PixHt-Lab: Pixel Height Based Light Effect Generation for Image Compositing
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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
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Citing papers per year: 2023: 2; 2024: 8; 2025: 2; 2026: 3.
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- 2023DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Visioncited by 537
- 2024IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representationcited by 64
- 2024Shadow Generation for Composite Image Using Diffusion modelcited by 17
- 2024ZeroComp: Zero-shot Object Compositing from Image Intrinsics via Diffusioncited by 16
- 2023GlobalMapper: Arbitrary-Shaped Urban Layout Generationcited by 15
- 2024Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning Eracited by 7
- 2024Refine-by-Align: Reference-Guided Artifacts Refinement through Semantic Alignmentcited by 5
- 2024MetaShadow: Object-Centered Shadow Detection, Removal, and Synthesiscited by 5
- 2024Floating No More: Object-Ground Reconstruction from a Single Imagecited by 3
- 2024All-frequency Full-body Human Image Relightingcited by 3
- 2025Refracting Reality: Generating Images with Realistic Transparent Objectscited by 2
- 2025Shoe Style-Invariant and Ground-Aware Learning for Dense Foot Contact Estimationcited by 1
- 2026VSDiffusion: Taming Ill-Posed Shadow Generation via Visibility-Constrained Diffusion9 Mar 2026
- 2026MultiShadow: Multi-Object Shadow Generation for Image Compositing via Diffusion Model3 Mar 2026
- 2026Joint Shadow Generation and Relighting via Light-Geometry Interaction Maps25 Feb 2026
- 2025Shoe Style-Invariant and Ground-Aware Learning for Dense Foot Contact Estimation27 Nov 2025
- 2025Refracting Reality: Generating Images with Realistic Transparent Objects21 Nov 2025
- 2024MetaShadow: Object-Centered Shadow Detection, Removal, and Synthesis3 Dec 2024
- 2024Refine-by-Align: Reference-Guided Artifacts Refinement through Semantic Alignment30 Nov 2024
- 2024All-frequency Full-body Human Image Relighting1 Nov 2024
- 2024ZeroComp: Zero-shot Object Compositing from Image Intrinsics via Diffusion10 Oct 2024
- 2024Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning Era3 Sep 2024
- 2024Floating No More: Object-Ground Reconstruction from a Single Image26 Jul 2024
- 2024Shadow Generation for Composite Image Using Diffusion model22 Mar 2024
- 2023DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Visioncited by 537
- 2024IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representationcited by 64
- 2024ZeroComp: Zero-shot Object Compositing from Image Intrinsics via Diffusioncited by 16
- 2023GlobalMapper: Arbitrary-Shaped Urban Layout Generationcited by 15
- 2024Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning Eracited by 7
- 2024Refine-by-Align: Reference-Guided Artifacts Refinement through Semantic Alignmentcited by 5
- 2024MetaShadow: Object-Centered Shadow Detection, Removal, and Synthesiscited by 5
- 2024Floating No More: Object-Ground Reconstruction from a Single Imagecited by 3
- 2024All-frequency Full-body Human Image Relightingcited by 3
- 2025Refracting Reality: Generating Images with Realistic Transparent Objectscited by 2
- 2025Shoe Style-Invariant and Ground-Aware Learning for Dense Foot Contact Estimationcited by 1
- 2026Joint Shadow Generation and Relighting via Light-Geometry Interaction Mapscited by 0
12 most cited of 13 All 13 (JSON)
- 2026VSDiffusion: Taming Ill-Posed Shadow Generation via Visibility-Constrained Diffusion9 Mar 2026
- 2026Joint Shadow Generation and Relighting via Light-Geometry Interaction Maps25 Feb 2026
- 2025Shoe Style-Invariant and Ground-Aware Learning for Dense Foot Contact Estimation27 Nov 2025
- 2025Refracting Reality: Generating Images with Realistic Transparent Objects21 Nov 2025
- 2024MetaShadow: Object-Centered Shadow Detection, Removal, and Synthesis3 Dec 2024
- 2024Refine-by-Align: Reference-Guided Artifacts Refinement through Semantic Alignment30 Nov 2024
- 2024All-frequency Full-body Human Image Relighting1 Nov 2024
- 2024ZeroComp: Zero-shot Object Compositing from Image Intrinsics via Diffusion10 Oct 2024
- 2024Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning Era3 Sep 2024
- 2024Floating No More: Object-Ground Reconstruction from a Single Image26 Jul 2024
- 2024IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representation15 Mar 2024
- 2023DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision26 Dec 2023
12 newest of 13 All 13 (JSON)