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arXiv · 2412.08684

Coherent3D: Coherent 3D Portrait Video Reconstruction via Triplane Fusion

Shengze Wang·Xueting Li·Chao Liu·Matthew Chan·Michael Stengel·Henry Fuchs·Shalini De Mello·Koki Nagano
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Abstract

Recent breakthroughs in single-image 3D portrait reconstruction have enabled telepresence systems to stream 3D portrait videos from a single camera in real-time, democratizing telepresence. However, per-frame 3D reconstruction exhibits temporal inconsistency and forgets the user's appearance. On the other hand, self-reenactment methods can render coherent 3D portraits by driving a 3D avatar built from a single reference image, but fail to faithfully preserve the user's per-frame appearance (e.g., instantaneous facial expression and lighting). As a result, none of these two frameworks is an ideal solution for democratized 3D telepresence. In this work, we address this dilemma and propose a novel solution that maintains both coherent identity and dynamic per-frame appearance to enable the best possible realism. To this end, we propose a new fusion-based method that takes the best of both worlds by fusing a canonical 3D prior from a reference view with dynamic appearance from per-frame input views, producing temporally stable 3D videos with faithful reconstruction of the user's per-frame appearance. Trained only using synthetic data produced by an expression-conditioned 3D GAN, our encoder-based method achieves both state-of-the-art 3D reconstruction and temporal consistency on in-studio and in-the-wild datasets. https://research.nvidia.com/labs/amri/projects/coherent3d

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Shengze Wang, Xueting Li, Chao Liu, Matthew Chan, Michael Stengel, Henry Fuchs, Shalini De Mello, Koki Nagano. 2024-12-11. Coherent3D: Coherent 3D Portrait Video Reconstruction via Triplane Fusion. https://arxiv.org/abs/2412.08684

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