AvatarMAV: Fast 3D Head Avatar Reconstruction Using Motion-Aware Neural Voxels

• URL: http://arxiv.org/abs/2211.13206v3
• Date: Wed, 3 May 2023 06:47:11 GMT
• Title: AvatarMAV: Fast 3D Head Avatar Reconstruction Using Motion-Aware Neural Voxels
• Authors: Yuelang Xu, Lizhen Wang, Xiaochen Zhao, Hongwen Zhang, Yebin Liu
• Abstract summary: We propose AvatarMAV, a fast 3D head avatar reconstruction method using Motion-Aware Neural Voxels. AvatarMAV is the first to model both the canonical appearance and the decoupled expression motion by neural voxels for head avatar. The proposed AvatarMAV can recover photo-realistic head avatars in just 5 minutes, which is significantly faster than the state-of-the-art facial reenactment methods.
• Score: 33.085274792188756
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With NeRF widely used for facial reenactment, recent methods can recover photo-realistic 3D head avatar from just a monocular video. Unfortunately, the training process of the NeRF-based methods is quite time-consuming, as MLP used in the NeRF-based methods is inefficient and requires too many iterations to converge. To overcome this problem, we propose AvatarMAV, a fast 3D head avatar reconstruction method using Motion-Aware Neural Voxels. AvatarMAV is the first to model both the canonical appearance and the decoupled expression motion by neural voxels for head avatar. In particular, the motion-aware neural voxels is generated from the weighted concatenation of multiple 4D tensors. The 4D tensors semantically correspond one-to-one with 3DMM expression basis and share the same weights as 3DMM expression coefficients. Benefiting from our novel representation, the proposed AvatarMAV can recover photo-realistic head avatars in just 5 minutes (implemented with pure PyTorch), which is significantly faster than the state-of-the-art facial reenactment methods. Project page: https://www.liuyebin.com/avatarmav.

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