# Shadow Generation for Composite Image in Real-world Scenes

arXiv: https://arxiv.org/abs/2104.10338 · HTML: https://mlsanity.com/paper/2104.10338/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 54 references that are in the map.
- First posted on arXiv 21 Apr 2021.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [cGANs with Projection Discriminator](https://mlsanity.com/paper/1802.05637/) (2018)
- [Learning to Predict Indoor Illumination from a Single Image](https://mlsanity.com/paper/1704.00090/) (2017)
- [DoveNet: Deep Image Harmonization via Domain Verification](https://mlsanity.com/paper/1911.13239/) (2019)
- [Deep Image Harmonization](https://mlsanity.com/paper/1703.00069/) (2017)
- [Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN](https://mlsanity.com/paper/1911.08718/) (2019)
- [Shadow Removal via Shadow Image Decomposition](https://mlsanity.com/paper/1908.08628/) (2019)
- [Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data](https://mlsanity.com/paper/1903.10683/) (2019)
- [ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing](https://mlsanity.com/paper/1803.01837/) (2018)
- [Deep Parametric Indoor Lighting Estimation](https://mlsanity.com/paper/1910.08812/) (2019)
- [Improving the Harmony of the Composite Image by Spatial-Separated Attention Module](https://mlsanity.com/paper/1907.06406/) (2019)
- [Spatial Fusion GAN for Image Synthesis](https://mlsanity.com/paper/1812.05840/) (2018)
- [GP-GAN: Towards Realistic High-Resolution Image Blending](https://mlsanity.com/paper/1703.07195/) (2017)
- [Deep Image Blending](https://mlsanity.com/paper/1910.11495/) (2019)
- [From Shadow Segmentation to Shadow Removal](https://mlsanity.com/paper/2008.00267/) (2020)
- [Learning a Discriminative Model for the Perception of Realism in Composite Images](https://mlsanity.com/paper/1510.00477/) (2015)
- [BargainNet: Background-Guided Domain Translation for Image Harmonization](https://mlsanity.com/paper/2009.09169/) (2020)
- [All-Weather Deep Outdoor Lighting Estimation](https://mlsanity.com/paper/1906.04909/) (2019)
- [Instance Shadow Detection](https://mlsanity.com/paper/1911.07034/) (2019)
- [Automatic Scene Inference for 3D Object Compositing](https://mlsanity.com/paper/1912.12297/) (2019)
- [RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal](https://mlsanity.com/paper/1911.09178/) (2019)
- [Conditional Image-to-Image Translation](https://mlsanity.com/paper/1805.00251/) (2018)
- [Adversarial Image Composition with Auxiliary Illumination](https://mlsanity.com/paper/2009.08255/) (2020)

## Built on by (most cited first, 30 of 35)

- [TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition](https://mlsanity.com/paper/2307.12493/) (2023)
- [ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion](https://mlsanity.com/paper/2403.18818/) (2024)
- [IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representation](https://mlsanity.com/paper/2403.10701/) (2024)
- [Making Images Real Again: A Comprehensive Survey on Deep Image Composition](https://mlsanity.com/paper/2106.14490/) (2021)
- [ObjectStitch: Generative Object Compositing](https://mlsanity.com/paper/2212.00932/) (2022)
- [Collage Diffusion](https://mlsanity.com/paper/2303.00262/) (2023)
- [Does FLUX Already Know How to Perform Physically Plausible Image Composition?](https://mlsanity.com/paper/2509.21278/) (2025)
- [Latent Feature-Guided Diffusion Models for Shadow Removal](https://mlsanity.com/paper/2312.02156/) (2023)
- [Tuning-Free Image Customization with Image and Text Guidance](https://mlsanity.com/paper/2403.12658/) (2024)
- [Instance Shadow Detection with A Single-Stage Detector](https://mlsanity.com/paper/2207.04614/) (2022)
- [Controllable Shadow Generation Using Pixel Height Maps](https://mlsanity.com/paper/2207.05385/) (2022)
- [Learning Object Placement via Dual-path Graph Completion](https://mlsanity.com/paper/2207.11464/) (2022)
- [PrimeComposer: Faster Progressively Combined Diffusion for Image Composition with Attention Steering](https://mlsanity.com/paper/2403.05053/) (2024)
- [DreamCom: Finetuning Text-guided Inpainting Model for Image Composition](https://mlsanity.com/paper/2309.15508/) (2023)
- [Shadow Generation for Composite Image Using Diffusion model](https://mlsanity.com/paper/2403.15234/) (2024)
- [ObjectMate: A Recurrence Prior for Object Insertion and Subject-Driven Generation](https://mlsanity.com/paper/2412.08645/) (2024)
- [Cross-domain Compositing with Pretrained Diffusion Models](https://mlsanity.com/paper/2302.10167/) (2023)
- [Fast Object Placement Assessment](https://mlsanity.com/paper/2205.14280/) (2022)
- [ORIDa: Object-centric Real-world Image Composition Dataset](https://mlsanity.com/paper/2506.08964/) (2025)
- [Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.08865/) (2024)
- [Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning Era](https://mlsanity.com/paper/2409.02108/) (2024)
- [Foreground Object Search by Distilling Composite Image Feature](https://mlsanity.com/paper/2308.04990/) (2023)
- [MetaShadow: Object-Centered Shadow Detection, Removal, and Synthesis](https://mlsanity.com/paper/2412.02635/) (2024)
- [Structure-Informed Shadow Removal Networks](https://mlsanity.com/paper/2301.03182/) (2023)
- [DESOBAv2: Towards Large-scale Real-world Dataset for Shadow Generation](https://mlsanity.com/paper/2308.09972/) (2023)
- [Animating Street View](https://mlsanity.com/paper/2310.08534/) (2023)
- [Spatial Transformation for Image Composition via Correspondence Learning](https://mlsanity.com/paper/2207.02398/) (2022)
- [Refracting Reality: Generating Images with Realistic Transparent Objects](https://mlsanity.com/paper/2511.17340/) (2025)
- [FastInstShadow: A Simple Query-Based Model for Instance Shadow Detection](https://mlsanity.com/paper/2503.07517/) (2025)
- [Embedding Physical Reasoning into Diffusion-Based Shadow Generation Under the Sun and Sky](https://mlsanity.com/paper/2512.06174/) (2025)

## Abstract (arXiv)

Image composition targets at inserting a foreground object into a background image. Most previous image composition methods focus on adjusting the foreground to make it compatible with background while ignoring the shadow effect of foreground on the background. In this work, we focus on generating plausible shadow for the foreground object in the composite image. First, we contribute a real-world shadow generation dataset DESOBA by generating synthetic composite images based on paired real images and deshadowed images. Then, we propose a novel shadow generation network SGRNet, which consists of a shadow mask prediction stage and a shadow filling stage. In the shadow mask prediction stage, foreground and background information are thoroughly interacted to generate foreground shadow mask. In the shadow filling stage, shadow parameters are predicted to fill the shadow area. Extensive experiments on our DESOBA dataset and real composite images demonstrate the effectiveness of our proposed method. Our dataset and code are available at https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBA.