A Diffusion Transformer framework applies coordinate-transformed RoPE and disjoint attention masks to achieve controllable, high-fidelity texture tiling that preserves reference structure and scene lighting.
Materialfusion: High-quality, zero-shot, and controllable material transfer with diffusion models
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
MaTe proposes a training-free diffusion transformer that performs material transfer using only images by integrating them at the token level for unified multi-modal attention in a shared latent space.
DealMaTe proposes a simplified diffusion framework for material transfer that injects multi-dimensional 3D conditions via Multi-Dim 3D Shader LoRA and Shader Causal Mutual Attention with KV caching.
citing papers explorer
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Controllable Texture Tiling with Transformed RoPE-Enhanced Diffusion Models
A Diffusion Transformer framework applies coordinate-transformed RoPE and disjoint attention masks to achieve controllable, high-fidelity texture tiling that preserves reference structure and scene lighting.
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DealMaTe: Multi-Dimensional Material Transfer via Diffusion Transformer
DealMaTe proposes a simplified diffusion framework for material transfer that injects multi-dimensional 3D conditions via Multi-Dim 3D Shader LoRA and Shader Causal Mutual Attention with KV caching.