Automatic Stylized Action Generation in Animation Using Deep Learning
The style of animation plays a pivotal role in character animation, reflecting various aspects of a character such as emotions, personality, or traits. However, capturing these stylized actions is challenging due to the subjective perceptions of human observers and the resource-intensive nature of t...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10734093/ |
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| author | Xiaoyu Su Hyung-Gi Kim |
| author_facet | Xiaoyu Su Hyung-Gi Kim |
| author_sort | Xiaoyu Su |
| collection | DOAJ |
| description | The style of animation plays a pivotal role in character animation, reflecting various aspects of a character such as emotions, personality, or traits. However, capturing these stylized actions is challenging due to the subjective perceptions of human observers and the resource-intensive nature of traditional methods like action capture devices and manual animation. This study proposes a novel approach utilizing semi-supervision learning to generate high-quality stylized animations. Specifically, our method combines labeled and unlabeled animation data to train stylization models, employing spatiotemporal graph convolutional networks (ST-GCN) and StyleNet modules. The ST-GCN leverages the topological information of the character skeleton to enhance network representation capabilities, while StyleNet modules enable the transfer of both global and local styles. By incorporating content coherence loss, style triplet margin loss, content retention loss, and stylization loss, our approach ensures coherent stylization and improved generalization to unknown content animations. Experimental results demonstrate that our method significantly outperforms existing techniques in generating high-quality, coherent stylized animations. |
| format | Article |
| id | doaj-art-d02a0c0cc6b041dea1b59d187fced36d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d02a0c0cc6b041dea1b59d187fced36d2024-12-18T00:02:03ZengIEEEIEEE Access2169-35362024-01-011218877318878610.1109/ACCESS.2024.348602410734093Automatic Stylized Action Generation in Animation Using Deep LearningXiaoyu Su0https://orcid.org/0009-0009-4983-8756Hyung-Gi Kim1Graduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul, Republic of KoreaGraduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul, Republic of KoreaThe style of animation plays a pivotal role in character animation, reflecting various aspects of a character such as emotions, personality, or traits. However, capturing these stylized actions is challenging due to the subjective perceptions of human observers and the resource-intensive nature of traditional methods like action capture devices and manual animation. This study proposes a novel approach utilizing semi-supervision learning to generate high-quality stylized animations. Specifically, our method combines labeled and unlabeled animation data to train stylization models, employing spatiotemporal graph convolutional networks (ST-GCN) and StyleNet modules. The ST-GCN leverages the topological information of the character skeleton to enhance network representation capabilities, while StyleNet modules enable the transfer of both global and local styles. By incorporating content coherence loss, style triplet margin loss, content retention loss, and stylization loss, our approach ensures coherent stylization and improved generalization to unknown content animations. Experimental results demonstrate that our method significantly outperforms existing techniques in generating high-quality, coherent stylized animations.https://ieeexplore.ieee.org/document/10734093/Animationdeep learningstylizationST-GCNStyleNet |
| spellingShingle | Xiaoyu Su Hyung-Gi Kim Automatic Stylized Action Generation in Animation Using Deep Learning IEEE Access Animation deep learning stylization ST-GCN StyleNet |
| title | Automatic Stylized Action Generation in Animation Using Deep Learning |
| title_full | Automatic Stylized Action Generation in Animation Using Deep Learning |
| title_fullStr | Automatic Stylized Action Generation in Animation Using Deep Learning |
| title_full_unstemmed | Automatic Stylized Action Generation in Animation Using Deep Learning |
| title_short | Automatic Stylized Action Generation in Animation Using Deep Learning |
| title_sort | automatic stylized action generation in animation using deep learning |
| topic | Animation deep learning stylization ST-GCN StyleNet |
| url | https://ieeexplore.ieee.org/document/10734093/ |
| work_keys_str_mv | AT xiaoyusu automaticstylizedactiongenerationinanimationusingdeeplearning AT hyunggikim automaticstylizedactiongenerationinanimationusingdeeplearning |