Spectral normalization and relativistic adversarial training for conditional pose generation with self-attention

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

We address the problem of conditional image generation of synthesizing a new image of an individual given a reference image and target pose. We base our approach on generative adversarial networks and leverage deformable skip connections to deal with pixel-to-pixel misalignments, self-attention to leverage complementary features in separate portions of the image, e.g., arms or legs, and spectral normalization to improve the quality of the synthesized images. We train the synthesis model with a nearest-neighbour loss in combination with a relativistic average hinge adversarial loss. We evaluate on the Market-1501 dataset and show how our proposed approach can surpass existing approaches in conditional image synthesis performance.

本文言語English
ホスト出版物のタイトルProceedings of the 16th International Conference on Machine Vision Applications, MVA 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9784901122184
DOI
出版ステータスPublished - 2019 5
イベント16th International Conference on Machine Vision Applications, MVA 2019 - Tokyo, Japan
継続期間: 2019 5 272019 5 31

出版物シリーズ

名前Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019

Conference

Conference16th International Conference on Machine Vision Applications, MVA 2019
国/地域Japan
CityTokyo
Period19/5/2719/5/31

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 信号処理
  • コンピュータ ビジョンおよびパターン認識

フィンガープリント

「Spectral normalization and relativistic adversarial training for conditional pose generation with self-attention」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル