MAOAM unifies object and material selection via a VLM with segmentation head, supporting text and click interactions through multi-task training on VLM-generated material data.
Materialistic: Selecting Similar Materials in Images , year =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
-
MAOAM: Unified Object and Material Selection with Vision-Language Models
MAOAM unifies object and material selection via a VLM with segmentation head, supporting text and click interactions through multi-task training on VLM-generated material data.
-
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.