Skip to content
ModelListedExternal
Browse files

Maoam Ckpts

MAOAM (Mask Any Object And Material) is a unified selection framework that enables precise object- and material-level selection across both text- and click-based interactions. It leverages a Vision-Language Model...

KindOtherVersionRevision e30a5f0Cgrade

Model source

Kind
Other
File size
14.9–18.5 GB
Version
vRevision e30a5f0
Updated
Nov 1, 2026
Downloads
0
Source
Hugging Face

Source description

MAOAM: Unified Object and Material Selection with Vision-Language Models

MAOAM (Mask Any Object And Material) is a unified selection framework that enables precise object- and material-level selection across both text- and click-based interactions. It leverages a Vision-Language Model (VLM) with a segmentation head to produce pixel-accurate masks from user prompts.

Project Page | Paper (ArXiv) | Code

Features

  • Unified Selection: Supports both object-centric (e.g., "the chair") and material-centric (e.g., "the wood") selection.
  • Multi-modal Interaction: Handles user input via natural language descriptions, point clicks, or a combination of both.
  • High Precision: Integrates segmentation heads (like SAM and SAM 2) with VLM backbones (GLaMM or Sa2VA) to decode visual semantics into pixel-accurate boundaries.
  • Robust Material Understanding: Trained on a novel pipeline incorporating real and synthetic images with rich material-specific textual descriptions and VQA tasks.

Model Variants

The official implementation supports two backbones:

  • MAOAM-GLaMM: Based on LLaVA-Llama (GLaMM) and SAM ViT-H.
  • MAOAM-Sa2VA: Based on Qwen2.5-VL-7B and SAM2 Hiera-L.

Usage

For detailed instructions on setup, downloading weights, and running the Gradio demo, please refer to the official GitHub repository.

Citation

@inproceedings{park2026maoam,
  title     = {MAOAM: Unified Object and Material Selection with Vision-Language Models},
  author    = {Park, Jaden and Deschaintre, Valentin and Kuen, Jason and
               Liu, Kangning and Georgiev, Iliyan and Singh, Krishna Kumar and
               Lee, Yong Jae and Fischer, Michael},
  booktitle = {ACM SIGGRAPH 2026 Conference Papers},
  year      = {2026},
  publisher = {ACM},
  doi       = {10.1145/3799902.3811186},
}

Sources

1 source
Hugging FacePrimaryVersion e30a5f00218c00f03f1e6462cefa3f10b1e4afbb
huggingface.co/jpark677/maoam_ckpts/tree/e30a5f00218c00f03f1e6462cefa3f10b1e4afbb

Verified Nov 1

Download files

Model artifacts

2 artifacts

glamm/mp_rank_00_model_states.pt

pt · 14.9 GB · SHA-256 fbdec295e6f1…fa28 · Hugging Face

Download file

sa2va/mp_rank_00_model_states.pt

pt · 18.5 GB · SHA-256 c2e872c355b3…b15f · Hugging Face

Download file

Source excerpts

2 excerpts

--- pipeline_tag: image-segmentation license: other datasets: - jpark677/maoam_data --- # MAOAM: Unified Object and Material Selection with Vision-Language Models MAOAM (Mask Any Object And Material) is a unified selection framework that enables precise object- and material-level selection across both text- and click-based interactions. It leverages a Vision-Language Model (VLM) with a segmentation head to produce pixel-accurate masks from user prompts. [Project Page](https://jadenpark0.github.io/project_pages/maoam/) | [Paper (ArXiv)](https://arxiv.org/abs/2606.04880) | [Code](https://github.com/adobe-research/obj-and-mat-selection) ## Features - **Unified Selection:** Supports both object-centric (e.g., "the chair") and material-centric (e.g., "the wood") selection. - **Multi-modal Interaction:** Handles user input via natural language descriptions, point clicks, or a combination of both. - **High Precision:** Integrates segmentation heads (like SAM and SAM 2) with VLM backbones (GLaMM or Sa2VA) to decode visual semantics into pixel-accurate boundaries. - **Robust Material Understanding:** Trained on a novel pipeline incorporating real and synthetic images with rich material-specific textual descriptions and VQA tasks. ## Model Variants The official implementation supports two backbones: - **MAOAM-GLaMM:** Based on LLaVA-Llama (GLaMM) and SAM ViT-H. - **MAOAM-Sa2VA:** Based on Qwen2.5-VL-7B and SAM2 Hiera-L. ## Usage For detailed instructions on setup, downloading weights, and running the Gradio demo, please refer to the [official GitHub repository](https://github.com/adobe-research/obj-and-mat-selection). ## Citation ```bibtex @inproceedings{park2026maoam, title = {MAOAM: Unified Object and Material Selection with Vision-Language Models}, author = {Park, Jaden and Deschaintre, Valentin and Kuen, Jason and Liu, Kangning and Georgiev, Iliyan and Singh, Krishna Kumar and Lee, Yong Jae and Fischer, Michael}, booktitle = {ACM SIGGRAPH 2026 Conference Papers}, year = {2026}, publisher = {ACM}, doi = {10.1145/3799902.3811186}, } ```

Source context: 0 downloads · 1 likes · Pipeline image-segmentation · Repo jpark677/maoam_ckpts