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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.
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