InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images
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Cited by 24 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We present a method for learning to generate unbounded flythrough videos of natural scenes starting from a single view, where this capability is learned from a collection of single photographs, without requiring camera poses or even multiple views of each scene. To achieve this, we propose a novel self-supervised view generation training paradigm, where we sample and rendering virtual camera trajectories, including cyclic ones, allowing our model to learn stable view generation from a collection of single views. At test time, despite never seeing a video during training, our approach can take a single image and generate long camera trajectories comprised of hundreds of new views with realistic and diverse content. We compare our approach with recent state-of-the-art supervised view generation methods that require posed multi-view videos and demonstrate superior performance and synthesis quality.
Key facts. Cited by 82 papers in the map (data through 7 Oct 2026). Builds on 73 of its 94 references that are in the map. First posted on arXiv 22 Jul 2022.
Builds on
73 of 94 references.
References · 73
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- 2020Neural Sparse Voxel Fieldscited by 959
- 2020GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fieldscited by 784
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- 2021IBRNet: Learning Multi-View Image-Based Renderingcited by 698
- 2020GRAF: Generative Radiance Fields for 3D-Aware Image Synthesiscited by 688
- 2018Free-Form Image Inpainting with Gated Convolutioncited by 594
- 2018Generative Image Inpainting with Contextual Attentioncited by 578
- 2017MoCoGAN: Decomposing Motion and Content for Video Generationcited by 523
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- 2016Generating Videos with Scene Dynamicscited by 376
- 2019SynSin: End-to-end View Synthesis from a Single Imagecited by 364
- 2019SinGAN: Learning a Generative Model from a Single Natural Imagecited by 348
- 2017"Zero-Shot" Super-Resolution using Deep Internal Learningcited by 285
- 2020Softmax Splatting for Video Frame Interpolationcited by 276
- 2020Single-View View Synthesis with Multiplane Imagescited by 263
- 2016Unsupervised Learning for Physical Interaction through Video Predictioncited by 256
- 2019DeepView: View Synthesis with Learned Gradient Descentcited by 253
- 2018Stochastic Video Generation with a Learned Priorcited by 244
- 2020Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Imagecited by 239
- 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 236
- 2020Free View Synthesiscited by 231
- 2021Large Scale Image Completion via Co-Modulated Generative Adversarial Networkscited by 219
- 2020Swapping Autoencoder for Deep Image Manipulationcited by 215
- 2018Stereo Magnification: Learning View Synthesis using Multiplane Imagescited by 206
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- 2017Decomposing Motion and Content for Natural Video Sequence Predictioncited by 168
- 2021A Good Image Generator Is What You Need for High-Resolution Video Synthesiscited by 162
- 2021Unconstrained Scene Generation with Locally Conditioned Radiance Fieldscited by 130
- 20193D Ken Burns Effect from a Single Imagecited by 128
- 2018Learning to Decompose and Disentangle Representations for Video Predictioncited by 120
- 2021Pathdreamer: A World Model for Indoor Navigationcited by 117
- 2021Geometry-Free View Synthesis: Transformers and no 3D Priorscited by 99
- 2021GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worldscited by 98
- 2018Extreme View Synthesiscited by 94
- 2019High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networkscited by 88
50 most cited of 73 All 73 (JSON)
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Citing papers per year: 2022: 5; 2023: 24; 2024: 20; 2025: 28; 2026: 5.
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- 2025CameraCtrl II: Dynamic Scene Exploration via Camera-controlled Video Diffusion Models13 Mar 2025
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12 newest of 54 All 54 (JSON)