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- Title
基于 Transformer-CVAE的三维人体动画生成方法.
- Authors
冯文科; 石 敏; 朱登明; 李兆歆
- Abstract
3D human animation synthesis is a dominant technology in the domain of 3D animation. Traditional workflows depending on motion capture cannot generate human animation quickly due to complicated procedure and long authoring period. Existing data- driven methods have limited learning capability and therefore the generated animations are lack of realism and the categories of the generation are relatively limited. To that end, this paper presents a 3D human animation synthesis method based on a Transformer- based conditional variation autoencoder (Transformer-CVAE). Firstly, the motion dataset is constructed and classified by the motion category. Then, the temporal relationship between different frames in a common sequence is established by means of the Transformer architecture, and a variational autoencoder is further combined with the Transformer to infer the probabilistic distribution of human motions. Next, to control the desired body motion generated, the constraints are imposed on the latent space. Finally, a series of experiments are conducted on AMASS, HumanACT12 and UESTC datasets and the qualitative and quantitative evaluation is made from two aspects: the visual effect and the performance. Experimental results demonstrate that the method achieves superior performance in the metrics like STED, RMSE, etc. compared with the state- of- art, while capable of synthesizing various human animations with realism.
- Publication
Journal of Frontiers of Computer Science & Technology, 2023, Vol 17, Issue 9, p2137
- ISSN
1673-9418
- Publication type
Article
- DOI
10.3778/j.issn.1673-9418.2206060