# Diverse Plausible 360-Degree Image Outpainting for Efficient 3DCG Background Creation

arXiv: https://arxiv.org/abs/2203.14668 · HTML: https://mlsanity.com/paper/2203.14668/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 41 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [SynSin: End-to-end View Synthesis from a Single Image](https://mlsanity.com/paper/1912.08804/) (2019)
- [Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image](https://mlsanity.com/paper/2012.09855/) (2020)
- [Pluralistic Image Completion](https://mlsanity.com/paper/1903.04227/) (2019)
- [Learning to Predict Indoor Illumination from a Single Image](https://mlsanity.com/paper/1704.00090/) (2017)
- [High-Fidelity Pluralistic Image Completion with Transformers](https://mlsanity.com/paper/2103.14031/) (2021)
- [Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline](https://mlsanity.com/paper/2004.01179/) (2020)
- [Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting](https://mlsanity.com/paper/2005.09704/) (2020)
- [Neural Inverse Rendering of an Indoor Scene from a Single Image](https://mlsanity.com/paper/1901.02453/) (2019)
- [Geometry-Free View Synthesis: Transformers and no 3D Priors](https://mlsanity.com/paper/2104.07652/) (2021)
- [Coherent Semantic Attention for Image Inpainting](https://mlsanity.com/paper/1905.12384/) (2019)
- [Deep Parametric Indoor Lighting Estimation](https://mlsanity.com/paper/1910.08812/) (2019)
- [Learning Indoor Inverse Rendering with 3D Spatially-Varying Lighting](https://mlsanity.com/paper/2109.06061/) (2021)
- [Generative Face Completion](https://mlsanity.com/paper/1704.05838/) (2017)
- [Aligning Latent and Image Spaces to Connect the Unconnectable](https://mlsanity.com/paper/2104.06954/) (2021)
- [COCO-GAN: Generation by Parts via Conditional Coordinating](https://mlsanity.com/paper/1904.00284/) (2019)
- [DeepLight: Learning Illumination for Unconstrained Mobile Mixed Reality](https://mlsanity.com/paper/1904.01175/) (2019)
- [Boundless: Generative Adversarial Networks for Image Extension](https://mlsanity.com/paper/1908.07007/) (2019)
- [Neural Illumination: Lighting Prediction for Indoor Environments](https://mlsanity.com/paper/1906.07370/) (2019)
- [On Aliased Resizing and Surprising Subtleties in GAN Evaluation](https://mlsanity.com/paper/2104.11222/) (2021)
- [Very Long Natural Scenery Image Prediction by Outpainting](https://mlsanity.com/paper/1912.12688/) (2019)
- [HDR Environment Map Estimation for Real-Time Augmented Reality](https://mlsanity.com/paper/2011.10687/) (2020)

## Built on by (most cited first, 30 of 47)

- [Street-View Image Generation from a Bird's-Eye View Layout](https://mlsanity.com/paper/2301.04634/) (2023)
- [DiffusionLight: Light Probes for Free by Painting a Chrome Ball](https://mlsanity.com/paper/2312.09168/) (2023)
- [360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model](https://mlsanity.com/paper/2401.06578/) (2024)
- [PanoDiffusion: 360-degree Panorama Outpainting via Diffusion](https://mlsanity.com/paper/2307.03177/) (2023)
- [360-Degree Panorama Generation from Few Unregistered NFoV Images](https://mlsanity.com/paper/2308.14686/) (2023)
- [Deep Learning for Omnidirectional Vision: A Survey and New Perspectives](https://mlsanity.com/paper/2205.10468/) (2022)
- [CubeDiff: Repurposing Diffusion-Based Image Models for Panorama Generation](https://mlsanity.com/paper/2501.17162/) (2025)
- [Autoregressive Omni-Aware Outpainting for Open-Vocabulary 360-Degree Image Generation](https://mlsanity.com/paper/2309.03467/) (2023)
- [Guided Co-Modulated GAN for 360deg Field of View Extrapolation](https://mlsanity.com/paper/2204.07286/) (2022)
- [EverLight: Indoor-Outdoor Editable HDR Lighting Estimation](https://mlsanity.com/paper/2304.13207/) (2023)
- [Imagine360: Immersive 360 Video Generation from Perspective Anchor](https://mlsanity.com/paper/2412.03552/) (2024)
- [DreamSpace: Dreaming Your Room Space with Text-Driven Panoramic Texture Propagation](https://mlsanity.com/paper/2310.13119/) (2023)
- [DynamicScaler: Seamless and Scalable Video Generation for Panoramic Scenes](https://mlsanity.com/paper/2412.11100/) (2024)
- [PanSplat: 4K Panorama Synthesis with Feed-Forward Gaussian Splatting](https://mlsanity.com/paper/2412.12096/) (2024)
- [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://mlsanity.com/paper/2502.10444/) (2025)
- [DiT360: High-Fidelity Panoramic Image Generation via Hybrid Training](https://mlsanity.com/paper/2510.11712/) (2025)
- [What Makes for Text to 360-degree Panorama Generation with Stable Diffusion?](https://mlsanity.com/paper/2505.22129/) (2025)
- [Cylin-Painting: Seamless 360textdegree Panoramic Image Outpainting and Beyond](https://mlsanity.com/paper/2204.08563/) (2022)
- [Spatiotemporally Consistent HDR Indoor Lighting Estimation](https://mlsanity.com/paper/2305.04374/) (2023)
- [Taming Stable Diffusion for Text to 360deg Panorama Image Generation](https://mlsanity.com/paper/2404.07949/) (2024)
- [DreamCube: 3D Panorama Generation via Multi-plane Synchronization](https://mlsanity.com/paper/2506.17206/) (2025)
- [DiffusionLight-Turbo: Accelerated Light Probes for Free via Single-Pass Chrome Ball Inpainting](https://mlsanity.com/paper/2507.01305/) (2025)
- [Panorama Generation From NFoV Image Done Right](https://mlsanity.com/paper/2503.18420/) (2025)
- [CubeComposer: Spatio-Temporal Autoregressive 4K 360deg Video Generation from Perspective Video](https://mlsanity.com/paper/2603.04291/) (2026)
- [Conditional 360-degree Image Synthesis for Immersive Indoor Scene Decoration](https://mlsanity.com/paper/2307.09621/) (2023)
- [Dream360: Diverse and Immersive Outdoor Virtual Scene Creation via Transformer-Based 360 Image Outpainting](https://mlsanity.com/paper/2401.10564/) (2024)
- [Geometry Fidelity for Spherical Images](https://mlsanity.com/paper/2407.18207/) (2024)
- [Omni2: Unifying Omnidirectional Image Generation and Editing in an Omni Model](https://mlsanity.com/paper/2504.11379/) (2025)
- [360Anything: Geometry-Free Lifting of Images and Videos to 360deg](https://mlsanity.com/paper/2601.16192/) (2026)
- [CamFreeDiff: Camera-free Image to Panorama Generation with Diffusion Model](https://mlsanity.com/paper/2407.07174/) (2024)

## Abstract (arXiv)

We address the problem of generating a 360-degree image from a single image with a narrow field of view by estimating its surroundings. Previous methods suffered from overfitting to the training resolution and deterministic generation. This paper proposes a completion method using a transformer for scene modeling and novel methods to improve the properties of a 360-degree image on the output image. Specifically, we use CompletionNets with a transformer to perform diverse completions and AdjustmentNet to match color, stitching, and resolution with an input image, enabling inference at any resolution. To improve the properties of a 360-degree image on an output image, we also propose WS-perceptual loss and circular inference. Thorough experiments show that our method outperforms state-of-the-art (SOTA) methods both qualitatively and quantitatively. For example, compared to SOTA methods, our method completes images 16 times larger in resolution and achieves 1.7 times lower Frechet inception distance (FID). Furthermore, we propose a pipeline that uses the completion results for lighting and background of 3DCG scenes. Our plausible background completion enables perceptually natural results in the application of inserting virtual objects with specular surfaces.