mlsanityData to 7 Oct 2026

MetaShadow: Object-Centered Shadow Detection, Removal, and Synthesis

arXiv 2412.02635 ↗First posted 3 Dec 2024cs.CVcited by 5

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Abstract (arXiv)

Shadows are often under-considered or even ignored in image editing applications, limiting the realism of the edited results. In this paper, we introduce MetaShadow, a three-in-one versatile framework that enables detection, removal, and controllable synthesis of shadows in natural images in an object-centered fashion. MetaShadow combines the strengths of two cooperative components: Shadow Analyzer, for object-centered shadow detection and removal, and Shadow Synthesizer, for reference-based controllable shadow synthesis. Notably, we optimize the learning of the intermediate features from Shadow Analyzer to guide Shadow Synthesizer to generate more realistic shadows that blend seamlessly with the scene. Extensive evaluations on multiple shadow benchmark datasets show significant improvements of MetaShadow over the existing state-of-the-art methods on object-centered shadow detection, removal, and synthesis. MetaShadow excels in image-editing tasks such as object removal, relocation, and insertion, pushing the boundaries of object-centered image editing.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 40 of its 67 references that are in the map. First posted on arXiv 3 Dec 2024.

Builds on

40 of 67 references.

References · 40

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  4. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
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  7. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
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  11. 2022Paint by Example: Exemplar-based Image Editing with Diffusion Modelscited by 489
  12. 2023AnyDoor: Zero-shot Object-level Image Customizationcited by 411
28 more
  1. 2017Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removalcited by 160
  2. 2019Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GANcited by 102
  3. 2022ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removalcited by 102
  4. 2019Shadow Removal via Shadow Image Decompositioncited by 96
  5. 2019Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Datacited by 91
  6. 2024ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertioncited by 71
  7. 2022DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Networkcited by 68
  8. 2021Making Images Real Again: A Comprehensive Survey on Deep Image Compositioncited by 63
  9. 2018Direction-aware Spatial Context Features for Shadow Detection and Removalcited by 58
  10. 2017Direction-aware Spatial Context Features for Shadow Detectioncited by 56
  11. 2021From Shadow Generation to Shadow Removalcited by 54
  12. 2019ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removalcited by 51
  13. 2022CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Trainingcited by 49
  14. 2021CANet: A Context-Aware Network for Shadow Removalcited by 47
  15. 2017A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuationcited by 39
  16. 2019Instance Shadow Detectioncited by 37
  17. 2021Shadow Generation for Composite Image in Real-world Scenescited by 35
  18. 2023ShadowFormer: Global Context Helps Image Shadow Removalcited by 35
  19. 2019Revisiting Shadow Detection: A New Benchmark Dataset for Complex Worldcited by 34
  20. 2020SSN: Soft Shadow Network for Image Compositingcited by 26
  21. 2022Instance Shadow Detection with A Single-Stage Detectorcited by 22
  22. 2022Controllable Shadow Generation Using Pixel Height Mapscited by 22
  23. 2023SILT: Shadow-aware Iterative Label Tuning for Learning to Detect Shadows from Noisy Labelscited by 19
  24. 2024Shadow Generation for Composite Image Using Diffusion modelcited by 17
  25. 2023PixHt-Lab: Pixel Height Based Light Effect Generation for Image Compositingcited by 15
  26. 2024Regional Attention for Shadow Removalcited by 12
  27. 2015Shadow Optimization from Structured Deep Edge Detectioncited by 11
  28. 2020No Shadow Left Behind: Removing Objects and their Shadows using Approximate Lighting and Geometrycited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025OmniPaint: Mastering Object-Oriented Editing via Disentangled Insertion-Removal Inpaintingcited by 38
  2. 2025Cast and Attached Shadow Detection via Iterative Light and Geometry Reasoningcited by 0
  3. 2026MultiShadow: Multi-Object Shadow Generation for Image Compositing via Diffusion Modelcited by 0
  4. 2026You Only Erase Once: Erasing Anything without Bringing Unexpected Contentcited by 0
  5. 2026PredErase: Training-Free Object-and-Effect Removal with Predictive Latent Guidancecited by 0