Lap-Fai Yu
George Mason University
82 Papers
185 Citations
Lap-Fai Yu is an academic researcher from George Mason University. The author has contributed to research in topics: Computer science & Virtual reality. The author has an hindex of 17, co-authored 64 publications. Previous affiliations of Lap-Fai Yu include University of Massachusetts Boston & University of California, Los Angeles.
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Papers
Transferring Objects: Joint Inference of Container and Human Pose
Hanqing Wang,Wei Liang,Lap-Fai Yu +2 more
- 01 Oct 2017
TL;DR: This paper proposes an approach to jointly infer container and human pose for transferring objects by minimizing the costs associated both object and pose candidates.
Scene-Aware Background Music Synthesis
Yujia Wang,Wei Liang,Wanwan Li,Dingzeyu Li,Lap-Fai Yu +4 more
- 12 Oct 2020
TL;DR: This paper introduces an interactive background music synthesis algorithm guided by visual content that can synthesize dynamic background music for different types of scenarios and conducts quantitative and qualitative analysis on the synthesized results to validate the efficacy of the approach.
RGB-D to CAD Retrieval with ObjectNN Dataset.
Binh-Son Hua,Quang-Trung Truong,Minh-Khoi Tran,Quang-Hieu Pham,Asako Kanezaki,Tang Lee,Hung-Yueh Chiang,Winston H. Hsu,Bo Li,Yijuan Lu,Henry Johan,Shoki Tashiro,Masaki Aono,Minh-Triet Tran,V.-K. Pham,Hai-Dang Nguyen,Vinh-Tiep Nguyen,Quang-Thang Tran,Thuyen V. Phan,Bao Truong,Minh N. Do,Anh Duc Duong,Lap-Fai Yu,Duc Thanh Nguyen,Sai-Kit Yeung +24 more
- 01 Jan 2017
TL;DR: The evaluation results show that the RGB-D to CAD retrieval problem, while being challenging to solve due to partial and noisy 3D reconstruction, can be addressed to a good extent using deep learning techniques, particularly, convolutional neural networks trained by multi-view and 3D geometry.
Configurable 3D Scene Synthesis and 2D Image Rendering with Per-pixel Ground Truth Using Stochastic Grammars
Chenfanfu Jiang,Siyuan Qi,Yixin Zhu,Siyuan Huang,Jenny Lin,Lap-Fai Yu,Demetri Terzopoulos,Song-Chun Zhu +7 more
TL;DR: In this article, a learning-based approach is proposed for the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with associated ground truth information, for the purposes of training, benchmarking, and diagnosing learningbased computer vision and robotics algorithms.
Toward Automatic Audio Description Generation for Accessible Videos
Yujia Wang,Wei Liang,Haikun Huang,Yongqi Zhang,Dingzeyu Li,Lap-Fai Yu +5 more
- 06 May 2021
TL;DR: In this paper, a system that analyzes the audiovisual contents of a video and generates the audio descriptions is presented, which consists of three modules: AD insertion time prediction, AD generation, and AD optimization.