In this paper, we propose HeadNeRF, a novel NeRF-based parametric head model
that integrates the neural radiance field to the parametric representation of
the human head. It can render high fidelity head images in real-time on modern
GPUs, and supports directly controlling the generated images' rendering pose
and various semantic attributes. Different from existing related parametric
models, we use the neural radiance fields as a novel 3D proxy instead of the
traditional 3D textured mesh, which makes that HeadNeRF is able to generate
high fidelity images. However, the computationally expensive rendering process
of the original NeRF hinders the construction of the parametric NeRF model. To
address this issue, we adopt the strategy of integrating 2D neural rendering to
the rendering process of NeRF and design novel loss terms. As a result, the
rendering speed of HeadNeRF can be significantly accelerated, and the rendering
time of one frame is reduced from 5s to 25ms. The well designed loss terms also
improve the rendering accuracy, and the fine-level details of the human head,
such as the gaps between teeth, wrinkles, and beards, can be represented and
synthesized by HeadNeRF. Extensive experimental results and several
applications demonstrate its effectiveness. The trained parametric model is
available at this https URL

提出一种名为 HeadNeRF 的新人头模型，该模型利用神经辐射场来代替传统的 3D 网格，生成高保真的头部图像，并通过设计新的损失函数和将 2D 神经渲染与 NeRF 的渲染过程相结合，提高了渲染速度和准确性。