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Human Facial Animation Retargeting by Unsupervised Neural Network
https://ipsj.ixsq.nii.ac.jp/records/233208
https://ipsj.ixsq.nii.ac.jp/records/233208895b4b40-5530-447c-8694-c4772360ee19
| 名前 / ファイル | ライセンス | アクション |
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IPSJ-CG24193007.pdf (3.4 MB)
2026年3月11日からダウンロード可能です。
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Copyright (c) 2024 by the Information Processing Society of Japan
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| 非会員：¥660, IPSJ:学会員：¥330, CG:会員：¥0, DLIB:会員：¥0 | ||
| Item type | SIG Technical Reports(1) | |||||||||
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| 公開日 | 2024-03-11 | |||||||||
| タイトル | ||||||||||
| タイトル | Human Facial Animation Retargeting by Unsupervised Neural Network | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Human Facial Animation Retargeting by Unsupervised Neural Network | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | スポットライト | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||||||
| 資源タイプ | technical report | |||||||||
| 著者所属 | ||||||||||
| Tokyo Metropolitan University | ||||||||||
| 著者所属 | ||||||||||
| Tokyo Metropolitan University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Tokyo Metropolitan University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Tokyo Metropolitan University | ||||||||||
| 著者名 |
Yuhao, Dou
× Yuhao, Dou
× Tomohiko, Mukai
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| 著者名(英) |
Yuhao, Dou
× Yuhao, Dou
× Tomohiko, Mukai
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | This research proposes an unsupervised facial retargeting method based on graph convolution networks and a variant of generative adversarial networks. Our insight is that animations of different face models can be represented by a common set of landmark points. To enable the down-sampling and up-sampling on the graph structure, we introduce graph pooling and graph unpooling using the landmark set. In the encoding stage, the graph pooling operator merges the several adjacent node features into a landmark node to compose the shared latent space. The latent code is decoded to reconstruct the face model by the graph unpooling in the decoding stage. Our approach enables retargeting animation between face models of different mesh topologies with unpaired data in the common latent space. | |||||||||
| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | This research proposes an unsupervised facial retargeting method based on graph convolution networks and a variant of generative adversarial networks. Our insight is that animations of different face models can be represented by a common set of landmark points. To enable the down-sampling and up-sampling on the graph structure, we introduce graph pooling and graph unpooling using the landmark set. In the encoding stage, the graph pooling operator merges the several adjacent node features into a landmark node to compose the shared latent space. The latent code is decoded to reconstruct the face model by the graph unpooling in the decoding stage. Our approach enables retargeting animation between face models of different mesh topologies with unpaired data in the common latent space. | |||||||||
| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AN10100541 | |||||||||
| 書誌情報 |
研究報告コンピュータグラフィックスとビジュアル情報学（CG） 巻 2024-CG-193, 号 7, p. 1-4, 発行日 2024-03-11 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 2188-8949 | |||||||||
| Notice | ||||||||||
| SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. | ||||||||||
| 出版者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
