CInterno/GeoPhys-features

GeoPhys features

Frame-trajectory features of the videos of three physics benchmarks, extracted with the four frozen image encoders used by GeoPhys (paper, code).

Contents

folder	what	stores	size
readout/	the readout layer of each encoder: DINOv2 block 12, DINOv3 block 18, CORnet-S IT, VOneNet V1 block	32	15.3 GB
all_layers/	every layer: 24 blocks for DINOv2 and DINOv3; V1, V2, V4, IT for CORnet-S; the V1 block and ResNet-50 layers 1-4 for VOneNet	32	212.1 GB

Each folder holds one store per <dataset>_<encoder>_<preprocessing>:

dataset	videos
likephys	LikePhys: 800 plausible/implausible pairs, 920 videos
intphys2	IntPhys 2, Main set: 506 pairs, 1,012 videos
v2v, v2v16	Physics-IQ: 198 scenarios x 16 video-to-video generations of MAGI-1 24B; v2v holds candidates 0-7, v2v16 candidates 8-15

Encoders: dinov2 (facebook/dinov2-large), dinov3 (facebook/dinov3-vitl16-pretrain-lvd1689m), cornet (CORnet-S), vonenet (VOneNet, ResNet-50 back end). Preprocessing: direct resizes each frame to 224x224; hf resizes the short side to 256 and centre-crops 224x224. Every video is decoded to at most 60 frames (longer videos are subsampled evenly).

Files of a store

file	shape	content
gram.npy	[N, L, A, 60, 60] float32	Gram matrix G[t, s] = <z_t, z_s> of each video's frame trajectory, per layer and per frame feature ("atom": flat = all patch tokens, mean = mean patch token, cls = CLS token; the atoms present are listed in meta.json)
t2_{raw,l2,spd}_sq.npy	[N, L, P, 59] float32	squared step of every spatial token between consecutive frames: raw tokens, l2-normalised tokens, or raw steps divided by the token's mean speed
t2_{raw,l2,spd}_dot.npy	[N, L, P, 58] float32	inner product of consecutive steps of every token
done.npy	[N] bool	extraction completed for each video
meta.json		videos (paths relative to the benchmark's own video folder), pairs (vi, ii: plausible and implausible video of each pair) or scenarios and index (scenario and candidate number of each video), layers, atoms, P (tokens per frame)

N videos, L layers, A atoms, P spatial tokens (196 for the ViTs; 256 for the convolutional networks, whose feature maps are pooled to 16x16). Videos shorter than 60 frames are padded with zeros. Every distance, angle and curvature along a trajectory follows from these arrays without the features themselves; see geophys/signals.py.

Use

pip install -U huggingface_hub
hf download CInterno/GeoPhys-features --repo-type dataset --include "readout/*" --local-dir geophys_features

git clone https://github.com/ChristianInterno/GeoPhys && cd GeoPhys && pip install -e .
python -m geophys.detect --stores ../geophys_features/readout    # detection tables
python -m geophys.verify --stores ../geophys_features/readout    # Physics-IQ table

A single store: --include "all_layers/likephys_dinov3_direct/*".

import numpy as np
from geophys.signals import gram_signals

G = np.load("geophys_features/readout/likephys_dinov3_direct/gram.npy", mmap_mode="r")
g = np.array(G[:, 0, 1], np.float64)        # mean-token trajectories at the readout layer
g[g == 0] = np.nan                           # padding
curvature = gram_signals(g)["curv"]          # one value per video

Licences

The features are derived from third-party videos and models, and each part follows the terms of its sources:

• intphys2_*: derived from IntPhys 2, released under CC BY-NC 4.0. Non-commercial use only, with attribution.
• v2v_*, v2v16_*: derived from videos generated by MAGI-1 (Apache 2.0) from Physics-IQ inputs (CC BY 4.0).
• likephys_*: derived from the LikePhys benchmark videos; use them under that benchmark's terms.
• *_dinov3_*: produced with DINOv3 and distributed under the DINOv3 License (copy in LICENSE-DINOv3.md). Built with DINOv3.

Unless the terms above say otherwise, this dataset is released under CC BY-NC 4.0.

Citation

@misc{internò2026geophysgeometryphysicalplausibility,
      title={GEOPHYS: The Geometry of Physical Plausibility}, 
      author={Christian Internò and Alexander Pondaven and Habon Issa and Fabio Pizzati and Francesco Pinto and Markus Olhofer and Ivan Laptev and Philip Torr and Eero P. Simoncelli and Barbara Hammer and David Klindt},
      year={2026},
      eprint={2606.20707},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2606.20707}, 
}