Knowledge Externalization from Image-to-Image Translation
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 ones motion to an arbitrary animation head. Experiments demonstrate a 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 https://github.com/kangyeolk/AnimeCeleb. This work has been published in ECCV22.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 02, 2023
- Accession Number
- AD1205076
Entities
People
- Jaegul Choo
Organizations
- KAIST