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- W4368755705 abstract "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." @default.
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- W4368755705 date "2023-05-03" @default.
- W4368755705 modified "2023-09-29" @default.
- W4368755705 title "Real-Time Radiance Fields for Single-Image Portrait View Synthesis" @default.
- W4368755705 doi "https://doi.org/10.48550/arxiv.2305.02310" @default.
- W4368755705 hasPublicationYear "2023" @default.
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