VOODOO 3D: Volumetric Portrait Disentanglement for One-Shot 3D Head Reenactment

• URL: http://arxiv.org/abs/2312.04651v1
• Date: Thu, 7 Dec 2023 19:19:57 GMT
• Title: VOODOO 3D: Volumetric Portrait Disentanglement for One-Shot 3D Head Reenactment
• Authors: Phong Tran, Egor Zakharov, Long-Nhat Ho, Anh Tuan Tran, Liwen Hu, Hao Li
• Abstract summary: We present a 3D-aware one-shot head reenactment method based on a fully neural disentanglement framework for source appearance and driver expressions. Our method is real-time and produces high-fidelity and view-consistent output, suitable for 3D teleconferencing systems based on holographic displays.
• Score: 17.372274738231443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a 3D-aware one-shot head reenactment method based on a fully volumetric neural disentanglement framework for source appearance and driver expressions. Our method is real-time and produces high-fidelity and view-consistent output, suitable for 3D teleconferencing systems based on holographic displays. Existing cutting-edge 3D-aware reenactment methods often use neural radiance fields or 3D meshes to produce view-consistent appearance encoding, but, at the same time, they rely on linear face models, such as 3DMM, to achieve its disentanglement with facial expressions. As a result, their reenactment results often exhibit identity leakage from the driver or have unnatural expressions. To address these problems, we propose a neural self-supervised disentanglement approach that lifts both the source image and driver video frame into a shared 3D volumetric representation based on tri-planes. This representation can then be freely manipulated with expression tri-planes extracted from the driving images and rendered from an arbitrary view using neural radiance fields. We achieve this disentanglement via self-supervised learning on a large in-the-wild video dataset. We further introduce a highly effective fine-tuning approach to improve the generalizability of the 3D lifting using the same real-world data. We demonstrate state-of-the-art performance on a wide range of datasets, and also showcase high-quality 3D-aware head reenactment on highly challenging and diverse subjects, including non-frontal head poses and complex expressions for both source and driver.

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