InstantStyleGaussian: Efficient Art Style Transfer with 3D Gaussian Splatting
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We present InstantStyleGaussian, an innovative 3D style transfer method based on the 3D Gaussian Splatting (3DGS) scene representation. By inputting a target-style image, it quickly generates new 3D GS scenes. Our method operates on pre-reconstructed GS scenes, combining diffusion models with an improved iterative dataset update strategy. It utilizes diffusion models to generate target style images, adds these new images to the training dataset, and uses this dataset to iteratively update and optimize the GS scenes, significantly accelerating the style editing process while ensuring the quality of the generated scenes. Extensive experimental results demonstrate that our method ensures high-quality stylized scenes while offering significant advantages in style transfer speed and consistency.
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Cited by 2 Pith papers
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Boosting Zero-Shot 3D Style Transfer with 2D Pre-trained Priors
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.
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A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation
A survey that categorizes and summarizes methods applying 3D Gaussian Splatting to segmentation, editing, generation, and related tasks, including datasets and evaluation protocols.
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