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ZIGNeRF: Zero-shot 3D Scene Representation with Invertible Generative Neural Radiance Fields

arXiv 2306.02741 ↗First posted 5 Jun 2023cs.CVcited by 0

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Abstract (arXiv)

Generative Neural Radiance Fields (NeRFs) have demonstrated remarkable proficiency in synthesizing multi-view images by learning the distribution of a set of unposed images. Despite the aptitude of existing generative NeRFs in generating 3D-consistent high-quality random samples within data distribution, the creation of a 3D representation of a singular input image remains a formidable challenge. In this manuscript, we introduce ZIGNeRF, an innovative model that executes zero-shot Generative Adversarial Network (GAN) inversion for the generation of multi-view images from a single out-of-domain image. The model is underpinned by a novel inverter that maps out-of-domain images into the latent code of the generator manifold. Notably, ZIGNeRF is capable of disentangling the object from the background and executing 3D operations such as 360-degree rotation or depth and horizontal translation. The efficacy of our model is validated using multiple real-image datasets: Cats, AFHQ, CelebA, CelebA-HQ, and CompCars.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 46 of its 54 references that are in the map. First posted on arXiv 5 Jun 2023.

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References · 46

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34 more
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