mlsanityData to 7 Oct 2026

ConTEXTure: Consistent Multiview Images to Texture

arXiv 2407.10558 ↗First posted 15 Jul 2024cs.CV cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

We introduce ConTEXTure, a generative network designed to create a texture map/atlas for a given 3D mesh using images from multiple viewpoints. The process begins with generating a front-view image from a text prompt, such as 'Napoleon, front view', describing the 3D mesh. Additional images from different viewpoints are derived from this front-view image and camera poses relative to it. ConTEXTure builds upon the TEXTure network, which uses text prompts for six viewpoints (e.g., 'Napoleon, front view', 'Napoleon, left view', etc.). However, TEXTure often generates images for non-front viewpoints that do not accurately represent those viewpoints.To address this issue, we employ Zero123++, which generates multiple view-consistent images for the six specified viewpoints simultaneously, conditioned on the initial front-view image and the depth maps of the mesh for the six viewpoints. By utilizing these view-consistent images, ConTEXTure learns the texture atlas from all viewpoint images concurrently, unlike previous methods that do so sequentially. This approach ensures that the rendered images from various viewpoints, including back, side, bottom, and top, are free from viewpoint irregularities.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 36 references that are in the map. First posted on arXiv 15 Jul 2024.

Builds on

26 of 36 references.

References · 26

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  3. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  4. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  5. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,006
  6. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  7. 2021Vision Transformers for Dense Predictioncited by 1,789
  8. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,484
  9. 2023Zero-1-to-3: Zero-shot One Image to 3D Objectcited by 1,483
  10. 2022Magic3D: High-Resolution Text-to-3D Content Creationcited by 1,285
  11. 2023Objaverse-XL: A Universe of 10M+ 3D Objectscited by 767
  12. 2023Zero123++: a Single Image to Consistent Multi-view Diffusion Base Modelcited by 516
14 more
  1. 2021Zero-Shot Text-Guided Object Generation with Dream Fieldscited by 513
  2. 2022Latent-NeRF for Shape-Guided Generation of 3D Shapes and Texturescited by 506
  3. 2023TEXTure: Text-Guided Texturing of 3D Shapescited by 303
  4. 2023Text2Tex: Text-driven Texture Synthesis via Diffusion Modelscited by 234
  5. 2019Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Researchcited by 98
  6. 2023Text-Guided Texturing by Synchronized Multi-View Diffusioncited by 86
  7. 2023Paint-it: Text-to-Texture Synthesis via Deep Convolutional Texture Map Optimization and Physically-Based Renderingcited by 84
  8. 2022PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereocited by 69
  9. 2022AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesiscited by 39
  10. 2024InTeX: Interactive Text-to-texture Synthesis via Unified Depth-aware Inpaintingcited by 22
  11. 2023Breathing New Life into 3D Assets with Generative Repaintingcited by 17
  12. 2024MatAtlas: Text-driven Consistent Geometry Texturing and Material Assignmentcited by 14
  13. 2024TexRO: Generating Delicate Textures of 3D Models by Recursive Optimizationcited by 9
  14. 2023Text-guided High-definition Consistency Texture Modelcited by 7

Built on by

Not cited yet.