README.md

metadata

license: cc-by-4.0
library_name: pytorch
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 (ECCV 2026 TwinWorld Workshop). It is not the full MedPhyGraph framework by itself: MedPhyGraph additionally applies deterministic State Consistency and Union-Based Transition-Aware Consistency — non-learned, code-only operations — on top of these scores to produce the final maintained support graph. See the project page for the paper and complete pipeline code.

Files

File	Role
health_dyphygraph_r1.0_seed0.pt	Primary checkpoint — used for all headline results in the paper (ρ=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 (temporal encoder over counterfactual evidence channels)
• Geometry branch: 2 → 32 → 32 MLP (static geometric channels)
• Fusion branch: 96 → 64 → 64 MLP, dropout 0.1
• 25,409 trainable parameters total

Full architectural and training details are in the paper, Section 3.4.

Results

Frozen seed-0 checkpoint, full observation (ρ=1.0):

Protocol	Transition-Macro Dyn-F1	Pooled Delta Micro-F1	Transfer Dyn-F1
Core (15 transfers, Isaac + Procedural)	1.000	1.000	1.000
Expanded (217 transfers, 19 templates)	—	—	0.998

Across seeds 0–4, pooled Transfer Dyn-F1 on the expanded suite is 0.997 ± 0.001.

Loading the checkpoint

from huggingface_hub import hf_hub_download
import torch

path = hf_hub_download(
    repo_id="MedPhyGraph/CF-SupportNet",
    filename="health_dyphygraph_r1.0_seed0.pt",
)
state_dict = torch.load(path, map_location="cpu", weights_only=True)

This repository hosts raw PyTorch weights only, not a packaged from_pretrained-compatible class. To reconstruct the model, define the architecture above (or import it from the code repository) and load state_dict into it directly.

Limitations

• The counterfactual evidence used at both training and inference comes from an analytic AABB-based host-removal proxy, not a full physics simulator — it does not model mesh collisions, contact forces, or materials.
• Operates downstream of perception on structured object states (poses, categories, AABB extents); it does not take raw RGB-D or perception input.
• Evaluates direct, primary SupportedBy relations only.
• Assumes the correct destination candidate is present in the candidate set at inference; if it is not, a support transfer cannot be recovered by the scorer or the downstream graph-maintenance stages.
• Does not model distributed support, articulated contact, or temporary/transient support.

Related artifacts

• Structured training/evaluation data (procedural + Isaac for Healthcare-derived scenes): MedPhyGraph/support-graph-data (forthcoming)

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 Workshop on Visual Intelligence for Built Environment Digital Twins (TwinWorld)},
  year      = {2026}
}