DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks
Su S-Y, Bagautdinov T, Rhodin H (2022)
In: European Conference on Computer Vision. 107-124.
Konferenzbeitrag | Englisch
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
Autor*in
Su, Shih-Yang;
Bagautdinov, Timur;
Rhodin, HelgeUniBi 
Erscheinungsjahr
2022
Titel des Konferenzbandes
European Conference on Computer Vision
Seite(n)
107-124
Page URI
https://pub.uni-bielefeld.de/record/2992669
Zitieren
Su S-Y, Bagautdinov T, Rhodin H. DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks. In: European Conference on Computer Vision. 2022: 107-124.
Su, S. - Y., Bagautdinov, T., & Rhodin, H. (2022). DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks. European Conference on Computer Vision, 107-124.
Su, Shih-Yang, Bagautdinov, Timur, and Rhodin, Helge. 2022. “DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks”. In European Conference on Computer Vision, 107-124.
Su, S. - Y., Bagautdinov, T., and Rhodin, H. (2022). “DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks” in European Conference on Computer Vision 107-124.
Su, S.-Y., Bagautdinov, T., & Rhodin, H., 2022. DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks. In European Conference on Computer Vision. pp. 107-124.
S.-Y. Su, T. Bagautdinov, and H. Rhodin, “DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks”, European Conference on Computer Vision, 2022, pp.107-124.
Su, S.-Y., Bagautdinov, T., Rhodin, H.: DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks. European Conference on Computer Vision. p. 107-124. (2022).
Su, Shih-Yang, Bagautdinov, Timur, and Rhodin, Helge. “DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks”. European Conference on Computer Vision. 2022. 107-124.
Open Access
Google Scholar