BlenderFusion: 3D-Grounded Visual Editing and Generative Compositing
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912565319172096 |
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| author | Chen, Jiacheng Mehran, Ramin Jia, Xuhui Xie, Saining Woo, Sanghyun |
| author_facet | Chen, Jiacheng Mehran, Ramin Jia, Xuhui Xie, Saining Woo, Sanghyun |
| contents | We present BlenderFusion, a generative visual compositing framework that synthesizes new scenes by recomposing objects, camera, and background. It follows a layering-editing-compositing pipeline: (i) segmenting and converting visual inputs into editable 3D entities (layering), (ii) editing them in Blender with 3D-grounded control (editing), and (iii) fusing them into a coherent scene using a generative compositor (compositing). Our generative compositor extends a pre-trained diffusion model to process both the original (source) and edited (target) scenes in parallel. It is fine-tuned on video frames with two key training strategies: (i) source masking, enabling flexible modifications like background replacement; (ii) simulated object jittering, facilitating disentangled control over objects and camera. BlenderFusion significantly outperforms prior methods in complex compositional scene editing tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17450 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BlenderFusion: 3D-Grounded Visual Editing and Generative Compositing Chen, Jiacheng Mehran, Ramin Jia, Xuhui Xie, Saining Woo, Sanghyun Computer Vision and Pattern Recognition Graphics We present BlenderFusion, a generative visual compositing framework that synthesizes new scenes by recomposing objects, camera, and background. It follows a layering-editing-compositing pipeline: (i) segmenting and converting visual inputs into editable 3D entities (layering), (ii) editing them in Blender with 3D-grounded control (editing), and (iii) fusing them into a coherent scene using a generative compositor (compositing). Our generative compositor extends a pre-trained diffusion model to process both the original (source) and edited (target) scenes in parallel. It is fine-tuned on video frames with two key training strategies: (i) source masking, enabling flexible modifications like background replacement; (ii) simulated object jittering, facilitating disentangled control over objects and camera. BlenderFusion significantly outperforms prior methods in complex compositional scene editing tasks. |
| title | BlenderFusion: 3D-Grounded Visual Editing and Generative Compositing |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2506.17450 |