Differentiable rendering-based pose-conditioned human image generation

研究成果: Conference contribution

抄録

Conditional human image generation, or generation of human images with specified pose based on one or more reference images, is an inherently ill-defined problem, as there can be multiple plausible appearance for parts that are occluded in the reference. Using multiple images can mitigate this problem while boosting the performance. In this work, we introduce a differentiable vertex and edge renderer for incorporating the pose information to realize human image generation conditioned on multiple reference images. The differentiable renderer has parameters that can be jointly optimized with other parts of the system to obtain better results by learning more meaningful shape representation of human pose. We evaluate our method on the Market-1501 and DeepFashion datasets and comparison with existing approaches validates the effectiveness of our approach.

本文言語English
ホスト出版物のタイトルProceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
出版社IEEE Computer Society
ページ3916-3920
ページ数5
ISBN(電子版)9781665448994
DOI
出版ステータスPublished - 2021 6
イベント2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 - Virtual, Online, United States
継続期間: 2021 6 192021 6 25

出版物シリーズ

名前IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN(印刷版)2160-7508
ISSN(電子版)2160-7516

Conference

Conference2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
国/地域United States
CityVirtual, Online
Period21/6/1921/6/25

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学

引用スタイル