Generative Multiplane Images: Making a 2D GAN 3D-Aware
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Abstract (arXiv)
What is really needed to make an existing 2D GAN 3D-aware? To answer this question, we modify a classical GAN, i.e., StyleGANv2, as little as possible. We find that only two modifications are absolutely necessary: 1) a multiplane image style generator branch which produces a set of alpha maps conditioned on their depth; 2) a pose-conditioned discriminator. We refer to the generated output as a 'generative multiplane image' (GMPI) and emphasize that its renderings are not only high-quality but also guaranteed to be view-consistent, which makes GMPIs different from many prior works. Importantly, the number of alpha maps can be dynamically adjusted and can differ between training and inference, alleviating memory concerns and enabling fast training of GMPIs in less than half a day at a resolution of 10242. Our findings are consistent across three challenging and common high-resolution datasets, including FFHQ, AFHQv2, and MetFaces.
Key facts. Cited by 57 papers in the map (data through 14 Oct 2026). Builds on 54 of its 66 references that are in the map. First posted on arXiv 21 Jul 2022.
Builds on
54 of 66 references.
References · 54
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- 2023Articulated 3D Head Avatar Generation using Text-to-Image Diffusion Modelscited by 6
- 20233D-CLFusion: Fast Text-to-3D Rendering with Contrastive Latent Diffusioncited by 4
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- 2024Denoising Diffusion via Image-Based Rendering5 Feb 2024
- 2023Articulated 3D Head Avatar Generation using Text-to-Image Diffusion Models10 Jul 2023
- 20233D-CLFusion: Fast Text-to-3D Rendering with Contrastive Latent Diffusion21 Mar 2023
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- 2024One-shot Generative Domain Adaptation in 3D GANs11 Oct 2024
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