Texture Space Material Diffusion

arXiv:2609.37654 · cs.CV, cs.GR · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.GR

Submitted: 2026-09-29

Updated: 2026-10-01

Code: https://github.com/black-forest-labs/flux

Project page: https://seed3d.github.io/Seed3D/report.pdf

Terminology

• Diffusion Model — 31× in this paper · explained in 8 episodes
• Diffusion Models — 24× in this paper · explained in 6 episodes
• Transformer — 14× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 12× in this paper
• Metallicity — 10× in this paper · explained in 3 episodes
• Consistency — 7× in this paper · explained in 3 episodes
• Fidelity — 7× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 6× in this paper
• European Conference on Computer Vision — 5× in this paper
• International Conference on Computer Vision — 5× in this paper
• Distillation — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Diffusion Process — 2× in this paper · explained in 2 episodes
• Digital Twin — 2× in this paper · explained in 5 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Gaussian Splatting — 2× in this paper · explained in 4 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Sparse Attention — 2× in this paper · explained in 5 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Decoupling — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• In International Conference on Machine Learning — 1× in this paper
• Physically Based Rendering — 1× in this paper
• Redundancy — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes
• Transactions on Pattern Analysis and Machine Intelligence — 1× in this paper
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• MatAtlas: Text-driven Consistent Geometry Texturing and Material Assignment
• Cosmos World Foundation Model Platform for Physical AI
• Qwen2.5 Technical Report
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
• MVPainter: Accurate and Detailed 3D Texture Generation via Multi-View Diffusion with Geometric Control
• Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material
• Wan: Open and Advanced Large-Scale Video Generative Models
• Pandora3D: A Comprehensive Framework for High-Quality 3D Shape and Texture Generation
• Extracting Neural Materials from Images
• TexVerse: A Universe of 3D Objects with High-Resolution Textures

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