컴퓨터 그래픽스 학회인 SIGGRAPH Asia 2018에 Motion AI 팀의 A Variational U-Net for Motion Retargeting 연구가 소개되었습니다.
Abstract
In this paper, we present a novel motion retargeting system by using the deep autoencoder combining the Deep Convolution Inverse Graphics Network (DC-IGN) ([Kulkarni et al. 2015]) and the U-Net ([Long et al. 2015]) to produce high-quality human motion. The retargeted motion is fully-automatically and naturally generated from the given input motion and bone length ratios. To validate the proposed motion retargeting system, we conduct several experiments and achieve more accuracy and less computational burden when compared with the conventional motion retargeting approach and other neural network architectures.
Authors
Hanyoung Jang, Byungjun Kwon, Moonwon Yu(NCSOFT), Seong Uk Kim, Jongmin Kim(Kangwon National University)
Proceeding
SA ‘18 SIGGRAPH Asia 2018 Posters Article No. 1
A variational U-Net for motion retargeting