| ---
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| license: cc-by-4.0
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| library_name: pytorch
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| datasets:
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| - MedPhyGraph/support-graph-data
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| tags:
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| - digital-twins
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| - scene-graphs
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| - dynamic-scene-graphs
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| - robotics
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| - support-relations
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| - graph-maintenance
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| - counterfactual-evidence
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| - pytorch
|
| ---
|
|
|
| # CF-SupportNet
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|
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| CF-SupportNet is the learned edge-scoring component of **MedPhyGraph**
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| (*TwinWorld: Visual Intelligence for Built Environment Digital Twins, ECCV 2026 Workshop*).
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|
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| It is **not** the full MedPhyGraph framework by itself. MedPhyGraph also applies
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| deterministic **State Consistency** and **Union-Based Transition-Aware
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| Consistency** on top of CF-SupportNet scores to maintain support relations
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| across adjacent digital-twin states.
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|
|
| | Resource | Link |
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| |---|---|
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| | Paper | [openreview.net/forum?id=McTaO1qM8H](https://openreview.net/forum?id=McTaO1qM8H) |
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| | Model | **You are here** |
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| | Code | [github.com/kamranghz/medphygraph](https://github.com/kamranghz/medphygraph) |
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| | Dataset | [MedPhyGraph/support-graph-data](https://huggingface.co/datasets/MedPhyGraph/support-graph-data) |
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| | Project | [medphygraph.github.io](https://medphygraph.github.io/) |
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|
|
| ## Overview
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|
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| CF-SupportNet scores candidate `SupportedBy` edges from structured scene
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| information.
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| The scorer combines:
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|
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| - static geometric features
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| - candidate support-edge features
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| - analytic host-removal counterfactual evidence
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|
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| The counterfactual signal is computed using an **analytic AABB-based geometric
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| proxy**. It should not be interpreted as a full rigid-body physics simulation.
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|
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| CF-SupportNet operates downstream of scene perception. Rendered RGB images are
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| **not** model inputs and are **not** used as label sources.
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|
|
| ## Architecture
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|
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| The released CF-SupportNet checkpoints use the paper-frozen configuration:
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|
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| | Property | Value |
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| |---|---|
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| | Framework | PyTorch |
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| | Architecture | GRU + MLP |
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| | Hidden size | 64 |
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| | Trainable parameters | 25,409 |
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| | Counterfactual weight `rho` | 1.0 |
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|
|
| ## Paper-frozen checkpoints
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|
|
| This repository contains five released checkpoints:
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|
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| | Checkpoint | Role |
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| |---|---|
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| | `health_dyphygraph_r1.0_seed0.pt` | Primary paper checkpoint |
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| | `health_dyphygraph_r1.0_seed1.pt` | Multi-seed checkpoint |
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| | `health_dyphygraph_r1.0_seed2.pt` | Multi-seed checkpoint |
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| | `health_dyphygraph_r1.0_seed3.pt` | Multi-seed checkpoint |
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| | `health_dyphygraph_r1.0_seed4.pt` | Multi-seed checkpoint |
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|
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| **Seed 0** is the primary paper checkpoint.
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| **Seeds 1–4** are provided for the paper's multi-seed evaluation.
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|
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| The checkpoint files are paper-frozen artifacts and should not be modified when
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| reproducing the released results.
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|
|
| ## Download
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|
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| Download the complete model release with the Hugging Face CLI:
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|
|
| ```bash
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| hf download MedPhyGraph/CF-SupportNet \
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| --local-dir ./CF-SupportNet
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| ```
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|
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| Or with Python:
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|
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| ```python
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| from huggingface_hub import snapshot_download
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|
|
| snapshot_download(
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| repo_id="MedPhyGraph/CF-SupportNet",
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| local_dir="./CF-SupportNet",
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| )
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| ```
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|
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| To download only the primary checkpoint:
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|
|
| ```python
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| from huggingface_hub import hf_hub_download
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|
|
| checkpoint_path = hf_hub_download(
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| repo_id="MedPhyGraph/CF-SupportNet",
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| filename="health_dyphygraph_r1.0_seed0.pt",
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| )
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|
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| print(checkpoint_path)
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| ```
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|
|
| ## Relationship to MedPhyGraph
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|
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| CF-SupportNet provides learned scores for candidate support edges.
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|
|
| MedPhyGraph then applies deterministic graph-maintenance components, including:
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|
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| 1. **State Consistency**
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| 2. **Union-Based Transition-Aware Consistency**
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|
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| These components are non-learned inference operations and are not encoded as
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| separate model checkpoints. The full reference implementation, including these
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| consistency modules, is on GitHub: [kamranghz/medphygraph](https://github.com/kamranghz/medphygraph).
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|
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| In other words:
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|
|
| ```text
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| structured scene state
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| │
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| â–¼
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| candidate support edges
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| │
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| â–¼
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| CF-SupportNet
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| (learned edge scores)
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| │
|
| â–¼
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| State Consistency
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| │
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| â–¼
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| Union-Based Transition-Aware Consistency
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| │
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| â–¼
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| maintained SupportedBy graph
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| ```
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|
|
| ## Dataset
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|
|
| The corresponding public procedural training and evaluation data are available
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| at:
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|
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| [**MedPhyGraph/support-graph-data**](https://huggingface.co/datasets/MedPhyGraph/support-graph-data)
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|
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| The dataset release contains:
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|
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| - the frozen candidate-edge dataset and split
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| - structured Procedural scene states
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| - the 136-case Procedural subset of the expanded transfer evaluation
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|
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| NVIDIA Isaac for Healthcare assets and Isaac-derived structured scene states
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| are not redistributed in that repository.
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|
|
| ## Scope
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|
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| CF-SupportNet is intended to reproduce and study the learned scoring component
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| used in MedPhyGraph.
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|
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| The model does not directly provide:
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|
|
| - scene perception
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| - RGB/image understanding
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| - object detection
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| - complete physical simulation
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| - the deterministic MedPhyGraph consistency modules
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| - NVIDIA Isaac for Healthcare assets or scenes
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|
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| The complete MedPhyGraph method combines the learned scorer with deterministic
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| graph-maintenance logic. See the GitHub repository —
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| [kamranghz/medphygraph](https://github.com/kamranghz/medphygraph) — for the
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| full implementation.
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|
|
| ## Integrity
|
|
|
| The repository includes:
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|
|
| - **`checkpoint_manifest.json`** — metadata for the released checkpoints
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| - **`SHA256SUMS.txt`** — SHA256 hashes for checkpoint integrity verification
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|
|
| The primary seed-0 checkpoint has SHA256:
|
|
|
| ```text
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| e0b34529745399ecc5da5341ed7a162173611e12c8bd50dec121b0c575c5b789
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| ```
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|
|
| ## License
|
|
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| The released CF-SupportNet model weights are provided under the
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| **Creative Commons Attribution 4.0 International License (CC BY 4.0)**.
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|
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| Please provide appropriate attribution when using or redistributing these
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| weights.
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|
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| The license for the MedPhyGraph source code is separate from the model-weight
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| license.
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|
|
| ## Citation
|
|
|
| Paper: [https://openreview.net/forum?id=McTaO1qM8H](https://openreview.net/forum?id=McTaO1qM8H)
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|
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| If you use CF-SupportNet, MedPhyGraph, or these released checkpoints, please
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| cite:
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|
|
| ```bibtex
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| @inproceedings{hamlabadi2026medphygraph,
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| title = {MedPhyGraph: Counterfactual Support-Graph Maintenance for Dynamic Built-Environment Digital Twins},
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| author = {Kamran Gholizadeh HamlAbadi and Monica Vahdati and Abdulmotaleb El Saddik},
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| booktitle = {[Archival Track] ECCV 2026 TwinWorld: 1st Workshop on Visual Intelligence for Built Environment Digital Twins},
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| year = {2026},
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| url = {https://openreview.net/forum?id=McTaO1qM8H}
|
| }
|
| ``` |
