| ---
|
| license: cc-by-4.0
|
| library_name: pytorch
|
| datasets:
|
| - MedPhyGraph/support-graph-data
|
| tags:
|
| - digital-twins
|
| - scene-graphs
|
| - dynamic-scene-graphs
|
| - robotics
|
| - support-relations
|
| - graph-maintenance
|
| - counterfactual-evidence
|
| - pytorch
|
| ---
|
|
|
| # 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** |
|
| | Dataset | [MedPhyGraph/support-graph-data](https://huggingface.co/datasets/MedPhyGraph/support-graph-data) |
|
| | Project | [medphygraph.github.io](https://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:
|
|
|
| 1. **State Consistency**
|
| 2. **Union-Based Transition-Aware Consistency**
|
|
|
| These components are non-learned inference operations and are not encoded as
|
| separate model checkpoints.
|
|
|
| In other words:
|
|
|
| ```text
|
| structured scene state
|
| │
|
| â–¼
|
| candidate support edges
|
| │
|
| â–¼
|
| CF-SupportNet
|
| (learned edge scores)
|
| │
|
| â–¼
|
| State Consistency
|
| │
|
| â–¼
|
| Union-Based Transition-Aware Consistency
|
| │
|
| â–¼
|
| maintained SupportedBy graph
|
| ```
|
|
|
| ## Dataset
|
|
|
| The corresponding public procedural training and evaluation data are available
|
| at:
|
|
|
| [**MedPhyGraph/support-graph-data**](https://huggingface.co/datasets/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.
|
|
|
| ## 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:
|
|
|
| ```text
|
| 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,
|
| title = {MedPhyGraph: Counterfactual Support-Graph Maintenance for Dynamic Built-Environment Digital Twins},
|
| author = {Gholizadeh HamlAbadi, Kamran and Vahdati, Monica and El Saddik, Abdulmotaleb},
|
| booktitle = {TwinWorld: Visual Intelligence for Built Environment Digital Twins, ECCV 2026 Workshop},
|
| year = {2026}
|
| }
|
| ``` |
