Head avatar reconstruction, crucial for applications in virtual reality,
online meetings, gaming, and film industries, has garnered substantial
attention within the computer vision community. The fundamental objective of
this field is to faithfully recreate the head avatar and precisely control
expressions and postures. Existing methods, categorized into 2D-based warping,
mesh-based, and neural rendering approaches, present challenges in maintaining
multi-view consistency, incorporating non-facial information, and generalizing
to new identities. In this paper, we propose a framework named GPAvatar that
reconstructs 3D head avatars from one or several images in a single forward
pass. The key idea of this work is to introduce a dynamic point-based
expression field driven by a point cloud to precisely and effectively capture
expressions. Furthermore, we use a Multi Tri-planes Attention (MTA) fusion
module in the tri-planes canonical field to leverage information from multiple
input images. The proposed method achieves faithful identity reconstruction,
precise expression control, and multi-view consistency, demonstrating promising
results for free-viewpoint rendering and novel view synthesis.

通过引入一个由点云驱动的动态基于点的表情场，以及在三平面规范场中使用多三平面注意力（MTA）融合模块来利用多个输入图像的信息，我们提出了一种名为 GPAvatar 的框架，可以在单次前向传递中从一个或多个图像中重建 3D 头像，实现忠实的身份重建，精确的表情控制和多视角一致性，并展示了自由视点渲染和新视角合成的有 promising 结果。