metadata
license: mit
library_name: pytorch
tags:
- digital-twins
- scene-graphs
- robotics
- computer-vision
- graph-neural-network
CF-SupportNet
CF-SupportNet is the learned edge-scoring component of MedPhyGraph (ECCV 2026 TwinWorld Workshop). It is NOT the full MedPhyGraph framework by itself: MedPhyGraph also applies deterministic State Consistency and Union-Based Transition-Aware Consistency (non-learned, code-only) on top of these scores to produce the final maintained support graph. See the paper and code at https://medphygraph.github.io/ for the complete pipeline.
Files
health_dyphygraph_r1.0_seed0.pt— primary checkpoint used for all headline paper results (rho=1.0, seed 0)health_dyphygraph_r1.0_seed{1-4}.pt— used only for the paper's multi-seed reproducibility audit; not intended as alternative production checkpoints
Architecture
One-layer GRU (hidden width 64), fusion MLP, 25,409 trainable parameters. Full training details in the paper, Section 3.4.
Citation
@inproceedings{gholizadeh2026medphygraph,
title={MedPhyGraph: Counterfactual Support-Graph Maintenance for Dynamic Built-Environment Digital Twins},
author={Gholizadeh HamlAbadi, Kamran and Vahdati, Monica and El Saddik, Abdulmotaleb},
booktitle={ECCV 2026 TwinWorld Workshop},
year={2026}
}