AI-Driven Stylization of 3D Environments
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Abstract (arXiv)
In this system, we discuss methods to stylize a scene of 3D primitive objects into a higher fidelity 3D scene using novel 3D representations like NeRFs and 3D Gaussian Splatting. Our approach leverages existing image stylization systems and image-to-3D generative models to create a pipeline that iteratively stylizes and composites 3D objects into scenes. We show our results on adding generated objects into a scene and discuss limitations.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 19 references that are in the map. First posted on arXiv 9 Nov 2024.
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5 of 19 references.
References · 5
- 2024GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generationcited by 301
- 2024CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Modelcited by 215
- 2022NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)cited by 158
- 2023Compositional 3D Scene Generation using Locally Conditioned Diffusioncited by 101
- 2024SIGNeRF: Scene Integrated Generation for Neural Radiance Fieldscited by 11
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