UltraTex: Unleashing 2K Multi-View Diffusion for 3D Texturing

arXiv:2609.23169 · cs.CV · Submitted 2026-09-19 · Read on arXiv

cs.CV

Submitted: 2026-09-19

Updated: 2026-09-19

Project page: https://yiboz2001.github.io/UltraTex

Terminology

• Sparse Attention — 28× in this paper · explained in 5 episodes
• Fidelity — 19× in this paper · explained in 2 episodes
• Transformer — 19× in this paper · explained in 2 episodes
• Block-Sparse Attention — 18× in this paper
• Background Token Dropping — 16× in this paper
• Redundancy — 16× in this paper · explained in 2 episodes
• Diffusion Model — 8× in this paper · explained in 8 episodes
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Flow Matching — 4× in this paper · explained in 8 episodes
• Sparsity — 4× in this paper · explained in 4 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• alpha — 3× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• CLIP Maximum Mean Discrepancy — 1× in this paper
• Decode — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• LumiTex: Towards High-Fidelity PBR Texture Generation with Illumination Context
• Sparse-vDiT: Unleashing the Power of Sparse Attention to Accelerate Video Diffusion Transformers
• Seed3D 1.0: From Images to High-Fidelity Simulation-Ready 3D Assets
• Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material
• Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details
• Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured 3D Assets
• Radial Attention: $O(n\log n)$ Sparse Attention with Energy Decay for Long Video Generation
• Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations
• UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes
• Flow Matching for Generative Modeling
• CaliTex: Geometry-Calibrated Attention for View-Coherent 3D Texture Generation
• MoBA: Mixture of Block Attention for Long-Context LLMs
• VMoBA: Mixture-of-Block Attention for Video Diffusion Models
• Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention
• SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse-Linear Attention
• SpargeAttention: Accurate and Training-free Sparse Attention Accelerating Any Model Inference
• VSA: Faster Video Diffusion with Trainable Sparse Attention
• TexVerse: A Universe of 3D Objects with High-Resolution Textures
• Trainable Log-linear Sparse Attention for Efficient Diffusion Transformers

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