activity
20172022
most citedNeural 3D Clothes Retargeting from a Single Image

5 citations · 7 across the 4 of their papers we have counts for

collaborators

8 papers

cs.CV2022

Learning Motion-Dependent Appearance for High-Fidelity Rendering of Dynamic Humans from a Single Camera

Jae Shin Yoon, Duygu Ceylan, Tuanfeng Y. Wang +4

Appearance of dressed humans undergoes a complex geometric transformation induced not only by the static pose but also by its dynamics, i.e., there exists a number of cloth geometr…

cs.CV20215 cited

Neural 3D Clothes Retargeting from a Single Image

Jae Shin Yoon, Kihwan Kim, Jan Kautz +1

In this paper, we present a method of clothes retargeting; generating the potential poses and deformations of a given 3D clothing template model to fit onto a person in a single RG…

cs.CV2020

Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths from a Monocular Camera

Jae Shin Yoon, Kihwan Kim, Orazio Gallo +2

This paper presents a new method to synthesize an image from arbitrary views and times given a collection of images of a dynamic scene. A key challenge for the novel view synthesis…

cs.CV2019

Self-Supervised Adaptation of High-Fidelity Face Models for Monocular Performance Tracking

Jae Shin Yoon, Takaaki Shiratori, Shoou-I Yu +1

Improvements in data-capture and face modeling techniques have enabled us to create high-fidelity realistic face models. However, driving these realistic face models requires speci…

cs.CV2018

HUMBI: A Large Multiview Dataset of Human Body Expressions

Zhixuan Yu, Jae Shin Yoon, In Kyu Lee +4

This paper presents a new large multiview dataset called HUMBI for human body expressions with natural clothing. The goal of HUMBI is to facilitate modeling view-specific appearanc…

cs.CV2017

3D Semantic Trajectory Reconstruction from 3D Pixel Continuum

Jae Shin Yoon, Ziwei Li, Hyun Soo Park

This paper presents a method to reconstruct dense semantic trajectory stream of human interactions in 3D from synchronized multiple videos. The interactions inherently introduce se…