re-identification
Zelong Zeng, Zhixiang Wang, Zheng Wang +3
Most person re-identification (ReID) approaches assume that person images are captured under relatively similar illumination conditions. In reality, long-term person retrieval is c…
Learning Deep Convolutional Networks for Demosaicing
Nai-Sheng Syu, Yu-Sheng Chen, Yung-Yu Chuang
This paper presents a comprehensive study of applying the convolutional neural network (CNN) to solving the demosaicing problem. The paper presents two CNN models that learn end-to…