102 citations · 194 across the 6 of their papers we have counts for
6 papers
Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real Adaptation
Yu-Ying Yeh, Koki Nagano, Sameh Khamis +3
Given a portrait image of a person and an environment map of the target lighting, portrait relighting aims to re-illuminate the person in the image as if the person appeared in an…
PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes
Yu-Ying Yeh, Zhengqin Li, Yannick Hold-Geoffroy +5
Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout. In this work, we go beyond this to propose PhotoScene, a framework that takes input im…
OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets
Zhengqin Li, Ting-Wei Yu, Shen Sang +14
We propose a novel framework for creating large-scale photorealistic datasets of indoor scenes, with ground truth geometry, material, lighting and semantics. Our goal is to make th…
Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes
Zhengqin Li, Yu-Ying Yeh, Manmohan Chandraker
Recovering the 3D shape of transparent objects using a small number of unconstrained natural images is an ill-posed problem. Complex light paths induced by refraction and reflectio…
A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation
Alexander H. Liu, Yen-Cheng Liu, Yu-Ying Yeh +1
We present a novel and unified deep learning framework which is capable of learning domain-invariant representation from data across multiple domains. Realized by adversarial train…
Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification
Yu-Jhe Li, Fu-En Yang, Yen-Cheng Liu +3
Person re-identification (Re-ID) aims at recognizing the same person from images taken across different cameras. To address this task, one typically requires a large amount labeled…