Lighting-Consistent Object Transfer Across Radiance Fields
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
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- 2024A Diffusion Approach to Radiance Field Relighting using Multi-Illumination Synthesis13 Sep
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- 2024CAT3D: Create Anything in 3D with Multi-View Diffusion Models16 May
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- 2024InFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion Prior17 Apr
- 2024ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion27 Mar
- 2023ReconFusion: 3D Reconstruction with Diffusion Priors5 Dec
Other references · 74
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- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
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- 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
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- 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,613
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- 2018Demystifying MMD GANscited by 1,293
- 2025FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Spacecited by 1,006
- 2025Cosmos World Foundation Model Platform for Physical AIcited by 864
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- 2019Seeing What a GAN Cannot Generatecited by 148
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- 2021Making Images Real Again: A Comprehensive Survey on Deep Image Compositioncited by 63
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