Real-time Diverse Motion In-betweening with Space-time Control
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914961242980352 |
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| author | Chu, Yuchen Yang, Zeshi |
| author_facet | Chu, Yuchen Yang, Zeshi |
| contents | In this work, we present a data-driven framework for generating diverse in-betweening motions for kinematic characters. Our approach injects dynamic conditions and explicit motion controls into the procedure of motion transitions. Notably, this integration enables a finer-grained spatial-temporal control by allowing users to impart additional conditions, such as duration, path, style, etc., into the in-betweening process. We demonstrate that our in-betweening approach can synthesize both locomotion and unstructured motions, enabling rich, versatile, and high-quality animation generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00270 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-time Diverse Motion In-betweening with Space-time Control Chu, Yuchen Yang, Zeshi Graphics Machine Learning In this work, we present a data-driven framework for generating diverse in-betweening motions for kinematic characters. Our approach injects dynamic conditions and explicit motion controls into the procedure of motion transitions. Notably, this integration enables a finer-grained spatial-temporal control by allowing users to impart additional conditions, such as duration, path, style, etc., into the in-betweening process. We demonstrate that our in-betweening approach can synthesize both locomotion and unstructured motions, enabling rich, versatile, and high-quality animation generation. |
| title | Real-time Diverse Motion In-betweening with Space-time Control |
| topic | Graphics Machine Learning |
| url | https://arxiv.org/abs/2410.00270 |