mlsanityData to 14 Oct 2026

Lighting-Consistent Object Transfer Across Radiance Fields

arXiv 2606.22481 ↗First posted 21 Jun 2026cs.GR cs.CVcited by 0

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

3D Gaussian Splatting (3DGS) is widely used to capture and render real scenes. Compositing objects from one capture into another has applications in many domains, such as VFX, architecture and interior design, or marketing. However, extracting an object from a source scene and naively pasting it into a target scene will fail to produce realistic results due to the different lighting conditions between the two scenes. To address this problem, we introduce a diffusion model that harmonizes naively composited images with inconsistent lighting. The model is trained with a heterogeneous dataset of image pairs (inconsistent composite input, consistent output), combining synthetic, generated, and real data. Our complete 3D solution allows a user to extract an object from the source scene and composite it into the target scene. From this, the (inconsistent) views of the target scene with the composite object are rendered. Our diffusion model harmonizes each one of these views, which are finally consolidated in a 3DGS representation with a post-optimization step. Our method provides visually compelling results, making object transfer between 3DGS easy to use and significantly improving quality compared to previous methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 87 of its 104 references that are in the map. First posted on arXiv 21 Jun 2026.

Builds on

87 of 104 references.

Closest ancestors · 13

Same direction, newest first

  1. 2025MatSpray: Fusing 2D Material World Knowledge on 3D Geometry20 Dec
  2. 2025LightLab: Controlling Light Sources in Images with Diffusion Models14 May
  3. 2025TrajectoryCrafter: Redirecting Camera Trajectory for Monocular Videos via Diffusion Models7 Mar
  4. 2025Difix3D+: Improving 3D Reconstructions with Single-Step Diffusion Models3 Mar
  5. 20243DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with View-consistent 2D Diffusion Priors21 Oct
  6. 2024A Diffusion Approach to Radiance Field Relighting using Multi-Illumination Synthesis13 Sep
  7. 2024Neural Gaffer: Relighting Any Object via Diffusion11 Jun
  8. 2024CAT3D: Create Anything in 3D with Multi-View Diffusion Models16 May
  9. 2024RGBleftrightarrowX: Image decomposition and synthesis using material- and lighting-aware diffusion models1 May
  10. 2024InFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion Prior17 Apr
  11. 2024ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion27 Mar
  12. 2023ReconFusion: 3D Reconstruction with Diffusion Priors5 Dec
1 more
  1. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Models24 Jan

Other references · 74

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  6. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  7. 2023Segment Anythingcited by 8,234
  8. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  9. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  10. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  11. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  12. 2022Flow Matching for Generative Modelingcited by 5,702
38 more
  1. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  2. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  3. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  4. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  5. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
  6. 2024SAM 2: Segment Anything in Images and Videoscited by 2,863
  7. 2025Wan: Open and Advanced Large-Scale Video Generative Modelscited by 2,708
  8. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,032
  9. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,964
  10. 2024HunyuanVideo: A Systematic Framework For Large Video Generative Modelscited by 1,660
  11. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,613
  12. 2023IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Modelscited by 1,464
  13. 2018Demystifying MMD GANscited by 1,293
  14. 2025FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Spacecited by 1,006
  15. 2025Cosmos World Foundation Model Platform for Physical AIcited by 864
  16. 2023Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fieldscited by 671
  17. 2020GANSpace: Discovering Interpretable GAN Controlscited by 614
  18. 2021Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fieldscited by 575
  19. 2023LERF: Language Embedded Radiance Fieldscited by 543
  20. 2023DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Visioncited by 537
  21. 2022Paint by Example: Exemplar-based Image Editing with Diffusion Modelscited by 489
  22. 2023Instruct-NeRF2NeRF: Editing 3D Scenes with Instructionscited by 484
  23. 2023LangSplat: 3D Language Gaussian Splattingcited by 415
  24. 2023AnyDoor: Zero-shot Object-level Image Customizationcited by 412
  25. 2023Gaussian Grouping: Segment and Edit Anything in 3D Scenescited by 393
  26. 2023Segment Anything in High Qualitycited by 376
  27. 2023Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fieldscited by 364
  28. 2025GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Controlcited by 341
  29. 2023GS-IR: 3D Gaussian Splatting for Inverse Renderingcited by 224
  30. 2024Taming 3DGS: High-Quality Radiance Fields with Limited Resourcescited by 204
  31. 2022Panoptic Lifting for 3D Scene Understanding with Neural Fieldscited by 174
  32. 2023NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Imagescited by 163
  33. 2023Segment Any 3D Gaussianscited by 161
  34. 2019Seeing What a GAN Cannot Generatecited by 148
  35. 2024GARField: Group Anything with Radiance Fieldscited by 109
  36. 2023OmniSeg3D: Omniversal 3D Segmentation via Hierarchical Contrastive Learningcited by 75
  37. 2023Intrinsic Image Diffusion for Indoor Single-view Material Estimationcited by 73
  38. 2021Making Images Real Again: A Comprehensive Survey on Deep Image Compositioncited by 63

50 most cited of 74 All 74 (JSON)

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  1. 2025Diffusion Models are Secretly Zero-Shot 3DGS Harmonizers
  2. 2024Localized Gaussian Splatting Editing with Contextual Awareness
  3. 2025Generative Gaussian Splatting: Generating 3D Scenes with Video Diffusion Priors
  4. 2026LightBridge: Feed-Forward Generative Relighting for 3D Gaussian Splatting
  5. 2024GS-ID: Illumination Decomposition on Gaussian Splatting via Adaptive Light Aggregation and Diffusion-Guided Material Priors
  6. 2024Compact 3D Gaussian Splatting for Static and Dynamic Radiance Fields
  7. 2023Compact 3D Gaussian Representation for Radiance Field
  8. 2026Benchmarking Single-Step Inpainting Methods for Multi-Object 3D Gaussian Splatting Scenes