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arxiv: 2407.09473 · v1 · pith:HLUUCHYTnew · submitted 2024-07-12 · 💻 cs.CV

StyleSplat: 3D Object Style Transfer with Gaussian Splatting

classification 💻 cs.CV
keywords styleobjectstransferscenesstylesplatassetsgaussiangaussians
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Recent advancements in radiance fields have opened new avenues for creating high-quality 3D assets and scenes. Style transfer can enhance these 3D assets with diverse artistic styles, transforming creative expression. However, existing techniques are often slow or unable to localize style transfer to specific objects. We introduce StyleSplat, a lightweight method for stylizing 3D objects in scenes represented by 3D Gaussians from reference style images. Our approach first learns a photorealistic representation of the scene using 3D Gaussian splatting while jointly segmenting individual 3D objects. We then use a nearest-neighbor feature matching loss to finetune the Gaussians of the selected objects, aligning their spherical harmonic coefficients with the style image to ensure consistency and visual appeal. StyleSplat allows for quick, customizable style transfer and localized stylization of multiple objects within a scene, each with a different style. We demonstrate its effectiveness across various 3D scenes and styles, showcasing enhanced control and customization in 3D creation.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Structured 3D Latents Are Surprisingly Powerful: Unleashing Generalizable Style with 2D Diffusion

    cs.CV 2026-05 unverdicted novelty 6.0

    DiLAST optimizes 3D latents via guidance from a 2D diffusion model to enable generalizable style transfer for OOD styles in 3D asset generation.

  2. Boosting Zero-Shot 3D Style Transfer with 2D Pre-trained Priors

    cs.CV 2026-05 unverdicted novelty 5.0

    DS-StyleGaussian integrates a 2D-pretrained decoder with feature Gaussian splatting and deferred stylization to achieve view-consistent zero-shot 3D style transfer from a single style image.

  3. A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation

    cs.CV 2025-08 unverdicted novelty 3.0

    A survey that categorizes and summarizes methods applying 3D Gaussian Splatting to segmentation, editing, generation, and related tasks, including datasets and evaluation protocols.