Abstract
We present a novel Animation CelebHeads dataset (AnimeCeleb) to address an animation head reenactment. Different from previous animation head datasets, we utilize a 3D animation models as the controllable image samplers, which can provide a large amount of head images with their corresponding detailed pose annotations. To facilitate a data creation process, we build a semi-automatic pipeline leveraging an open 3D computer graphics software with a developed annotation system. After training with the AnimeCeleb, recent head reenactment models produce high-quality animation head reenactment results, which are not achievable with existing datasets. Furthermore, motivated by metaverse application, we propose a novel pose mapping method and architecture to tackle a cross-domain head reenactment task. During inference, a user can easily transfer one’s motion to an arbitrary animation head. Experiments demonstrate an usefulness of the AnimeCeleb to train animation head reenactment models, and the superiority of our cross-domain head reenactment model compared to state-of-the-art methods. Our dataset and code are available at this url.
K. Kim, S. Park and J. Lee—Equal contributions
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Notes
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Throughout this paper, we mean by the ’pose’ the information about head rotation, translation, and facial expression.
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Related work regarding to the AnimeCeleb and the proposed algorithm is provided in supplementary material.
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Acknowledgements
This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2019-0-00075, Artificial Intelligence Graduate School Program (KAIST), No. 2021-0-01778, Development of human image synthesis and discrimination technology below the perceptual threshold), and the Air Force Research Laboratory, under agreement number FA2386-22-1-4024. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. Finally, we thank all researchers at NAVER WEBTOON Corp.
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Kim, K., Park, S., Lee, J., Chung, S., Lee, J., Choo, J. (2022). AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13668. Springer, Cham. https://doi.org/10.1007/978-3-031-20074-8_24
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