RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion Models (2409.19989v1)

Abstract: Text-to-texture generation has recently attracted increasing attention, but existing methods often suffer from the problems of view inconsistencies, apparent seams, and misalignment between textures and the underlying mesh. In this paper, we propose a robust text-to-texture method for generating consistent and seamless textures that are well aligned with the mesh. Our method leverages state-of-the-art 2D diffusion models, including SDXL and multiple ControlNets, to capture structural features and intricate details in the generated textures. The method also employs a symmetrical view synthesis strategy combined with regional prompts for enhancing view consistency. Additionally, it introduces novel texture blending and soft-inpainting techniques, which significantly reduce the seam regions. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods.

• The paper introduces a novel approach for text-to-texture synthesis using SDXL and multiple ControlNets to generate high-fidelity, seamless textures.
• The paper leverages symmetrical view synthesis and regional prompts to overcome challenges of view inconsistency and misalignment in 3D assets.
• The paper demonstrates superior performance with lower KID scores and favorable user studies, indicating a significant advancement in digital asset quality.

RoCoTex: Advancements in Texture Synthesis via Diffusion Models

Text-to-texture generation is a crucial component in the development of photorealistic 3D assets, particularly within the realms of gaming, film production, and virtual/augmented reality. Traditional methods have struggled with issues such as view inconsistency, misalignment with underlying meshes, and the presence of seams. RoCoTex presents a novel approach to address these issues, leveraging state-of-the-art diffusion models alongside several innovative techniques.

Overview of the Methodology

RoCoTex utilizes 2D diffusion models, specifically Stable Diffusion XL (SDXL), and multiple complimentary control techniques to enhance the generation of well-aligned, seamless textures. By employing a symmetrical view synthesis strategy akin to Paint3D, RoCoTex captures multiple views simultaneously, mitigating issues of context loss and view inconsistency. This method is further refined through the integration of regional prompts, which help circumvent the Janus problem encountered in earlier models where faces or objects appeared duplicated or misaligned when viewed from different angles.

In conjunction with symmetrical viewing, RoCoTex incorporates multiple ControlNets—specifically depth, normal, and edge ControlNets—to improve alignment accuracy and high-fidelity detail capture in generated textures. Unlike previous models, which rely on a single depth control, the adoption of multiple ControlNets ensures a better comprehension of the 3D geometry, thus improving the alignment with the UV maps of the models.

Furthermore, the system introduces a confidence-based texture blending technique, which employs pixel confidence values to integrate generated texture data into evolving global textures. This mitigates the seam issue that tends to arise from iterative texture synthesis.

Experimental Verification and Results

The robustness and effectiveness of RoCoTex are extensively verified through experiments that demonstrate the system's superior performance over existing methodologies such as TEXTure, Text2Tex, and Paint3D. Qualitatively, RoCoTex exhibits an enhanced ability to maintain consistency and alignment while eliminating common artifacts, showing a marked improvement in visual quality. Quantitatively, the Kernel Inception Distance (KID) scores reflect RoCoTex’s superior texturing capability, indicating a lower metric score which is indicative of high image quality and diversity. User studies corroborate these findings, with participants favorably rating RoCoTex for its texture quality, consistency, and alignment.

Practical and Theoretical Implications

The practical implications of RoCoTex are significant. By facilitating the generation of high-quality textures with reduced inconsistencies and artifacts, RoCoTex enhances the efficiency of 3D asset production, reducing time and resource expenditure in industries reliant on digital models. Theoretically, the method paves the way for further exploration into multi-view and text-guided synthesis techniques.

Despite these advancements, RoCoTex has limitations concerning occlusion treatment and baked-in illumination challenges, which suggest potential avenues for future exploration. Additionally, the current approach may necessitate further refinement in handling complex lighting scenarios.

Speculation on Future Developments

Advancements in AI-driven texture synthesis such as those demonstrated by RoCoTex promise a future where digital asset creation is not only faster and more resource-effective but also significantly more precise in terms of quality and realism. Further development could see enhanced AI models with better capacity for handling occlusions and illumination discrepancies in 3D models. Expanding the dataset size and diversity for training may also bolster the applicability of this system across various genres and stylistic demands.

In summary, RoCoTex represents a notable evolution in texture synthesis methods, integrating powerful diffusion models with strategic innovations to overcome long-standing challenges in text-to-texture generation. As the methodology evolves, it holds promise for revolutionizing digital asset creation across multiple industries.