mlsanityData to 14 Oct 2026

Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds

arXiv 2412.07766 ↗First posted 10 Dec 2024cs.CV cs.GRcited by 12

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 32 of its 42 references that are in the map. First posted on arXiv 10 Dec 2024.

Builds on

32 of 42 references.

Closest ancestors · 7

Same direction, newest first

  1. 2024MVDiffusion++: A Dense High-resolution Multi-view Diffusion Model for Single or Sparse-view 3D Object Reconstruction20 Feb
  2. 2023Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Models21 Dec
  3. 2023Wonder3D: Single Image to 3D using Cross-Domain Diffusion23 Oct
  4. 2023Texture Generation on 3D Meshes with Point-UV Diffusion21 Aug
  5. 2023Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation24 Mar
  6. 2023Text2Tex: Text-driven Texture Synthesis via Diffusion Models20 Mar
  7. 2023TEXTure: Text-Guided Texturing of 3D Shapes3 Feb

Other references · 25

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2020Denoising Diffusion Implicit Modelscited by 9,781
  5. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  6. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  7. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,614
  8. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,937
  9. 2023T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Modelscited by 1,351
  10. 2018Demystifying MMD GANscited by 1,293
  11. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,114
  12. 2022DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Modelscited by 938
13 more
  1. 2020Accelerating 3D Deep Learning with PyTorch3Dcited by 784
  2. 2022Latent-NeRF for Shape-Guided Generation of 3D Shapes and Texturescited by 506
  3. 2021Text2Mesh: Text-Driven Neural Stylization for Meshescited by 334
  4. 2022CLIP-Mesh: Generating textured meshes from text using pretrained image-text modelscited by 281
  5. 2023Text-Guided Texturing by Synchronized Multi-View Diffusioncited by 86
  6. 2023Paint-it: Text-to-Texture Synthesis via Deep Convolutional Texture Map Optimization and Physically-Based Renderingcited by 84
  7. 2024TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusioncited by 52
  8. 2024Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D Objectscited by 52
  9. 2023Mesh2Tex: Generating Mesh Textures from Image Queriescited by 31
  10. 2024Garment3DGen: 3D Garment Stylization and Texture Generationcited by 26
  11. 2024InTeX: Interactive Text-to-texture Synthesis via Unified Depth-aware Inpaintingcited by 22
  12. 2024PuzzleAvatar: Assembling 3D Avatars from Personal Albumscited by 16
  13. 2024TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generationcited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 5.

12 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured 3D Assets
  2. 2024Infinite Texture: Text-guided High Resolution Diffusion Texture Synthesis
  3. 2025CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation
  4. 2024GenesisTex2: Stable, Consistent and High-Quality Text-to-Texture Generation
  5. 2023Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior
  6. 2024RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion Models
  7. 2025End-to-End Fine-Tuning of 3D Texture Generation using Differentiable Rewards
  8. 2025MVPainter: Accurate and Detailed 3D Texture Generation via Multi-View Diffusion with Geometric Control