In this paper, we present Export3D, a one-shot 3D-aware portrait animation
method that is able to control the facial expression and camera view of a given
portrait image. To achieve this, we introduce a tri-plane generator that
directly generates a tri-plane of 3D prior by transferring the expression
parameter of 3DMM into the source image. The tri-plane is then decoded into the
image of different view through a differentiable volume rendering. Existing
portrait animation methods heavily rely on image warping to transfer the
expression in the motion space, challenging on disentanglement of appearance
and expression. In contrast, we propose a contrastive pre-training framework
for appearance-free expression parameter, eliminating undesirable appearance
swap when transferring a cross-identity expression. Extensive experiments show
that our pre-training framework can learn the appearance-free expression
representation hidden in 3DMM, and our model can generate 3D-aware expression
controllable portrait image without appearance swap in the cross-identity
manner.

本文提出了一种一次性 3D 感知肖像动画方法 Export3D，通过介绍一个三平面生成器，将 3DMM 的表情参数转换成源图像的三平面，并通过可区分体素渲染将三平面解码为不同视角的图像，从而实现对给定肖像图像的面部表情和相机视角的控制。与现有的肖像动画方法不同的是，我们提出了一种对无外貌的表情参数进行对比预训练的框架，消除了在跨身份表情转换时不希望出现的外貌交换。广泛实验证明，我们的预训练框架能够学习隐藏在 3DMM 中的无外貌表情表示，我们的模型能够以跨身份的方式生成具有 3D 感知的表情可控肖像图像，而不会发生外貌交换。