ACWM-Phys Dataset

ACWM-Phys: Investigating Generalized Physical Interaction in Action-Conditioned Video World Models

Haotian Xue†, Yipu Chen*, Liqian Ma*, Zelin Zhao, Lama Moukheiber, Yongxin Chen
Georgia Institute of Technology

[Project Page] · [Paper] · [Checkpoints]

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Overview

ACWM-Phys is a benchmark dataset for evaluating action-conditioned video world models under diverse physical dynamics. It spans 8 environments across 4 physics regimes, each with 1,000 training trajectories and controlled in-distribution (InD) / out-of-distribution (OoD) test splits.

Category	Environments	OoD Axis
Rigid-Body	Push Cube, Stack Cube	Unseen workspace / object count
Deformable	Push Rope, Cloth Move	Unseen stiffness / cloth size
Particle	Push Sand, Pour Water	Doubled particle count / unseen water volume
Kinematics	Robot Arm, Reacher	Expanded workspace / corner-sector goals

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Repository Structure

rigid_dynamics/
├── push_block/
│   ├── ind_train/   (1,000 episodes)
│   ├── ind_test/
│   └── ood_test/
└── stack_cube/      (same structure)
deformable/
├── push_rope/
└── clothmove/
particle/
├── push_sand/
└── pour_water/
kinematics/
├── robot_arm_64/
└── reacher/

Each split directory contains:

• episode_{i}.mp4 — RGB video at 10 fps, 240×240 (240×400 for Push Sand)
• metadata.pt — torch.load → list of episode dicts

Each episode dict has:

Field	Type	Description
video_path	str	Filename relative to split dir, e.g. episode_0.mp4
actions	FloatTensor [T, action_dim]	Per-step action sequence
length	int	Number of frames T
seed	int	Simulation random seed
episode_idx	int	Global episode index (some environments)

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Download

huggingface-cli download t1an/ACWM-Phys --repo-type dataset --local-dir ./data
export ACWM_DATA_ROOT=./data

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Usage Example

import torch

metadata = torch.load("data/rigid_dynamics/push_block/ind_train/metadata.pt", weights_only=False)
entry = metadata[0]
# entry["video_path"]  → "episode_0.mp4"
# entry["actions"]     → Tensor of shape [T, 2]
# entry["length"]      → 16

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Citation

Citation

Coming soon.