InstantStyleGaussian: Efficient Art Style Transfer with 3D Gaussian Splatting
Abstract
Introducing InstantStyleGaussian, a cutting-edge 3D style transfer method built upon the 3DGS scene representation.
By inputting a target-style image, it quickly generates new 3DGS scenes.
Through our approach, we work with previously reconstructed ground-sensing (GS) scenes. Our solution integrates diffusion models with an enhanced iterative dataset update mechanism.
By employing diffusion models, it can produce images in a specific artistic style, which are then incorporated into the training dataset. This dataset is subsequently utilized for iterative refinement and optimization of GS scenes, greatly enhancing the style editing process while maintaining scene quality.
Experimental results consistently confirm the effectiveness of our approach in generating high-fidelity stylized outputs, while delivering notable improvements in style transfer speed and consistency.
Figure
Figure 1
InstantStyleGaussian represents an innovative 3D style transfer technique. Upon receiving a target style image, editing may begin, which permits swift and consistent style transformations across various viewpoints.
The experimental section presents extra scenes in different viewpoints in accordance with the stylization process.
Figure 2
The InstantStyleGaussian algorithm repeatedly modifies a selected portion of the GS dataset images to refine and reassemble the GS scenes.
(1)capture rendered images from the reconstructed scene,
Utilize these images and the specified style image through InstantStyle to create new artworks in the specified style.
(3)add the new images to the training dataset,
(4) continuously iterate to update and optimize the GS scenes.
Figure 3
Qualitative Evaluation.
Unlike StyleGaussian, our approach offers a compelling alternative by showcasing a remarkable capability in terms of style preservation. It not only achieves a closer alignment with the reference aesthetic but also ensures that the original material's integrity is maintained with greater precision.
Figure 4
Without experiencing NNFM loss, the style transfer process’s quality significantly diminishes and fails to preserve the multi-view consistency.
Figure 5
Enhancing the number of iterations is subject to causing overfitting of textures in local regions.
Figure 6
Stylistic modifications are resulted in additional scenarios. Such instances further highlight the flexibility and efficacy of our method across diverse environments.
LIMITATIONS
Our method is mainly dedicated to scene surface texture editing, rendering other modifications ineffective. For example, eliminating existing entities from the scene via segmentation can produce notable artifacts because it necessitates retaining original positional data.
Similarly, adding new objects to the scene is challenging as placing them accurately in the specified positions is difficult.
We concur that incorporating and deleting content will likewise be achievable with the emergence of more advanced image processing diffusion models.
In addition, InstantStyleGaussian tunes the color attributes within the Gaussian function by retaining its geometric structure, thereby rendering it inadequate for style transfer that entails geometric deformations.
Introducing geometric transformations into style transfer represents a promising avenue for future research. It may necessitate the implementation of more robust supervision mechanisms.
CONCLUSION
This paper presented InstantStyleGaussian , an innovative approach to 3D style transfer, capable of rapidly generating new 3D GS scenes by specifying a target style image.
This method functions on reconstructed GS scenes, incorporating diffusion models into an enhanced Iterative Dataset Update strategy.
Fast style transfer is achieved through this approach, without sacrificing rapid rendering performance, and ensuring consistent views across multiple perspectives.
We showcased high-quality results in different game scenarios. Our method could have applications in game development areas, virtual reality technologies, and augmented reality implementations.