SinGRAF: Learning a 3D Generative Radiance Field for a Single Scene
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Abstract (arXiv)
Generative models have shown great promise in synthesizing photorealistic 3D objects, but they require large amounts of training data. We introduce SinGRAF, a 3D-aware generative model that is trained with a few input images of a single scene. Once trained, SinGRAF generates different realizations of this 3D scene that preserve the appearance of the input while varying scene layout. For this purpose, we build on recent progress in 3D GAN architectures and introduce a novel progressive-scale patch discrimination approach during training. With several experiments, we demonstrate that the results produced by SinGRAF outperform the closest related works in both quality and diversity by a large margin.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 47 of its 60 references that are in the map. First posted on arXiv 30 Nov 2022.
Builds on
47 of 60 references.
References · 47
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35 more
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- 2019Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representationscited by 864
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- 2018Which Training Methods for GANs do actually Converge?cited by 679
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- 2021StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generationcited by 289
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- 2021ATISS: Autoregressive Transformers for Indoor Scene Synthesiscited by 200
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- 2021CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesiscited by 149
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- 2021Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesiscited by 140
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- 2021Unconstrained Scene Generation with Locally Conditioned Radiance Fieldscited by 130
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- 2022EpiGRAF: Rethinking training of 3D GANscited by 108
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- 2021Diverse Generation from a Single Video Made Possiblecited by 13
- 2022SinGRAV: Learning a Generative Radiance Volume from a Single Natural Scenecited by 8
Built on by
Citing papers per year: 2023: 6; 2024: 2; 2025: 3.
11 papers.
- 2023Single Motion Diffusioncited by 65
- 2023CC3D: Layout-Conditioned Generation of Compositional 3D Scenescited by 58
- 2023Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shapecited by 29
- 2023GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fieldscited by 26
- 2023Efficient 3D Articulated Human Generation with Layered Surface Volumescited by 13
- 2025ShapeShifter: 3D Variations Using Multiscale and Sparse Point-Voxel Diffusioncited by 3
- 2024Prim2Room: Layout-Controllable Room Mesh Generation from Primitivescited by 2
- 2023GOEmbed: Gradient Origin Embeddings for Representation Agnostic 3D Feature Learningcited by 1
- 2024Towards Practical Single-shot Motion Synthesiscited by 1
- 2025Controllable Single-shot Animation Blending with Temporal Conditioningcited by 1
- 2025LPA3D: 3D Room-Level Scene Generation from In-the-Wild Imagescited by 0
- 2025Controllable Single-shot Animation Blending with Temporal Conditioning25 Aug 2025
- 2025LPA3D: 3D Room-Level Scene Generation from In-the-Wild Images3 Apr 2025
- 2025ShapeShifter: 3D Variations Using Multiscale and Sparse Point-Voxel Diffusion4 Feb 2025
- 2024Prim2Room: Layout-Controllable Room Mesh Generation from Primitives9 Sep 2024
- 2024Towards Practical Single-shot Motion Synthesis3 Jun 2024
- 2023GOEmbed: Gradient Origin Embeddings for Representation Agnostic 3D Feature Learning14 Dec 2023
- 2023Efficient 3D Articulated Human Generation with Layered Surface Volumes11 Jul 2023
- 2023GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields9 Jun 2023
- 2023Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape24 May 2023
- 2023CC3D: Layout-Conditioned Generation of Compositional 3D Scenes21 Mar 2023
- 2023Single Motion Diffusion12 Feb 2023