mlsanityData to 7 Oct 2026

Joint Shadow Generation and Relighting via Light-Geometry Interaction Maps

arXiv 2602.21820 ↗First posted 25 Feb 2026cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

We propose Light-Geometry Interaction (LGI) maps, a novel representation that encodes light-aware occlusion from monocular depth. Unlike ray tracing, which requires full 3D reconstruction, LGI captures essential light-shadow interactions reliably and accurately, computed from off-the-shelf 2.5D depth map predictions. LGI explicitly ties illumination direction to geometry, providing a physics-inspired prior that constrains generative models. Without such prior, these models often produce floating shadows, inconsistent illumination, and implausible shadow geometry. Building on this representation, we propose a unified pipeline for joint shadow generation and relighting - unlike prior methods that treat them as disjoint tasks - capturing the intrinsic coupling of illumination and shadowing essential for modeling indirect effects. By embedding LGI into a bridge-matching generative backbone, we reduce ambiguity and enforce physically consistent light-shadow reasoning. To enable effective training, we curated the first large-scale benchmark dataset for joint shadow and relighting, covering reflections, transparency, and complex interreflections. Experiments show significant gains in realism and consistency across synthetic and real images. LGI thus bridges geometry-inspired rendering with generative modeling, enabling efficient, physically consistent shadow generation and relighting.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 48 references that are in the map. First posted on arXiv 25 Feb 2026.

Builds on

26 of 48 references.

References · 26

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
  4. 2024Depth Anything V2cited by 1,385
  5. 2024RGBleftrightarrowX: Image decomposition and synthesis using material- and lighting-aware diffusion modelscited by 159
  6. 2024DiLightNet: Fine-grained Lighting Control for Diffusion-based Image Generationcited by 93
  7. 2019Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Datacited by 91
  8. 2022Learning-based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracingcited by 78
  9. 2024ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertioncited by 71
  10. 2024SwitchLight: Co-design of Physics-driven Architecture and Pre-training Framework for Human Portrait Relightingcited by 66
  11. 2024LightIt: Illumination Modeling and Control for Diffusion Modelscited by 56
  12. 2025LBM: Latent Bridge Matching for Fast Image-to-Image Translationcited by 36
14 more
  1. 2021Shadow Generation for Composite Image in Real-world Scenescited by 35
  2. 2022OutCast: Outdoor Single-image Relighting with Cast Shadowscited by 30
  3. 2024IllumiNeRF: 3D Relighting Without Inverse Renderingcited by 29
  4. 2020VIDIT: Virtual Image Dataset for Illumination Transfercited by 26
  5. 2020SSN: Soft Shadow Network for Image Compositingcited by 26
  6. 2022Controllable Shadow Generation Using Pixel Height Mapscited by 22
  7. 2023UrbanIR: Large-Scale Urban Scene Inverse Rendering from a Single Videocited by 22
  8. 2024Shadow Generation for Composite Image Using Diffusion modelcited by 17
  9. 2024ZeroComp: Zero-shot Object Compositing from Image Intrinsics via Diffusioncited by 16
  10. 2023PixHt-Lab: Pixel Height Based Light Effect Generation for Image Compositingcited by 15
  11. 2023Shadow Generation with Decomposed Mask Prediction and Attentive Shadow Fillingcited by 8
  12. 2024Retinex-Diffusion: On Controlling Illumination Conditions in Diffusion Models via Retinex Theorycited by 7
  13. 2023DESOBAv2: Towards Large-scale Real-world Dataset for Shadow Generationcited by 3
  14. 2024Controllable Shadow Generation with Single-Step Diffusion Models from Synthetic Datacited by 2

Built on by

Not cited yet.