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.

翻译：随着NeRF在面部重建中的广泛应用，现有方法已能通过单目视频恢复具备照片级真实感的3D头部虚拟形象。然而，基于NeRF的方法训练过程相当耗时，因为其所使用的MLP效率低下，需要大量迭代才能收敛。为解决此问题，我们提出AvatarMAV——一种利用运动感知神经体素的快速3D头部虚拟形象重建方法。AvatarMAV首次通过神经体素对头部虚拟形象的标准外观与解耦表情运动进行联合建模。具体而言，运动感知神经体素由多个4D张量的加权拼接生成，这些4D张量与3DMM表情基形成语义一一对应，并共享3DMM表情系数的权重。得益于这种创新表示，所提出的AvatarMAV可在5分钟内（基于纯PyTorch实现）恢复照片级真实感的头部虚拟形象，其速度显著优于当前最先进的面部重建方法。项目主页：https://www.liuyebin.com/avatarmav。