Text-guided High-definition Consistency Texture Model
Zhibin Tang Tiantong He
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 strategy that prevents the
appearance of noise on the textures caused by backpropagation. Our proposed
approach has demonstrated promising results in generating high-definition and
consistent textures for 3D meshes, as demonstrated through a series of
experiments.