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Portrait4D-v2: Pseudo Multi-View Data Creates Better 4D Head Synthesizer
March 21, 2024, 4:45 a.m. | Yu Deng, Duomin Wang, Baoyuan Wang
cs.CV updates on arXiv.org arxiv.org
Abstract: In this paper, we propose a novel learning approach for feed-forward one-shot 4D head avatar synthesis. Different from existing methods that often learn from reconstructing monocular videos guided by 3DMM, we employ pseudo multi-view videos to learn a 4D head synthesizer in a data-driven manner, avoiding reliance on inaccurate 3DMM reconstruction that could be detrimental to the synthesis performance. The key idea is to first learn a 3D head synthesizer using synthetic multi-view images to …
abstract arxiv avatar cs.cv data data-driven head learn novel paper synthesis type videos view
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