HiMat: DiT-based Ultra-High Resolution SVBRDF Generation

• URL: http://arxiv.org/abs/2508.07011v4
• Date: Tue, 07 Oct 2025 01:56:25 GMT
• Title: HiMat: DiT-based Ultra-High Resolution SVBRDF Generation
• Authors: Zixiong Wang, Jian Yang, Yiwei Hu, Milos Hasan, Beibei Wang,
• Abstract summary: HiMat is a diffusion-based framework tailored for efficient and diverse 4K SVBRDF generation.<n>CrossStitch is a lightweight convolutional module that enforces cross-map consistency without incurring the cost of global attention.
• Score: 26.081964370337943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Creating ultra-high-resolution spatially varying bidirectional reflectance functions (SVBRDFs) is critical for photorealistic 3D content creation, to faithfully represent fine-scale surface details required for close-up rendering. However, achieving 4K generation faces two key challenges: (1) the need to synthesize multiple reflectance maps at full resolution, which multiplies the pixel budget and imposes prohibitive memory and computational cost, and (2) the requirement to maintain strong pixel-level alignment across maps at 4K, which is particularly difficult when adapting pretrained models designed for the RGB image domain. We introduce HiMat, a diffusion-based framework tailored for efficient and diverse 4K SVBRDF generation. To address the first challenge, HiMat performs generation in a high-compression latent space via DC-AE, and employs a pretrained diffusion transformer with linear attention to improve per-map efficiency. To address the second challenge, we propose CrossStitch, a lightweight convolutional module that enforces cross-map consistency without incurring the cost of global attention. Our experiments show that HiMat achieves high-fidelity 4K SVBRDF generation with superior efficiency, structural consistency, and diversity compared to prior methods. Beyond materials, our framework also generalizes to related applications such as intrinsic decomposition.

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