MAMAMMA
MAMMA
Model Overview
MAMMA is a undisclosed-parameter model developed by Academic/Research. Released on 2026-07-15.
📊 Quick Specs
| Specification | Value |
|---|---|
| Parameters | undisclosed |
| Task | other |
| Modality | vision, 3d, video |
| License | Other/Custom |
| Type | research-preview |
✨ Key Features
- MammaNet Transformer Architecture: Employs a ViT-Base backbone to decode 512 dense surface landmarks with pixel coordinates, uncertainty, and visibility estimation
- Contact-Aware & Visibility Heads: Jointly predicts person-to-person and person-to-floor contact probabilities to constrain 3D multi-person pose optimization
- Markerless SMPL-X Fitting: Recovers full-body expressive SMPL-X parameters directly from synchronized multi-view video without specialized marker suits
- Multi-View Epipolar Optimization: Uses symmetric epipolar distance matching across camera views to resolve person-to-landmark correspondences under heavy occlusion
- MammaSyn Synthetic Dataset: Trained on a large-scale synthetic multi-person dataset with ground-truth dense landmarks and physics-based contact labels
- Web GUI & CLI Tooling: Offers zero-config command-line execution (python -m inference run) as well as a local web browser user interface on port 3000
🔗 Resources
- GitHub: Repository
- Paper: arXiv
- Website: Project Page
📜 License & Access
Other/Custom — See repository for specific license details.
Key Features
MammaNet Transformer Architecture: Employs a ViT-Base backbone to decode 512 dense surface landmarks with pixel coordinates, uncertainty, and visibility estimation
Contact-Aware & Visibility Heads: Jointly predicts person-to-person and person-to-floor contact probabilities to constrain 3D multi-person pose optimization
Markerless SMPL-X Fitting: Recovers full-body expressive SMPL-X parameters directly from synchronized multi-view video without specialized marker suits
Multi-View Epipolar Optimization: Uses symmetric epipolar distance matching across camera views to resolve person-to-landmark correspondences under heavy occlusion
MammaSyn Synthetic Dataset: Trained on a large-scale synthetic multi-person dataset with ground-truth dense landmarks and physics-based contact labels
Web GUI & CLI Tooling: Offers zero-config command-line execution (python -m inference run) as well as a local web browser user interface on port 3000
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Verified Sources
Tags
Model Specs
Parameters
Undisclosed
Context Window
undisclosed
License
Other/Custom
Deployment
Resources & Links
Curator Notes
Partially enriched via migration on 2026-07-25. Manual review recommended.
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