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...
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 14,9–18,5 GB
- Versão
- vRevision e30a5f0
- Atualizado
- 1 de nov. de 2026
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- Fonte
- Hugging Face
Descrição da fonte
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},
}
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2 artefatosglamm/mp_rank_00_model_states.pt
pt · 14,9 GB · SHA-256 fbdec295e6f1…fa28 · Hugging Face
Download filesa2va/mp_rank_00_model_states.pt
pt · 18,5 GB · SHA-256 c2e872c355b3…b15f · Hugging Face
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Trechos de fonte
2 trechos--- 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
