Text-guided Controllable Mesh Refinement for Interactive 3D Modeling

Core Concepts

A novel fast method for adding high-quality geometric details to coarse 3D meshes using text guidance and multi-view normal generation.

Abstract

The authors present a novel technique for adding geometric details to an input coarse 3D mesh guided by a text prompt. The method consists of three stages:

Single-view RGB generation: A single-view RGB image is generated conditioned on the input coarse geometry and the input text prompt. This allows the user to pre-visualize the result and offers stronger conditioning for subsequent multi-view generation.

Multi-view normal generation: A novel multi-view normal generation architecture is used to jointly generate six different views of normal images. The joint view generation reduces inconsistencies and leads to sharper details.

Mesh refinement and optimization: The mesh is optimized with respect to all views and a fine, detailed geometry is generated as output. The resulting method produces an output within seconds and offers explicit user control over the coarse structure, pose, and desired details of the resulting 3D mesh.

The authors compare their method with state-of-the-art text-to-3D methods and show that their approach generates shapes with better geometric details and visual quality, while being significantly faster (around 90x) than the competing methods.

Stats

The authors report the following key metrics:

CLIP similarity score: Our method achieves the best CLIP similarity score compared to the baseline methods.
Runtime: Our method runs in around 32 seconds, which is almost two orders of magnitude faster than the baseline methods.

Quotes

"Our method generates results that are more consistent with the input 3D mesh structure than the other methods and is much faster since we only need to run inference on pre-trained networks."
"The mesh refinement step operates directly on the input mesh and converges within seconds."

Key Insights Distilled From

Text-guided Controllable Mesh Refinement for Interactive 3D Modeling

by Yun-Chun Che... at arxiv.org 09-12-2024

https://arxiv.org/pdf/2406.01592.pdf

Deeper Inquiries

How can the method be extended to handle more complex geometric details, such as fine-grained textures or intricate surface features?

The method presented in the paper can be extended to handle more complex geometric details by integrating advanced texture synthesis techniques and enhancing the multi-view generation process. One approach is to incorporate high-resolution texture maps alongside the normal images generated during the multi-view normal generation stage. By utilizing texture synthesis models that leverage deep learning, the system can generate fine-grained textures that correspond to the intricate surface features of the 3D mesh. Additionally, employing techniques such as texture transfer from high-quality reference images can enrich the output with realistic surface details.
Moreover, the refinement stage could be enhanced by implementing a multi-scale approach, where different levels of detail are processed separately. This would allow the method to focus on generating intricate surface features at a finer scale while maintaining the overall structure of the mesh. Techniques such as procedural texture generation or the use of generative adversarial networks (GANs) could also be explored to create more complex surface patterns and textures, thereby improving the visual fidelity of the final output.

How does the performance of the method scale with the complexity of the input mesh and the desired level of detail?

The performance of the method scales favorably with the complexity of the input mesh and the desired level of detail due to its efficient architecture and reliance on pre-trained models. As the complexity of the input mesh increases, the method can still generate detailed outputs quickly because it operates primarily through feed-forward networks, which are significantly faster than iterative optimization methods used in other approaches.
However, the desired level of detail can impact the computational load. For instance, while the method can handle low-poly meshes efficiently, increasing the resolution or complexity of the mesh may require more computational resources during the mesh refinement stage. The multi-view normal generation process, which generates multiple normal images, may also become more demanding as the number of views increases. Nevertheless, the method's design allows for rapid inference times, typically within seconds, even for moderately complex meshes, making it suitable for interactive applications.

Could the multi-view normal generation be further improved by incorporating additional cues, such as depth information or silhouette constraints, to better preserve the global structure of the input mesh?

Yes, the multi-view normal generation can be significantly improved by incorporating additional cues such as depth information and silhouette constraints. Depth information can provide critical insights into the spatial relationships between different parts of the mesh, allowing the model to generate more accurate normal maps that reflect the true geometry of the object. By integrating depth cues, the model can better understand the 3D structure, leading to enhanced detail and consistency across different views.
Silhouette constraints can also play a vital role in preserving the global structure of the input mesh. By enforcing silhouette alignment during the normal generation process, the model can ensure that the generated normals adhere closely to the outline of the input mesh, reducing the likelihood of artifacts and inconsistencies. This approach would help maintain the integrity of the original shape while allowing for the addition of intricate details.
Combining these cues with the existing multi-view ControlNet architecture could lead to a more robust generation process, resulting in higher-quality outputs that are both visually appealing and geometrically accurate. This would enhance the overall effectiveness of the method in producing detailed and realistic 3D models.