Pose‐to‐Motion: Cross‐Domain Motion Retargeting with Pose Prior
Creating plausible motions for a diverse range of characters is a long‐standing goal in computer graphics. Current learning‐based motion synthesis methods rely on large‐scale motion datasets, which are often difficult if not impossible to acquire. On the other hand, pose data is more accessible, sin...
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| Veröffentlicht in: | Computer graphics forum Jg. 43; H. 8 |
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01.12.2024
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| Abstract | Creating plausible motions for a diverse range of characters is a long‐standing goal in computer graphics. Current learning‐based motion synthesis methods rely on large‐scale motion datasets, which are often difficult if not impossible to acquire. On the other hand, pose data is more accessible, since static posed characters are easier to create and can even be extracted from images using recent advancements in computer vision. In this paper, we tap into this alternative data source and introduce a neural motion synthesis approach through retargeting, which generates plausible motion of various characters that only have pose data by transferring motion from one single existing motion capture dataset of another drastically different characters. Our experiments show that our method effectively combines the motion features of the source character with the pose features of the target character, and performs robustly with small or noisy pose data sets, ranging from a few artist‐created poses to noisy poses estimated directly from images. Additionally, a conducted user study indicated that a majority of participants found our retargeted motion to be more enjoyable to watch, more lifelike in appearance, and exhibiting fewer artifacts. Our code and dataset can be accessed here. |
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| AbstractList | Creating plausible motions for a diverse range of characters is a long‐standing goal in computer graphics. Current learning‐based motion synthesis methods rely on large‐scale motion datasets, which are often difficult if not impossible to acquire. On the other hand, pose data is more accessible, since static posed characters are easier to create and can even be extracted from images using recent advancements in computer vision. In this paper, we tap into this alternative data source and introduce a neural motion synthesis approach through retargeting, which generates plausible motion of various characters that only have pose data by transferring motion from one single existing motion capture dataset of another drastically different characters. Our experiments show that our method effectively combines the motion features of the source character with the pose features of the target character, and performs robustly with small or noisy pose data sets, ranging from a few artist‐created poses to noisy poses estimated directly from images. Additionally, a conducted user study indicated that a majority of participants found our retargeted motion to be more enjoyable to watch, more lifelike in appearance, and exhibiting fewer artifacts. Our code and dataset can be accessed here. |
| Author | Olga, Sorkine‐Hornung Li, Peizhuo Wetzstein, Gordon Yifan, Wang Zhao, Qingqing |
| Author_xml | – sequence: 1 givenname: Qingqing surname: Zhao fullname: Zhao, Qingqing organization: Stanford University USA – sequence: 2 givenname: Peizhuo surname: Li fullname: Li, Peizhuo organization: ETH Zurich Switzerland – sequence: 3 givenname: Wang surname: Yifan fullname: Yifan, Wang organization: Stanford University USA – sequence: 4 givenname: Sorkine‐Hornung surname: Olga fullname: Olga, Sorkine‐Hornung organization: ETH Zurich Switzerland – sequence: 5 givenname: Gordon surname: Wetzstein fullname: Wetzstein, Gordon organization: Stanford University USA |
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| Cites_doi | 10.1109/ICCV48922.2021.00958 10.1007/978-3-031-20086-1_37 10.1145/1477926.1477927 10.1007/978-3-319-70139-4 10.1145/3550469.3555428 10.1109/3DV50981.2020.00102 10.1109/ICCV.2019.00546 10.1109/ICCV.2017.244 10.1109/CVPR52729.2023.00051 10.1016/j.cag.2022.04.001 10.1145/2601097.2601192 10.1145/3424636.3426909 10.1145/2897824.2925975 10.1145/3197517.3201366 10.1145/3528223.3530157 10.1145/311535.311539 10.1145/3283289.3283316 10.1145/3355089.3356505 10.1109/CVPR.2019.01123 10.1145/3606928 10.1145/3272127.3275028 10.1145/3414685.3417877 10.1109/ICCV48922.2021.01315 10.1109/CVPR.2018.00901 10.1145/2485895.2485903 10.1145/1015706.1015736 10.1145/3386569.3392480 10.1145/311535.311536 10.1002/1099-1778(200012)11:5<223::AID-VIS236>3.0.CO;2-5 10.1111/cgf.12860 10.1111/cgf.12507 10.1145/3450626.3459670 10.1109/ICCV.2015.494 10.1145/3528223.3530178 10.1145/3386569.3392469 10.1145/1037957.1037963 10.1007/s10654-016-0149-3 10.1109/WACVW60836.2024.00017 10.1145/3528223.3530106 10.1145/1531326.1531342 10.1145/3386569.3392462 |
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| Title | Pose‐to‐Motion: Cross‐Domain Motion Retargeting with Pose Prior |
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