# SLIDE: Single Image 3D Photography with Soft Layering and Depth-aware Inpainting

arXiv: https://arxiv.org/abs/2109.01068 · HTML: https://mlsanity.com/paper/2109.01068/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 45 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 30 of 31)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [NeRD: Neural Reflectance Decomposition from Image Collections](https://mlsanity.com/paper/2012.03918/) (2020)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue](https://mlsanity.com/paper/1603.04992/) (2016)
- [SynSin: End-to-end View Synthesis from a Single Image](https://mlsanity.com/paper/1912.08804/) (2019)
- [Single-View View Synthesis with Multiplane Images](https://mlsanity.com/paper/2004.11364/) (2020)
- [DeepView: View Synthesis with Learned Gradient Descent](https://mlsanity.com/paper/1906.07316/) (2019)
- [Stereo Magnification: Learning View Synthesis using Multiplane Images](https://mlsanity.com/paper/1805.09817/) (2018)
- [3D Photography using Context-aware Layered Depth Inpainting](https://mlsanity.com/paper/2004.04727/) (2020)
- [Pushing the Boundaries of View Extrapolation with Multiplane Images](https://mlsanity.com/paper/1905.00413/) (2019)
- [DeepStereo: Learning to Predict New Views from the World's Imagery](https://mlsanity.com/paper/1506.06825/) (2015)
- [Single-Image Depth Perception in the Wild](https://mlsanity.com/paper/1604.03901/) (2016)
- [Learning-Based View Synthesis for Light Field Cameras](https://mlsanity.com/paper/1609.02974/) (2016)
- [3D Ken Burns Effect from a Single Image](https://mlsanity.com/paper/1909.05483/) (2019)
- [Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting](https://mlsanity.com/paper/2005.09704/) (2020)
- [Neural Rerendering in the Wild](https://mlsanity.com/paper/1904.04290/) (2019)
- [Learning the Depths of Moving People by Watching Frozen People](https://mlsanity.com/paper/1904.11111/) (2019)
- [Unsupervised Training for 3D Morphable Model Regression](https://mlsanity.com/paper/1806.06098/) (2018)
- [Coherent Semantic Attention for Image Inpainting](https://mlsanity.com/paper/1905.12384/) (2019)
- [Layer-structured 3D Scene Inference via View Synthesis](https://mlsanity.com/paper/1807.10264/) (2018)
- [Places: An Image Database for Deep Scene Understanding](https://mlsanity.com/paper/1610.02055/) (2016)
- [Learning to Synthesize a 4D RGBD Light Field from a Single Image](https://mlsanity.com/paper/1708.03292/) (2017)
- [F, B, Alpha Matting](https://mlsanity.com/paper/2003.07711/) (2020)
- [Learning Single Camera Depth Estimation using Dual-Pixels](https://mlsanity.com/paper/1904.05822/) (2019)
- [One Shot 3D Photography](https://mlsanity.com/paper/2008.12298/) (2020)

## Built on by (most cited first, 30 of 48)

- [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://mlsanity.com/paper/2410.02073/) (2024)
- [Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior](https://mlsanity.com/paper/2303.14184/) (2023)
- [NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360deg Views](https://mlsanity.com/paper/2211.16431/) (2022)
- [SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields](https://mlsanity.com/paper/2211.12254/) (2022)
- [InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images](https://mlsanity.com/paper/2207.11148/) (2022)
- [Single-View View Synthesis in the Wild with Learned Adaptive Multiplane Images](https://mlsanity.com/paper/2205.11733/) (2022)
- [Diffusion Handles: Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D](https://mlsanity.com/paper/2312.02190/) (2023)
- [3D Photo Stylization: Learning to Generate Stylized Novel Views from a Single Image](https://mlsanity.com/paper/2112.00169/) (2021)
- [Persistent Nature: A Generative Model of Unbounded 3D Worlds](https://mlsanity.com/paper/2303.13515/) (2023)
- [Reference-guided Controllable Inpainting of Neural Radiance Fields](https://mlsanity.com/paper/2304.09677/) (2023)
- [HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D](https://mlsanity.com/paper/2312.15980/) (2023)
- [3D Cinemagraphy from a Single Image](https://mlsanity.com/paper/2303.05724/) (2023)
- [Sharp Monocular View Synthesis in Less Than a Second](https://mlsanity.com/paper/2512.10685/) (2025)
- [Image Amodal Completion: A Survey](https://mlsanity.com/paper/2207.02062/) (2022)
- [Pano2Room: Novel View Synthesis from a Single Indoor Panorama](https://mlsanity.com/paper/2408.11413/) (2024)
- [3D Moments from Near-Duplicate Photos](https://mlsanity.com/paper/2205.06255/) (2022)
- [Tiled Multiplane Images for Practical 3D Photography](https://mlsanity.com/paper/2309.14291/) (2023)
- [SpatialDreamer: Self-supervised Stereo Video Synthesis from Monocular Input](https://mlsanity.com/paper/2411.11934/) (2024)
- [Bridging Implicit and Explicit Geometric Transformation for Single-Image View Synthesis](https://mlsanity.com/paper/2209.07105/) (2022)
- [Efficient-3DiM: Learning a Generalizable Single-image Novel-view Synthesizer in One Day](https://mlsanity.com/paper/2310.03015/) (2023)
- [SinMPI: Novel View Synthesis from a Single Image with Expanded Multiplane Images](https://mlsanity.com/paper/2312.11037/) (2023)
- [M2SVid: End-to-End Inpainting and Refinement for Monocular-to-Stereo Video Conversion](https://mlsanity.com/paper/2505.16565/) (2025)
- [Clutter Detection and Removal in 3D Scenes with View-Consistent Inpainting](https://mlsanity.com/paper/2304.03763/) (2023)
- [Learning 3D Photography Videos via Self-supervised Diffusion on Single Images](https://mlsanity.com/paper/2302.10781/) (2023)
- [SpatialMe: Stereo Video Conversion Using Depth-Warping and Blend-Inpainting](https://mlsanity.com/paper/2412.11512/) (2024)
- [Free-form 3D Scene Inpainting with Dual-stream GAN](https://mlsanity.com/paper/2212.08464/) (2022)
- [CodecNeRF: Toward Fast Encoding and Decoding, Compact, and High-quality Novel-view Synthesis](https://mlsanity.com/paper/2404.04913/) (2024)
- [Layered Depth Refinement with Mask Guidance](https://mlsanity.com/paper/2206.03048/) (2022)
- [Virtual Occlusions Through Implicit Depth](https://mlsanity.com/paper/2305.07014/) (2023)
- [Amodal Depth Anything: Amodal Depth Estimation in the Wild](https://mlsanity.com/paper/2412.02336/) (2024)

## Abstract (arXiv)

Single image 3D photography enables viewers to view a still image from novel viewpoints. Recent approaches combine monocular depth networks with inpainting networks to achieve compelling results. A drawback of these techniques is the use of hard depth layering, making them unable to model intricate appearance details such as thin hair-like structures. We present SLIDE, a modular and unified system for single image 3D photography that uses a simple yet effective soft layering strategy to better preserve appearance details in novel views. In addition, we propose a novel depth-aware training strategy for our inpainting module, better suited for the 3D photography task. The resulting SLIDE approach is modular, enabling the use of other components such as segmentation and matting for improved layering. At the same time, SLIDE uses an efficient layered depth formulation that only requires a single forward pass through the component networks to produce high quality 3D photos. Extensive experimental analysis on three view-synthesis datasets, in combination with user studies on in-the-wild image collections, demonstrate superior performance of our technique in comparison to existing strong baselines while being conceptually much simpler. Project page: https://varunjampani.github.io/slide