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.

中文翻译：

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学习为 3D 感知表达可控肖像动画生成条件三平面

在本文中，我们提出了 Export3D，这是一种一次性 3D 感知肖像动画方法，能够控制给定肖像图像的面部表情和相机视图。为了实现这一目标，我们引入了一个三平面生成器，它通过将 3DMM 的表达参数传递到源图像中来直接生成 3D 先验的三平面。然后通过可微体积渲染将三平面解码为不同视图的图像。现有的肖像动画方法严重依赖图像扭曲来转移运动空间中的表情，这对外观和表情的解开提出了挑战。相比之下，我们提出了一种用于无外观表达参数的对比预训练框架，消除了传输跨身份表达时不需要的外观交换。大量实验表明，我们的预训练框架可以学习隐藏在 3DMM 中的无外观表情表示，并且我们的模型可以以跨身份方式生成 3D 感知表情可控肖像图像，而无需外观交换。