We present a one-shot method to infer and render a photorealistic 3D
representation from a single unposed image (e.g., face portrait) in real-time.
Given a single RGB input, our image encoder directly predicts a canonical
triplane representation of a neural radiance field for 3D-aware novel view
synthesis via volume rendering. Our method is fast (24 fps) on consumer
hardware, and produces higher quality results than strong GAN-inversion
baselines that require test-time optimization. To train our triplane encoder
pipeline, we use only synthetic data, showing how to distill the knowledge from
a pretrained 3D GAN into a feedforward encoder. Technical contributions include
a Vision Transformer-based triplane encoder, a camera data augmentation
strategy, and a well-designed loss function for synthetic data training. We
benchmark against the state-of-the-art methods, demonstrating significant
improvements in robustness and image quality in challenging real-world
settings. We showcase our results on portraits of faces (FFHQ) and cats (AFHQ),
but our algorithm can also be applied in the future to other categories with a
3D-aware image generator.

该论文提出了一种快速、高质量的单张图像推断和呈现真实感三维重建的方法，使用基于视觉 Transformer 的三面板编码器，利用合成数据进行训练，通过卷积渲染技术对神经辐射场的规范三面板进行重建，可以在具有 3D 感知图像生成器的其他类别中应用。