Text-guided High-definition Consistency Texture Model
Zhibin Tang, Tiantong He

TL;DR
This paper introduces HCTM, a novel method that generates high-definition, consistent textures for 3D meshes guided by text prompts, overcoming the resolution and consistency limitations of previous diffusion models.
Contribution
HCTM leverages a pre-trained depth-to-image diffusion model with parameter-efficient fine-tuning and multi-diffusion strategies to produce high-resolution, consistent textures from text prompts.
Findings
Successfully generates high-definition textures for 3D meshes
Achieves consistent textures across multiple viewpoints
Demonstrates promising results through experiments
Abstract
With the advent of depth-to-image diffusion models, text-guided generation, editing, and transfer of realistic textures are no longer difficult. However, due to the limitations of pre-trained diffusion models, they can only create low-resolution, inconsistent textures. To address this issue, we present the High-definition Consistency Texture Model (HCTM), a novel method that can generate high-definition and consistent textures for 3D meshes according to the text prompts. We achieve this by leveraging a pre-trained depth-to-image diffusion model to generate single viewpoint results based on the text prompt and a depth map. We fine-tune the diffusion model with Parameter-Efficient Fine-Tuning to quickly learn the style of the generated result, and apply the multi-diffusion strategy to produce high-resolution and consistent results from different viewpoints. Furthermore, we propose a…
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Taxonomy
TopicsComputer Graphics and Visualization Techniques · 3D Shape Modeling and Analysis · Generative Adversarial Networks and Image Synthesis
MethodsDiffusion
