Bring Your Own Character: A Holistic Solution for Automatic Facial Animation Generation of Customized Characters
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910338995191808 |
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| author | Bai, Zechen Chen, Peng Peng, Xiaolan Liu, Lu Chen, Hui Shou, Mike Zheng Tian, Feng |
| author_facet | Bai, Zechen Chen, Peng Peng, Xiaolan Liu, Lu Chen, Hui Shou, Mike Zheng Tian, Feng |
| contents | Animating virtual characters has always been a fundamental research problem in virtual reality (VR). Facial animations play a crucial role as they effectively convey emotions and attitudes of virtual humans. However, creating such facial animations can be challenging, as current methods often involve utilization of expensive motion capture devices or significant investments of time and effort from human animators in tuning animation parameters. In this paper, we propose a holistic solution to automatically animate virtual human faces. In our solution, a deep learning model was first trained to retarget the facial expression from input face images to virtual human faces by estimating the blendshape coefficients. This method offers the flexibility of generating animations with characters of different appearances and blendshape topologies. Second, a practical toolkit was developed using Unity 3D, making it compatible with the most popular VR applications. The toolkit accepts both image and video as input to animate the target virtual human faces and enables users to manipulate the animation results. Furthermore, inspired by the spirit of Human-in-the-loop (HITL), we leveraged user feedback to further improve the performance of the model and toolkit, thereby increasing the customization properties to suit user preferences. The whole solution, for which we will make the code public, has the potential to accelerate the generation of facial animations for use in VR applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_13724 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bring Your Own Character: A Holistic Solution for Automatic Facial Animation Generation of Customized Characters Bai, Zechen Chen, Peng Peng, Xiaolan Liu, Lu Chen, Hui Shou, Mike Zheng Tian, Feng Human-Computer Interaction Computer Vision and Pattern Recognition Animating virtual characters has always been a fundamental research problem in virtual reality (VR). Facial animations play a crucial role as they effectively convey emotions and attitudes of virtual humans. However, creating such facial animations can be challenging, as current methods often involve utilization of expensive motion capture devices or significant investments of time and effort from human animators in tuning animation parameters. In this paper, we propose a holistic solution to automatically animate virtual human faces. In our solution, a deep learning model was first trained to retarget the facial expression from input face images to virtual human faces by estimating the blendshape coefficients. This method offers the flexibility of generating animations with characters of different appearances and blendshape topologies. Second, a practical toolkit was developed using Unity 3D, making it compatible with the most popular VR applications. The toolkit accepts both image and video as input to animate the target virtual human faces and enables users to manipulate the animation results. Furthermore, inspired by the spirit of Human-in-the-loop (HITL), we leveraged user feedback to further improve the performance of the model and toolkit, thereby increasing the customization properties to suit user preferences. The whole solution, for which we will make the code public, has the potential to accelerate the generation of facial animations for use in VR applications. |
| title | Bring Your Own Character: A Holistic Solution for Automatic Facial Animation Generation of Customized Characters |
| topic | Human-Computer Interaction Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.13724 |