- Qualitative Comparisons: We compare our method against other state-of-the-art (SOTA) approaches: StyleGaussian (Liu et al., 2024), IGS2GS (IGS2GS Reference), and Ref-NPR (Zhang et al., 2023) on three datasets (Chair, Hotdog, and Mic) using three styles: Cartoon, Sky Painting, and Fire. + Qualitative Comparisons: We compare our method against other state-of-the-art (SOTA) approaches: StyleGaussian (Liu et al., 2024), IGS2GS (IGS2GS Reference), and Ref-NPR (Zhang et al., 2023) on three datasets (Chair, Hotdog, and Mic) using three styles: Cartoon, Sky Painting, and Fire.
The horizontal axis represents the compared methods, while the vertical axis displays different datasets. Our method effectively preserves the original model details, including: @@ -650,6 +650,41 @@
+ Below are some related works that were introduced around the same time as ours. +
++ StyleGaussian: Instant 3D Style Transfer with Gaussian Splatting (Liu et al., 2024) introduces an approach for fast 3D style transfer using Gaussian splatting techniques. +
++ Instruct-GS2GS: Editing 3D Gaussian Splats with Instructions (Vachha & Haque, 2024) presents a framework for editing 3D Gaussian splats through natural language instructions. +
++ Styleshot: A Snapshot on Any Style (Gao et al., 2024) explores the ability to apply arbitrary styles to 3D objects using a snapshot-based approach. +
++ Ip-adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models (Ye et al., 2023) introduces an image prompt adapter for enhancing text-to-image models. +
++ Instantstyle: Free Lunch Towards Style-Preserving in Text-to-Image Generation (Wang et al., 2024) proposes a method for maintaining style consistency in text-to-image generation. +
++ 3D Gaussian Splatting for Real-Time Radiance Field Rendering (Kerbl et al., 2023) investigates real-time rendering techniques for 3D Gaussian splatting in radiance fields. +
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