StyleSplat: 3D Object Style Transfer with Gaussian Splatting

Overview of StyleSplat: 3D Object Style Transfer with Gaussian Splatting

"StyleSplat: 3D Object Style Transfer with Gaussian Splatting" introduces a novel technique aimed at efficient and precise stylization of 3D objects within scenes. This methodology builds upon advancements in radiance field generation and 3D Gaussian splatting (3DGS) to offer a lightweight and customizable approach to 3D object style transfer.

Significant progress in neural radiance fields (NeRF) has previously enhanced the fidelity and realism of 3D scene representation. Traditional methods such as NeRF-based techniques exhibit slow rendering times, limiting their practicality for real-time applications. Conversely, 3D Gaussian splatting, a more recent approach, allows for fast training and high-quality rendering, making it a suitable candidate for interactive applications in areas like gaming, virtual reality, and digital art.

StyleSplat addresses the limitations of existing techniques which often lack the capacity for localized style transfer. Typical existing methods either apply styles globally or struggle with localizing the style application accurately. To resolve this, StyleSplat combines the strengths of 3D Gaussian splatting with a segmentation-based approach to achieve precise and efficient stylization of multiple objects within a scene, each potentially stylized differently.

Methodology

The proposed approach consists of three primary stages:

1. 2D Mask Generation and Object Tracking: Utilizing off-the-shelf image segmentation and tracking models (e.g., SAM and DEVA) to generate temporally coherent 2D masks across a sequence of images. These masks play a critical role in the subsequent segmentation and training of 3D Gaussians.

2. 3D Gaussian Training and Segmentation: In this phase, the method employs a joint optimization process to segment 3D Gaussians into distinct objects based on the 2D masks. This segmentation is achieved by augmenting each Gaussian with a compact feature vector, which is optimized similarly to the spherical harmonic coefficients. The feature vectors are then classified to provide object labels, enabling accurate selection and distinction of objects within the 3D scene.

3. 3D Style Transfer: Leveraging a nearest-neighbor feature matching (NNFM) loss, the method finetunes the spherical harmonic (SH) coefficients of the selected Gaussians. The NNFM loss ensures that the stylization remains consistent with the reference style image and localizes the style transfer to user-specified objects. This stage is computationally efficient, taking less than a minute to complete on a modern GPU after the initial training and segmentation.

Results and Evaluation

StyleSplat demonstrates its capability through a range of qualitative evaluations on diverse real-world scenes, showing effective localization of style transfer to specific objects. The paper presents various examples where different artistic styles are applied to individual objects within a scene, maintaining high fidelity to the original geometry and texture. The implementation details reveal that StyleSplat achieves significant efficiency, rendering at speeds exceeding 100 FPS, making it highly suitable for real-time applications.

The method also addresses potential issues of artifact leakage from 2D mask inconsistency by utilizing robust 3D segmentation, which eliminates common pitfalls of 2D-based approaches. By maintaining view-consistent feature vectors, the approach effectively prevents unintended stylization of adjacent objects or regions.

Implications and Future Work

Practically, StyleSplat can significantly enhance workflows in industries requiring rapid and flexible 3D asset customization, such as digital content creation for gaming and virtual reality environments. The ability to apply different styles to multiple objects within a scene opens new possibilities for creative expression and efficient asset production.

Theoretically, this research contributes to the ongoing development of advanced radiance field representations and real-time rendering techniques. It opens avenues for further exploration of Gaussian-based methods and their applications in various domains of computer graphics.

Future work might focus on addressing the limitations highlighted, such as resolving geometric artifacts arising from initial 3DGS reconstruction and refining view-specific segmentation masks. Expanding the approach to incorporate dynamic or temporally varying styles could also be a fruitful direction, paving the way for even richer interactive applications.

By integrating precision, efficiency, and flexibility, StyleSplat provides a robust framework that pushes the boundaries of current 3D object style transfer methodologies, fostering new creative possibilities in digital content creation and interactive media.