3DStyle-Diffusion: Pursuing Fine-grained Text-driven 3D Stylization with 2D Diffusion Models (2311.05464v1)

Abstract: 3D content creation via text-driven stylization has played a fundamental challenge to multimedia and graphics community. Recent advances of cross-modal foundation models (e.g., CLIP) have made this problem feasible. Those approaches commonly leverage CLIP to align the holistic semantics of stylized mesh with the given text prompt. Nevertheless, it is not trivial to enable more controllable stylization of fine-grained details in 3D meshes solely based on such semantic-level cross-modal supervision. In this work, we propose a new 3DStyle-Diffusion model that triggers fine-grained stylization of 3D meshes with additional controllable appearance and geometric guidance from 2D Diffusion models. Technically, 3DStyle-Diffusion first parameterizes the texture of 3D mesh into reflectance properties and scene lighting using implicit MLP networks. Meanwhile, an accurate depth map of each sampled view is achieved conditioned on 3D mesh. Then, 3DStyle-Diffusion leverages a pre-trained controllable 2D Diffusion model to guide the learning of rendered images, encouraging the synthesized image of each view semantically aligned with text prompt and geometrically consistent with depth map. This way elegantly integrates both image rendering via implicit MLP networks and diffusion process of image synthesis in an end-to-end fashion, enabling a high-quality fine-grained stylization of 3D meshes. We also build a new dataset derived from Objaverse and the evaluation protocol for this task. Through both qualitative and quantitative experiments, we validate the capability of our 3DStyle-Diffusion. Source code and data are available at \url{https://github.com/yanghb22-fdu/3DStyle-Diffusion-Official}.

Insights into "3DStyle-Diffusion: Pursuing Fine-grained Text-driven 3D Stylization with 2D Diffusion Models"

The paper 3DStyle-Diffusion: Pursuing Fine-grained Text-driven 3D Stylization with 2D Diffusion Models presents a significant advancement in 3D content creation, specifically focusing on fine-grained 3D stylization driven by text prompts. This research exploits the capabilities of 2D diffusion models to enhance the detail and quality of 3D stylizations, addressing challenges inherent in existing methods relying exclusively on semantic-level cross-modal models like CLIP.

Background and Motivation

Text-driven 3D content creation has been a particularly challenging task within multimedia and graphics domains, mainly because of the intricate gap between textual descriptions and the corresponding visual appearances of 3D meshes. Previous methodologies attempted to bridge this gap using cross-modal foundation models; however, they fell short in delivering precise stylization of fine-grained details. Recognizing these challenges, this paper proposes a novel model, the 3DStyle-Diffusion, which effectively combines 3D parameterization with the diffusion process for more controllable stylization.

Technical Approach

The essence of the 3DStyle-Diffusion model lies in its integration of 3D texture parameterization and geometric information through implicit MLP networks. It leverages a pre-trained 2D diffusion model to guide the rendering of images, thereby aligning the synthesized image from different viewpoints with the text prompt and ensuring consistency with the associated depth map. The rendered image becomes a bridge, linking semantic alignment with geometric consistency.

Notably, the model capitalizes on implicit neural representations to parameterize the mesh texture in terms of reflectance and lighting properties. As the mesh is rendered from various viewpoints, precise depth maps augment the model's understanding, conditional on a pre-trained controllable 2D Diffusion model. This integration allows the diffusion model not only to synthesize images aligned with textual input but also to respect and maintain inherent geometric details, ensuring superior quality of the generated 3D stylized models.

Results and Evaluation

The authors develop a new dataset derived from Objaverse, complimented with a novel evaluation protocol to benchmark the model's performance against other state-of-the-art approaches. Through comprehensive qualitative and quantitative experiments, 3DStyle-Diffusion demonstrates impressive capability in achieving fine-grained text-driven stylization. It surpasses its predecessors, not only aligning rendered outputs with text descriptions but also preserving fine-grained visual details such as specific textures and complex geometries.

Implications and Future Directions

This work advances the boundary of 3D multimedia content creation, offering a refined approach for text-driven stylization tasks. The integration of 2D diffusion models introduces a promising direction, highlighting how combining cross-modal text understanding with geometrically aware diffusion processes can produce high-fidelity 3D visuals with greater detail and accuracy.

In the field of practical applications, this method can significantly benefit areas such as video game design, virtual reality environments, and digital art. Future research could extend this approach to support more intricate scenarios, such as dynamic scenes and environments with moving parts, or integrate more sophisticated machine learning techniques to enhance the robustness of text-to-image alignment further.

By bridging traditional semantic alignment methods with the geometric precision of diffusion models, 3DStyle-Diffusion sets a new course for advancements in AI-driven 3D content creation, promising more controllable and detailed 3D world construction.