Texture Generation on 3D Meshes with Point-UV Diffusion (2308.10490v1)

Abstract: In this work, we focus on synthesizing high-quality textures on 3D meshes. We present Point-UV diffusion, a coarse-to-fine pipeline that marries the denoising diffusion model with UV mapping to generate 3D consistent and high-quality texture images in UV space. We start with introducing a point diffusion model to synthesize low-frequency texture components with our tailored style guidance to tackle the biased color distribution. The derived coarse texture offers global consistency and serves as a condition for the subsequent UV diffusion stage, aiding in regularizing the model to generate a 3D consistent UV texture image. Then, a UV diffusion model with hybrid conditions is developed to enhance the texture fidelity in the 2D UV space. Our method can process meshes of any genus, generating diversified, geometry-compatible, and high-fidelity textures. Code is available at https://cvmi-lab.github.io/Point-UV-Diffusion

• The paper introduces a two-stage pipeline that combines point and UV diffusion for generating high-quality textures on 3D meshes.
• It employs a coarse-to-fine approach with style guidance and hybrid conditioning to ensure enhanced detail and consistency.
• Empirical results show improved performance over existing techniques, offering promising applications in AR, VR, and gaming.

Overview of "Texture Generation on 3D Meshes with Point-UV Diffusion"

This paper introduces a sophisticated two-stage framework termed Point-UV diffusion for synthesizing high-quality textures on 3D meshes. The extensive exploration of texture generation using a combined diffusion model and UV mapping approach represents a significant advancement in the field, allowing for the production of highly detailed and geometry-consistent textures applicable across a diverse array of mesh topologies.

The proposed method is predicated on a coarse-to-fine pipeline. Initially, a point diffusion model is employed to generate low-frequency texture components on sampled points. This model effectively addresses challenges such as biased color distribution by introducing style guidance. Subsequently, the UV diffusion model refines these results, enhancing texture fidelity in 2D UV space, corresponding with global consistency from the initial coarse texture.

Technical Approach

1. Point Diffusion Model: The method commences with point sampling on the mesh's surface using farthest point sampling (FPS), assigning low-frequency colors initialized across these samples. This step leverages global geometric information, directed by a style guidance mechanism to mitigate dataset-driven color biases and foster diverse texture outcomes.
2. UV Diffusion Model: Building on the coarse textures, this stage refines the textures through a UV diffusion model using conditions derived from both the mesh and the initial point diffusion results. Notably, the hybrid condition paradigm introduces robustness, ensuring that even with suboptimal coarse textures, the generated textures maintain realism and detail.

Empirical Evaluation

The evaluation of Point-UV diffusion showcases its prowess over existing techniques such as Texture Fields and Texturify, as evidenced in the quantitative metrics like Frechet Inception Distance (FID) and Kernel Inception Distance (KID). The results indicate superior generation quality, especially in maintaining mesh structural details and producing high-frequency texture details with substantial diversity, as quantified through LPIPS.

Implications and Speculative Extensions

The implications of Point-UV diffusion are substantial for various applications in computer graphics and related fields, including gaming, augmented reality (AR), and virtual reality (VR). The ability to automatically generate high-fidelity textures without compromising on structural details is particularly promising for efficient content creation. Additionally, the versatility of this framework, demonstrated through successful texture generation conditioned on text and images, opens up significant potential for further extensions into multi-modal generative models within AI.

Looking forward, this research points to exciting possibilities in leveraging larger datasets to enhance the diversity and quality of outputs further. Improvements in UV parameterization could also expand its applicability to more complex scenarios without sacrificing texture coherence. Moreover, the framework sets a foundation for future work involving adaptive models that learn to optimize texture styles dynamically, curating outputs as per context-specific requirements.

To summarize, the Point-UV diffusion framework decisively addresses traditional challenges in texture generation on 3D meshes, achieving commendable performance gains. It serves as a meaningful reference for future explorations in the domain of automated high-quality 3D texture synthesis.