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.

Real3D-Potrait 是一种框架，通过使用大型图像到平面模型和高效的运动适配器，从而改进了一次性 3D 重建的能力，实现了精确的运动条件动画，并利用头 - 躯干 - 背景超分辨率模型生成具有自然躯干运动和可切换背景的逼真视频，同时支持一次性以音频驱动的说话脸生成。与以前的方法相比，广泛的实验证明 Real3D-Portrait 对于未见过的身份具有很好的泛化能力，并生成更逼真的说话肖像视频。