Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesis

Published: 16 Jan 2024, Last Modified: 10 Mar 2024ICLR 2024 spotlightEveryoneRevisionsBibTeX
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Keywords: One-shot Talking Face Generation, Neural Radiance Field
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TL;DR: We present a zero-shot NeRF-based talking face video system that could generate 3D avatar with realistic torso movement and supports both audio/video-driven applications.
Abstract: One-shot 3D talking portrait generation aims to reconstruct a 3D avatar from an unseen image, and then animate it with a reference video or audio to generate a talking portrait video. The existing methods fail to simultaneously achieve the goals of accurate 3D avatar reconstruction and stable talking face animation. Besides, while the existing works mainly focus on synthesizing the head part, it is also vital to generate natural torso and background segments to obtain a realistic talking portrait video. To address these limitations, we present Real3D-Potrait, a framework that (1) improves the one-shot 3D reconstruction power with a large image-to-plane model that distills 3D prior knowledge from a 3D face generative model; (2) facilitates accurate motion-conditioned animation with an efficient motion adapter; (3) synthesizes realistic video with natural torso movement and switchable background using a head-torso-background super-resolution model; and (4) supports one-shot audio-driven talking face generation with a generalizable audio-to-motion model. Extensive experiments show that Real3D-Portrait generalizes well to unseen identities and generates more realistic talking portrait videos compared to previous methods. Video samples are available at
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Primary Area: generative models
Submission Number: 62