Search Results for author: Lap-Fai Yu

Found 9 papers, 0 papers with code

Designing Human-Robot Coexistence Space

no code implementations14 Nov 2020 Jixuan Zhi, Lap-Fai Yu, Jyh-Ming Lien

Our implementation shows that the proposed framework can produce a design around three to five minutes on average comparing to 10 to 20 minutes without the proposed motion planner.

Motion Planning

3D Face Synthesis Driven by Personality Impression

no code implementations27 Sep 2018 Yining Lang, Wei Liang, Yujia Wang, Lap-Fai Yu

In this paper, we propose a novel approach to synthesize 3D faces based on personality impression for creating virtual characters.


Transferring Objects: Joint Inference of Container and Human Pose

no code implementations ICCV 2017 Hanqing Wang, Wei Liang, Lap-Fai Yu

In the inference phase, given a scanned 3D scene with different object candidates and a dictionary of human poses, our approach infers the best object as a container together with human pose for transferring a given object.


Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars

no code implementations1 Apr 2017 Chenfanfu Jiang, Siyuan Qi, Yixin Zhu, Siyuan Huang, Jenny Lin, Lap-Fai Yu, Demetri Terzopoulos, Song-Chun Zhu

We propose a systematic learning-based approach to 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 learning-based computer vision and robotics algorithms.

Benchmarking Object +2

Fill and Transfer: A Simple Physics-Based Approach for Containability Reasoning

no code implementations ICCV 2015 Lap-Fai Yu, Noah Duncan, Sai-Kit Yeung

We apply our approach to reason about the containability of several real-world objects acquired using a consumer-grade depth camera.

Shading-Based Shape Refinement of RGB-D Images

no code implementations CVPR 2013 Lap-Fai Yu, Sai-Kit Yeung, Yu-Wing Tai, Stephen Lin

We present a shading-based shape refinement algorithm which uses a noisy, incomplete depth map from Kinect to help resolve ambiguities in shape-from-shading.

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