Surface-aware Mesh Texture Synthesis with Pre-trained 2D CNNs: Leveraging Neural Networks for 3D Textures

Core Concepts

The author presents a novel surface-aware approach for mesh texture synthesis using pre-trained 2D CNNs, overcoming previous limitations and achieving visually appealing results.

Abstract

Surface-aware Mesh Texture Synthesis with Pre-trained 2D CNNs introduces a method that leverages neural networks to generate textures for 3D meshes. The approach addresses the challenges of traditional methods and produces high-quality textures by considering both geometry and texture content. By utilizing pre-trained weights from a 2D network, the method optimizes mesh convolution and pooling to generate textures directly on the surface of a 3D mesh. Through comparisons with state-of-the-art methods, the effectiveness of this approach is demonstrated in producing visually compelling and contextually appropriate texture representations for various meshes and styles.

Stats

The shared architecture allows us to use the weights of the 2D CNN—pre-trained on thousands of natural images—on the CNN operating on the mesh. Our implementation is publicly available in our repository [KHR24]. We use the output of block4_conv2 to compute the content difference, which we multiply by 1000. The time complexity of this process is at worst linear w.r.t. the number of triangles and texels. For our approach, we give measurements for each mesh together with the percentage of used texels.

Quotes

"We compare our approach with state-of-the-art methods where, through qualitative and quantitative evaluations, we demonstrate that our approach is more effective for a variety of meshes and styles." "Our implementation is publicly available in our repository [KHR24]."

Key Insights Distilled From

Surface-aware Mesh Texture Synthesis with Pre-trained 2D CNNs

by Áron... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06855.pdf

Deeper Inquiries

How can this surface-aware approach be extended to other tasks beyond texture synthesis

This surface-aware approach can be extended to other tasks beyond texture synthesis by leveraging its ability to capture the geometric context of 3D meshes. One potential extension is in style transfer for whole scenes, where the method could be used to stylize entire environments while preserving the underlying geometry and topology of the scene. Additionally, this approach could also be applied to segmentation or classification tasks on 3D meshes. By utilizing pre-trained weights from 2D CNNs and adapting them for operations on mesh surfaces, it opens up possibilities for a wide range of applications in computer graphics and computer vision.

What are potential drawbacks or limitations when applying this method to more complex scenes

When applying this method to more complex scenes, there are several potential drawbacks or limitations that may arise. One limitation is related to computational costs, especially during the optimization step which can have high resource requirements. Additionally, fine-grained control over specific features or elements in synthesized textures may be challenging with rapidly changing tangent fields leading to artifacts like colored spots that do not match the style texture accurately. The performance and quality of synthesized textures can also be sensitive to hyperparameters requiring manual tuning and experimentation for optimal results.

How might advancements in neural networks impact future developments in mesh texture synthesis

Advancements in neural networks are likely to have a significant impact on future developments in mesh texture synthesis. As neural network architectures continue to evolve with improved capabilities such as better feature extraction and representation learning, they will enable more sophisticated methods for generating realistic textures on 3D meshes. Enhanced architectures could provide better control over specific features or patterns within synthesized textures while maintaining visual coherence with the original exemplar images. Furthermore, advancements in training techniques like self-supervised learning or generative adversarial networks (GANs) could further enhance the quality and efficiency of mesh texture synthesis algorithms by enabling more robust training processes and better utilization of available data resources.