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MedPhyGraph/
CF-SupportNet

counterfactual-evidence

MedPhyGraph/support-graph-data

cc-by-4.0

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CF-SupportNet

CF-SupportNet is the learned edge-scoring component of MedPhyGraph
(TwinWorld: Visual Intelligence for Built Environment Digital Twins, ECCV 2026 Workshop).

It is not the full MedPhyGraph framework by itself. MedPhyGraph also applies deterministic State Consistency and Union-Based Transition-Aware Consistency on top of CF-SupportNet scores to maintain support relations across adjacent digital-twin states.

Resource	Link
Model	You are here
Code	github.com/kamranghz/medphygraph
Dataset	MedPhyGraph/support-graph-data
Project	medphygraph.github.io

Overview

CF-SupportNet scores candidate SupportedBy edges from structured scene information.

The scorer combines:

static geometric features
candidate support-edge features
analytic host-removal counterfactual evidence

The counterfactual signal is computed using an analytic AABB-based geometric proxy. It should not be interpreted as a full rigid-body physics simulation.

CF-SupportNet operates downstream of scene perception. Rendered RGB images are not model inputs and are not used as label sources.

Architecture

The released CF-SupportNet checkpoints use the paper-frozen configuration:

Property	Value
Framework	PyTorch
Architecture	GRU + MLP
Hidden size	64
Trainable parameters	25,409
Counterfactual weight `rho`	1.0

Paper-frozen checkpoints

This repository contains five released checkpoints:

Checkpoint	Role
`health_dyphygraph_r1.0_seed0.pt`	Primary paper checkpoint
`health_dyphygraph_r1.0_seed1.pt`	Multi-seed checkpoint
`health_dyphygraph_r1.0_seed2.pt`	Multi-seed checkpoint
`health_dyphygraph_r1.0_seed3.pt`	Multi-seed checkpoint
`health_dyphygraph_r1.0_seed4.pt`	Multi-seed checkpoint

Seed 0 is the primary paper checkpoint.
Seeds 1–4 are provided for the paper's multi-seed evaluation.

The checkpoint files are paper-frozen artifacts and should not be modified when reproducing the released results.

Download

Download the complete model release with the Hugging Face CLI:

bash

hf download MedPhyGraph/CF-SupportNet \
  --local-dir ./CF-SupportNet

Or with Python:

python

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="MedPhyGraph/CF-SupportNet",
    local_dir="./CF-SupportNet",
)

To download only the primary checkpoint:

python

from huggingface_hub import hf_hub_download

checkpoint_path = hf_hub_download(
    repo_id="MedPhyGraph/CF-SupportNet",
    filename="health_dyphygraph_r1.0_seed0.pt",
)

print(checkpoint_path)

Relationship to MedPhyGraph

CF-SupportNet provides learned scores for candidate support edges.

MedPhyGraph then applies deterministic graph-maintenance components, including:

State Consistency
Union-Based Transition-Aware Consistency

These components are non-learned inference operations and are not encoded as separate model checkpoints. The full reference implementation, including these consistency modules, is on GitHub: kamranghz/medphygraph.

In other words:

text

structured scene state
        │
        ▼
candidate support edges
        │
        ▼
CF-SupportNet
(learned edge scores)
        │
        ▼
State Consistency
        │
        ▼
Union-Based Transition-Aware Consistency
        │
        ▼
maintained SupportedBy graph

Dataset

The corresponding public procedural training and evaluation data are available at:

MedPhyGraph/support-graph-data

The dataset release contains:

the frozen candidate-edge dataset and split
structured Procedural scene states
the 136-case Procedural subset of the expanded transfer evaluation

NVIDIA Isaac for Healthcare assets and Isaac-derived structured scene states are not redistributed in that repository.

Scope

CF-SupportNet is intended to reproduce and study the learned scoring component used in MedPhyGraph.

The model does not directly provide:

scene perception
RGB/image understanding
object detection
complete physical simulation
the deterministic MedPhyGraph consistency modules
NVIDIA Isaac for Healthcare assets or scenes

The complete MedPhyGraph method combines the learned scorer with deterministic graph-maintenance logic. See the GitHub repository — kamranghz/medphygraph — for the full implementation.

Integrity

The repository includes:

checkpoint_manifest.json — metadata for the released checkpoints
SHA256SUMS.txt — SHA256 hashes for checkpoint integrity verification

The primary seed-0 checkpoint has SHA256:

e0b34529745399ecc5da5341ed7a162173611e12c8bd50dec121b0c575c5b789

License

The released CF-SupportNet model weights are provided under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Please provide appropriate attribution when using or redistributing these weights.

The license for the MedPhyGraph source code is separate from the model-weight license.

Citation

If you use CF-SupportNet, MedPhyGraph, or these released checkpoints, please cite:

bibtex

@InProceedings{gholizadeh2026medphygraph,
  author    = {Gholizadeh HamlAbadi, Kamran and Vahdati, Monica and El Saddik, Abdulmotaleb},
  title     = {MedPhyGraph: Counterfactual Support-Graph Maintenance for Dynamic Built-Environment Digital Twins},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) Workshops (TwinWorld: Visual Intelligence for Built Environment Digital Twins)},
  year      = {2026},
  note      = {To appear}
}