GOATex: Geometry & Occlusion-Aware Texturing
On the map: Diffusion models → 3D, point cloud and scene generation
Abstract (arXiv)
We present GOATex, a diffusion-based method for 3D mesh texturing that generates high-quality textures for both exterior and interior surfaces. While existing methods perform well on visible regions, they inherently lack mechanisms to handle occluded interiors, resulting in incomplete textures and visible seams. To address this, we introduce an occlusion-aware texturing framework based on the concept of hit levels, which quantify the relative depth of mesh faces via multi-view ray casting. This allows us to partition mesh faces into ordered visibility layers, from outermost to innermost. We then apply a two-stage visibility control strategy that progressively reveals interior regions with structural coherence, followed by texturing each layer using a pretrained diffusion model. To seamlessly merge textures obtained across layers, we propose a soft UV-space blending technique that weighs each texture's contribution based on view-dependent visibility confidence. Empirical results demonstrate that GOATex consistently outperforms existing methods, producing seamless, high-fidelity textures across both visible and occluded surfaces. Unlike prior works, GOATex operates entirely without costly fine-tuning of a pretrained diffusion model and allows separate prompting for exterior and interior mesh regions, enabling fine-grained control over layered appearances. For more qualitative results, please visit our project page: https://goatex3d.github.io/.
Key facts. Cited by 1 paper 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 54 of its 60 references that are in the map. First posted on arXiv 28 Nov 2025.
Builds on
54 of 60 references.
Closest ancestors · 13
Same direction, newest first
- 2025StochSync: Stochastic Diffusion Synchronization for Image Generation in Arbitrary Spaces26 Jan
- 2025Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation21 Jan
- 2024TEXGen: a Generative Diffusion Model for Mesh Textures22 Nov
- 2024TexGen: Text-Guided 3D Texture Generation with Multi-view Sampling and Resampling2 Aug
- 2024CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assets30 May
- 2024EASI-Tex: Edge-Aware Mesh Texturing from Single Image27 May
- 2024DreamMat: High-quality PBR Material Generation with Geometry- and Light-aware Diffusion Models27 May
- 2023Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Models21 Dec
- 2023TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models20 Oct
- 2023SyncDiffusion: Coherent Montage via Synchronized Joint Diffusions8 Jun
- 2023Text2Tex: Text-driven Texture Synthesis via Diffusion Models20 Mar
- 2023TEXTure: Text-Guided Texturing of 3D Shapes3 Feb
1 more
Other references · 41
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2020Denoising Diffusion Implicit Modelscited by 9,781
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
- 2022Scalable Diffusion Models with Transformerscited by 6,054
- 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
- 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,356
- 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,181
- 2021CLIPScore: A Reference-free Evaluation Metric for Image Captioningcited by 2,424
- 2022Objaverse: A Universe of Annotated 3D Objectscited by 1,540
- 2018Demystifying MMD GANscited by 1,293
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- 2023Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inferencecited by 846
- 2020Accelerating 3D Deep Learning with PyTorch3Dcited by 784
- 2023Objaverse-XL: A Universe of 10M+ 3D Objectscited by 774
- 2018Open3D: A Modern Library for 3D Data Processingcited by 721
- 2023MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generationcited by 536
- 2021Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesiscited by 460
- 2022GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Imagescited by 446
- 2024SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformerscited by 340
- 2024PixArt-Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generationcited by 329
- 2024Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillationcited by 279
- 2023LCM-LoRA: A Universal Stable-Diffusion Acceleration Modulecited by 211
- 2019Texture Fields: Learning Texture Representations in Function Spacecited by 210
- 2021StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generatorscited by 164
- 2024GPT-4V(ision) is a Human-Aligned Evaluator for Text-to-3D Generationcited by 153
- 2023GPT-4V(ision) as a Generalist Evaluator for Vision-Language Taskscited by 119
- 2023Text-Guided Texturing by Synchronized Multi-View Diffusioncited by 86
- 2023Paint-it: Text-to-Texture Synthesis via Deep Convolutional Texture Map Optimization and Physically-Based Renderingcited by 84
- 2020Leveraging 2D Data to Learn Textured 3D Mesh Generationcited by 69
- 2022Texturify: Generating Textures on 3D Shape Surfacescited by 65
- 2024FlashTex: Fast Relightable Mesh Texturing with LightControlNetcited by 56
- 2020Convolutional Generation of Textured 3D Meshescited by 52
- 2024Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D Objectscited by 52
- 2021Learning Generative Models of Textured 3D Meshes from Real-World Imagescited by 44
- 2023Mesh2Tex: Generating Mesh Textures from Image Queriescited by 31
- 2024SyncTweedies: A General Generative Framework Based on Synchronized Diffusionscited by 26
- 2024TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generationcited by 15
- 2025CLIP Under the Microscope: A Fine-Grained Analysis of Multi-Object Representationcited by 14
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