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

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.