StyleSplat: 3D Object Style Transfer with Gaussian Splatting
Sahil Jain, Avik Kuthiala, Prabhdeep Singh Sethi, Prakanshul Saxena

TL;DR
StyleSplat is a fast, flexible method for applying artistic styles to specific objects within 3D scenes represented by Gaussian splatting, enabling localized and customizable style transfer.
Contribution
It introduces a novel Gaussian splatting-based approach that allows quick, localized, and multi-object style transfer in 3D scenes, addressing speed and localization limitations of prior methods.
Findings
Enables real-time style transfer for 3D objects
Supports multiple styles within a single scene
Demonstrates high-quality stylization across diverse scenes
Abstract
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…
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Taxonomy
TopicsHuman Pose and Action Recognition · Generative Adversarial Networks and Image Synthesis · Advanced Image and Video Retrieval Techniques
