mlsanityData to 14 Oct 2026

PixHt-Lab: Pixel Height Based Light Effect Generation for Image Compositing

arXiv 2303.00137 ↗First posted 28 Feb 2023cs.CV cs.GRcited by 15

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Abstract (arXiv)

Lighting effects such as shadows or reflections are key in making synthetic images realistic and visually appealing. To generate such effects, traditional computer graphics uses a physically-based renderer along with 3D geometry. To compensate for the lack of geometry in 2D Image compositing, recent deep learning-based approaches introduced a pixel height representation to generate soft shadows and reflections. However, the lack of geometry limits the quality of the generated soft shadows and constrain reflections to pure specular ones. We introduce PixHt-Lab, a system leveraging an explicit mapping from pixel height representation to 3D space. Using this mapping, PixHt-Lab reconstructs both the cutout and background geometry and renders realistic, diverse, lighting effects for image compositing. Given a surface with physically-based materials, we can render reflections with varying glossiness. To generate more realistic soft shadows, we further propose to use 3D-aware buffer channels to guide a neural renderer. Both quantitative and qualitative evaluations demonstrate that PixHt-Lab significantly improves soft shadow generation.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 32 of its 74 references that are in the map. First posted on arXiv 28 Feb 2023.

Builds on

32 of 74 references.

References · 32

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20 more
  1. 2018Monocular Total Capture: Posing Face, Body, and Hands in the Wildcited by 162
  2. 2019Single Image Portrait Relightingcited by 152
  3. 2019DoveNet: Deep Image Harmonization via Domain Verificationcited by 135
  4. 2017Real-Time User-Guided Image Colorization with Learned Deep Priorscited by 123
  5. 2017Deep Image Harmonizationcited by 120
  6. 2021NeRF for Outdoor Scene Relightingcited by 114
  7. 2019DenseRaC: Joint 3D Pose and Shape Estimation by Dense Render-and-Comparecited by 100
  8. 2019Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Datacited by 92
  9. 2021Region-aware Adaptive Instance Normalization for Image Harmonizationcited by 71
  10. 2021SSH: A Self-Supervised Framework for Image Harmonizationcited by 60
  11. 2020Foreground-aware Semantic Representations for Image Harmonizationcited by 53
  12. 2021Free-viewpoint Indoor Neural Relighting from Multi-view Stereocited by 50
  13. 2017SurfNet: Generating 3D shape surfaces using deep residual networkscited by 32
  14. 2017Image2Mesh: A Learning Framework for Single Image 3D Reconstructioncited by 29
  15. 2020SSN: Soft Shadow Network for Image Compositingcited by 26
  16. 2022Controllable Shadow Generation Using Pixel Height Mapscited by 22
  17. 2020People as Scene Probescited by 14
  18. 2020Design and Deployment of Photo2Building: A Cloud-based Procedural Modeling Tool as a Servicecited by 6
  19. 2022Interactive Portrait Harmonizationcited by 2
  20. 2017Compact Model Representation for 3D Reconstructioncited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 8; 2025: 2; 2026: 3.

15 papers.

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