3D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score Distillation (2311.09571v1)

Abstract: In this work we develop 3D Paintbrush, a technique for automatically texturing local semantic regions on meshes via text descriptions. Our method is designed to operate directly on meshes, producing texture maps which seamlessly integrate into standard graphics pipelines. We opt to simultaneously produce a localization map (to specify the edit region) and a texture map which conforms to it. This synergistic approach improves the quality of both the localization and the stylization. To enhance the details and resolution of the textured area, we leverage multiple stages of a cascaded diffusion model to supervise our local editing technique with generative priors learned from images at different resolutions. Our technique, referred to as Cascaded Score Distillation (CSD), simultaneously distills scores at multiple resolutions in a cascaded fashion, enabling control over both the granularity and global understanding of the supervision. We demonstrate the effectiveness of 3D Paintbrush to locally texture a variety of shapes within different semantic regions. Project page: https://threedle.github.io/3d-paintbrush

• The paper presents a dual-map approach that uses cascaded score distillation to achieve precise local edits on 3D meshes.
• It employs a cascaded diffusion model to concurrently refine both localization maps and texture detail for high fidelity results.
• The method integrates text-based guidance with neural field representations, enabling scalable and application-ready stylization in graphics pipelines.

An In-Depth Review of "3D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score Distillation"

The paper "3D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score Distillation" introduces a novel method designed to address the challenge of local editing on 3D meshes through automatically texturing discrete regions using text descriptions. Unlike many existing methodologies, this approach directly works on meshes and outputs texture maps, making it integration-ready with standard graphics pipelines.

Technical Approach

The 3D Paintbrush employs a dual-focused approach where a localization map and a texture map are generated concurrently. This method is enhanced by harnessing a cascaded diffusion model, realized in a technique termed Cascaded Score Distillation (CSD). By distilling scores across multiple resolutions in a cascaded manner, this method enables nuanced control over both the refinement and holistic integration of the supervised edits. This dual-map strategy not only improves the fidelity of stylized regions but also enhances localization precision, mitigating unnecessary modifications outside the target areas.

Key to this method is its interaction mechanism using neural fields encoded by multi-layer perceptrons (MLPs), allowing the system to operate in a triangle-agnostic and resolution-independent manner. This results in the flexible and scalable synthesis of detailed textures and fine-grained localization masks. Additionally, the method gives users intuitive input avenues through free-form text descriptions, providing wide usability for a variety of mesh forms.

Numerical and Practical Implications

The numerical results, although not explicitly quantified in terms of error rates or computational complexity, suggest high fidelity in localization and styling. Throughout numerous examples provided, the technique consistently demonstrates the capability to produce precise, richly detailed local textures, a feat that presents significant implications for applications in fields such as video games, movies, and VR/AR development.

The CSD method presents a significant advancement in utilizing cascaded diffusion models. By incorporating outputs from multiple model stages into the optimization process, it is a commendable approach to leveraging modern high-resolution model architectures for enhanced detail and style accuracy. Compared to earlier Score Distillation Sampling (SDS) methodologies, which limit operations to lower-resolution generative models, CSD’s capability to navigate and optimize across a spectrum of resolutions promises superior results.

Implications and Future Work

The applicability of 3D Paintbrush is far-reaching. It opens doors for more intricate control in artistic domains, allowing for the dynamic and expressive stylization of digital assets. Moreover, the finer accuracy in localization depicted by the method can greatly influence processes such as image segmentation, reconstruction, and detail enhancement in 3D modeling.

Furthermore, the research indicates potential avenues for extending localization capabilities beyond texturing alone, suggesting realms such as 3D geometry deformation or material property modification. Additionally, the generalizable aspects of CSD bear promise for broader applications, notably in fields requiring multi-resolution input handling, such as high-fidelity video processing and photorealistic rendering.

In conclusion, this paper introduces a substantial evolution in 3D local stylization through a robust integration of multi-resolution generative model outputs. Its methodology underlines a significant shift in approaching local 3D edits and opens numerous pathways for future exploration and improvement in computational graphics.