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

TL;DR
Coherent3D introduces a fusion-based approach that combines a canonical 3D prior with per-frame appearance to produce temporally consistent and realistic 3D portrait videos from a single image, advancing telepresence technology.
Contribution
The paper presents a novel fusion method that integrates a 3D prior with dynamic appearance, achieving state-of-the-art 3D reconstruction and temporal consistency from synthetic training data.
Findings
Achieves high-quality 3D reconstruction with temporal stability.
Performs well on both in-studio and in-the-wild datasets.
Outperforms existing methods in realism and consistency.
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…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image Processing Techniques and Applications · Advanced Vision and Imaging
