Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds
We present Make-A-Texture, a new framework that efficiently synthesizes high-resolution texture maps from textual prompts for given 3D geometries. Our approach progressively generates textures that are consistent across multiple viewpoints with a depth-aware inpainting diffusion model, in an optimized sequence of viewpoints determined by an automatic view selection algorithm. A significant feature of our method is its remarkable efficiency, achieving a full texture generation within an end-to-end runtime of just 3.07 seconds on a single NVIDIA H100 GPU, significantly outperforming existing methods. Such an acceleration is achieved by optimizations in the diffusion model and a specialized backprojection method. Moreover, our method reduces the artifacts in the backprojection phase, by selectively masking out non-frontal faces, and internal faces of open-surfaced objects. Experimental results demonstrate that Make-A-Texture matches or exceeds the quality of other state-of-the-art methods. Our work significantly improves the applicability and practicality of texture generation models for real-world 3D content creation, including interactive creation and text-guided texture editing.
Code (0)
등록된 구현이 없습니다.
Tasks
GPUTexture SynthesisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Exploring 3D-aware Lifespan Face Aging via Disentangled Shape-Texture Representations
Existing face aging methods often focus on modeling either texture aging or using an entangled shape-texture representation to achieve face aging. However, shape and texture are two distinct factors that mutually affect …
3D Face ReconstructionFace ReconstructionTexture SynthesisModeling Extent-of-Texture Information for Ground Terrain Recognition
Ground Terrain Recognition is a difficult task as the context information varies significantly over the regions of a ground terrain image. In this paper, we propose a novel approach towards ground-terrain recognition via…
image-classificationImage ClassificationSOGAN: 3D-Aware Shadow and Occlusion Robust GAN for Makeup Transfer
In recent years, virtual makeup applications have become more and more popular. However, it is still challenging to propose a robust makeup transfer method in the real-world environment. Current makeup transfer methods m…
Face ModelFacial Makeup TransferTriTex: Learning Texture from a Single Mesh via Triplane Semantic Features
As 3D content creation continues to grow, transferring semantic textures between 3D meshes remains a significant challenge in computer graphics. While recent methods leverage text-to-image diffusion models for texturing,…
TextureSAM: Towards a Texture Aware Foundation Model for Segmentation
Segment Anything Models (SAM) have achieved remarkable success in object segmentation tasks across diverse datasets. However, these models are predominantly trained on large-scale semantic segmentation datasets, which in…
Material ClassificationObjectSegmentationSemantic Segmentation