AnimeCeleb: Large-Scale Animation CelebFaces Dataset via Controllable 3D Synthetic Models

• URL: http://arxiv.org/abs/2111.07640v1
• Date: Mon, 15 Nov 2021 10:00:06 GMT
• Title: AnimeCeleb: Large-Scale Animation CelebFaces Dataset via Controllable 3D Synthetic Models
• Authors: Kangyeol Kim, Sunghyun Park, Jaeseong Lee, Sunghyo Chung, Junsoo Lee, Jaegul Choo
• Abstract summary: We present a large-scale animation celebfaces dataset (AnimeCeleb) via controllable synthetic animation models. To facilitate the data generation process, we build a semi-automatic pipeline based on an open 3D software. This leads to constructing a large-scale animation face dataset that includes multi-pose and multi-style animation faces with rich annotations.
• Score: 19.6347170450874
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite remarkable success in deep learning-based face-related models, these models are still limited to the domain of real human faces. On the other hand, the domain of animation faces has been studied less intensively due to the absence of a well-organized dataset. In this paper, we present a large-scale animation celebfaces dataset (AnimeCeleb) via controllable synthetic animation models to boost research on the animation face domain. To facilitate the data generation process, we build a semi-automatic pipeline based on an open 3D software and a developed annotation system. This leads to constructing a large-scale animation face dataset that includes multi-pose and multi-style animation faces with rich annotations. Experiments suggest that our dataset is applicable to various animation-related tasks such as head reenactment and colorization.

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