We present a method that reconstructs and animates a 3D head avatar from a
single-view portrait image. Existing methods either involve time-consuming
optimization for a specific person with multiple images, or they struggle to
synthesize intricate appearance details beyond the facial region. To address
these limitations, we propose a framework that not only generalizes to unseen
identities based on a single-view image without requiring person-specific
optimization, but also captures characteristic details within and beyond the
face area (e.g. hairstyle, accessories, etc.). At the core of our method are
three branches that produce three tri-planes representing the coarse 3D
geometry, detailed appearance of a source image, as well as the expression of a
target image. By applying volumetric rendering to the combination of the three
tri-planes followed by a super-resolution module, our method yields a high
fidelity image of the desired identity, expression and pose. Once trained, our
model enables efficient 3D head avatar reconstruction and animation via a
single forward pass through a network. Experiments show that the proposed
approach generalizes well to unseen validation datasets, surpassing SOTA
baseline methods by a large margin on head avatar reconstruction and animation.

该研究提出了一种通过对单视角幅面图像进行重建和动画化的三维头像，通过三个三角面分别捕捉源图像的粗略三维几何，详细外观以及目标图像的表情实现，再通过超分辨率模块的渲染进行填充并通过单向传递网络的高效性进行动画。实验表明，该方法在重建和动画方面优于当前最先进的基线方法，并且对新的验证数据集有很好的泛化能力。