Presentation 2021-03-04
Automatic 3D Mesh Generation by Using Extended Attentive Normalization
Yuta Fukatsu, Masaki Aono,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) In recent years, research on conditional image generation using GANs of the type where conditions are given by class labels or texts has been successful. On the other hand, the generation of conditional 3D models consisting of 3D meshes is still in its infancy. In this research, we focus on global information based on Attentive Normalization to improve 3D mesh generation. Specifically, we propose Conditional Attentive Normalization, which is an extension of Attentive Normalization and can add conditional information. Comparative experiments conditioned by class labels and texts have been carried out by using Caltech-UCSD Birds-200-201. It turns out that our proposed method outperforms the conventional methods.
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Paper # PRMU2020-69
Date of Issue 2021-02-25 (PRMU)

Conference Information
Committee PRMU / IPSJ-CVIM
Conference Date 2021/3/4(2days)
Place (in Japanese) (See Japanese page)
Place (in English) Online
Topics (in Japanese) (See Japanese page)
Topics (in English) Computer Vision and Pattern Recognition for specific environment
Chair Yoichi Sato(Univ. of Tokyo)
Vice Chair Akisato Kimura(NTT) / Masakazu Iwamura(Osaka Pref. Univ.)
Secretary Akisato Kimura(Mobility Technologies) / Masakazu Iwamura(Chubu Univ.)
Assistant Takashi Shibata(NTT) / Masashi Nishiyama(Tottori Univ.)

Paper Information
Registration To Technical Committee on Pattern Recognition and Media Understanding / Special Interest Group on Computer Vision and Image Media
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Automatic 3D Mesh Generation by Using Extended Attentive Normalization
Sub Title (in English)
Keyword(1)
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1st Author's Name Yuta Fukatsu
1st Author's Affiliation Toyohashi University of Technology(TUT)
2nd Author's Name Masaki Aono
2nd Author's Affiliation Toyohashi University of Technology(TUT)
Date 2021-03-04
Paper # PRMU2020-69
Volume (vol) vol.120
Number (no) PRMU-409
Page pp.pp.1-6(PRMU),
#Pages 6
Date of Issue 2021-02-25 (PRMU)