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PhysInOne

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Text-to-Video
Image-to-Video
Video-to-Video
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Video
3D
Languages:
English
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1M<n<10M
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video
3d
synthetic-data
physical-reasoning
visual-physics
world-model
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PhysInOne: Visual Physics Learning and Reasoning in One Suite

vLAR Group | The Hong Kong Polytechnic University | Syai Singapore | Meta

CVPR 2026

📌 Summary

PhysInOne is a large-scale synthetic dataset for visual physics learning and reasoning, containing 153,810 dynamic 3D scenes and 2 million annotated videos across 71 physical phenomena in mechanics, optics, fluid dynamics, and magnetism. Each scene features complex multi-object and multi-physics interactions with rich annotations, including RGB videos, depth maps, object masks, 3D trajectories, camera poses, object meshes, material properties, and textual descriptions. The dataset supports research on physics-aware video generation, future frame prediction, physical property estimation, motion transfer, physical reasoning, and world models.

153,810
Dynamic 3D scenes
2M
Annotated videos
71
Physical phenomena
4
Physics domains

🚀 Release Timetable

Component Progress Status Notes
SubSet ██████████100% Released
Rendered Data - Train █░░░░░░░░░ 5%(5277/122,988) In progress Last updated: May 21
Rendered Data - Test ░░░░░░░░░░ 0%(0/15411) In progress
Rendered Data - Val ░░░░░░░░░░ 1%(103/15411) In progress
3D Assets ░░░░░░░░░░ 0% Not released Expected around June
Leaderboard ░░░░░░░░░░ 0% Ongoing Link will be added when available
Baseline code ░░░░░░░░░░ 0% Not released Expected around June
Data processing ░░░░░░░░░░ 0% Not released Expected around June

Due to the large scale of PhysInOne, the rendered data and annotations are split across multiple Hugging Face repositories. Below is the list:

For large-scale downloading and filtering, please refer to the How to Download section below.

📊 Data Splits

PhysInOne is divided into Train, Val, and Test splits, containing 122,988, 15,411, and 15,411 scenes, respectively. The three splits are generated with completely distinct 3D meshes and backgrounds, ensuring that no scene or visual content is shared across splits. This separation provides a robust benchmark for evaluating generalization and physical reasoning.

📥 How to Download

1. Download Scripts

Please download the assets folder, which contains:

metadata/
  repo_assignment.txt
  repo_map.json
scripts/
  filter_cases.py
  download.py

PhysInOne is distributed across multiple Hugging Face dataset repositories because of its large scale. We provide two scripts for selecting and downloading cases:

  • filter_cases.py: exports selected cases to JSON.
  • download.py: downloads the selected case zip files from the corresponding repositories.

2. Install Dependencies

pip install huggingface_hub tqdm

3. Filter Cases

Export a JSON file containing case information matching your filter criteria, for use in subsequent download scripts. The filter_cases.py script supports selection by:

  • Split: train, val, test
  • Activity complexity: single, double, triple
  • Physical phenomenon abbreviation: MovingHitsFixed, FrictionStop, LiquidTension, ..., GranularFall
  • Number of cases: globally sample num cases after filtering

The following examples show common filtering workflows.

3.1 Filter by split

python scripts/filter_cases.py \
  --split train \
  --output selected_cases.json

3.2 Filter by split and activity complexity

python scripts/filter_cases.py \
  --split train \
  --activity_type double \
  --output selected_cases.json

3.3 Filter by physical phenomenon abbreviation

By default, phenomenon matching uses contains mode. For example, the following command selects all double-physics cases that contain FrictionStop(For all physical phenomena and their abbreviations, please refer to the Data Fields section below.):

python scripts/filter_cases.py \
  --split train \
  --activity_type double \
  --phenomena FrictionStop \
  --match_mode contains \
  --output selected_cases.json

For exact matching, use --match_mode exact. Order does not matter. For example, this command selects cases whose phenomenon set is exactly {AccelConcaveSpin, AccelSurfaceSpin}:

python scripts/filter_cases.py \
  --split train \
  --activity_type double \
  --phenomena AccelConcaveSpin AccelSurfaceSpin \
  --match_mode exact \
  --output selected_cases.json

3.4 Randomly sample a fixed number of cases

--num is the global number of cases sampled after filtering (This is because the number of cases meeting the filtering criteria may be much larger than the number you need.). The default random seed is 42.

python scripts/filter_cases.py \
  --split train \
  --activity_type double \
  --phenomena FrictionStop \
  --num 3000 \
  --seed 42 \
  --output selected_cases.json

4. Download Selected Cases

Each selected case is downloaded as a zip file from the corresponding Hugging Face shard repository.

python scripts/download.py \
  --selection selected_cases.json \
  --output_dir ./PhysInOne

If the dataset repositories are gated or private, pass a Hugging Face token:

python scripts/download.py \
  --selection selected_cases.json \
  --output_dir ./PhysInOne \
  --token YOUR_HF_TOKEN

By default, downloaded zip files are saved into a flat output folder. To preserve shard/split/activity structure:

python scripts/download.py \
  --selection selected_cases.json \
  --output_dir ./PhysInOne \
  --keep_shard_structure

The preserved structure is:

PhysInOne/
  physinone_part1/
    Train/
      DoublePhysics/
        AccelConcaveSpin_AccelSurfaceSpin__bg070__K5ER39_trajectory.zip

📦 Dataset Structure

AccelConcaveSpin__bg010__m3Zrtz_trajectory/
├── AccelConcaveSpin__bg010__m3Zrtz_trajectory.json
├── blender_CineCamera_0.json
├── blender_CineCamera_1.json
├── blender_CineCamera_2.json
├── blender_CineCamera_3.json
├── blender_CineCamera_5.json
├── blender_CineCamera_6.json
├── blender_CineCamera_7.json
├── blender_CineCamera_8.json
├── blender_CineCamera_9.json
├── blender_CineCamera_10.json
├── blender_CineCamera_11.json
├── blender_CineCamera_12.json
├── blender_CineCamera_Moving.json
├── cameras.ply
├── caption.txt
├── points3d.ply
├── recorder_stats.json
├── static_camera_list.txt
├── CineCamera_0/
│   ├── depth/  (0000.npz ~ 0089.npz, 90 files)
│   ├── rgb/    (0000.jpg ~ 0089.jpg, 90 files)
│   └── seg/    (0000.npz ~ 0089.npz, 90 files)
├── CineCamera_1/
│   ...
├── CineCamera_2/
│   ...
├── CineCamera_3/
│   ...
├── CineCamera_5/
│   ...
├── CineCamera_6/
│   ...
├── CineCamera_7/
│   ...
├── CineCamera_8/
│   ...
├── CineCamera_9/
│   ...
├── CineCamera_10/
│   ...
├── CineCamera_11/
│   ...
├── CineCamera_12/
│   ...
└── CineCamera_Moving/
    ├── depth/  (0000.npz ~ 0089.npz, 90 files)
    ├── rgb/    (0000.jpg ~ 0089.jpg, 90 files)
    └── seg/    (0000.npz ~ 0089.npz, 90 files)

📝 Annotation Details

PhysInOne provides synchronized visual and physical annotations for each dynamic 3D scene.

•Depth

Depth maps are provided in .npz format and are expressed in meters.

•Segmentation

Segmentation masks encode background, static foreground objects, and dynamic foreground objects.

Expected encoding:

Pixel Value Meaning
0 Background
1-127 Static foreground objects
128-255 Dynamic foreground objects

•Captions

Each scene includes a caption.txt file containing an English paragraph that describes the visual elements and the physical activity.

•Trajectories

Each sequence includes a JSON file with the same name as the sequence. This file records the per-frame poses of selected dynamic objects, including their rotations and translations in the world coordinate system.

JSON file structure

The JSON file contains two main parts:

  • sequence_info: basic information about the sequence, such as the total number of frames and the frame rate (fps).
  • actors: pose and metadata for dynamic objects, including object category, asset index path, actor type, and transformation data.
Actor-level pose storage

For each actor, the storage format depends on its actor_type:

  • If actor_type is solid, the object is treated as a single rigid body. Its per-frame pose is stored in the actor-level transform_data field, while the components field is empty.
  • If actor_type is interactable, the object is represented by multiple components. The actor-level transform_data field is empty, and the per-component poses are stored under components[component_name].transform_data.
Pose entry format in transform_data

Each entry in transform_data describes the pose of the object or component at a specific frame:

  • frame: frame index in the sequence
  • time_seconds: timestamp of the frame in seconds
  • transform: object or component transformation, including:
    • location: 3D position
    • rotation: rotation quaternion
    • scale: 3D scale

•Cameras

Each scene includes multiple camera viewpoints to support 3D perception, reconstruction, and physics-based visual reasoning tasks.

  • Taichi-based MPM simulations (elastic solids, plasticine, granular substances, some Newtonian and Non-Newtonian fluids): 15 static cameras + 1 dynamic camera
  • Other scenes: 12 static cameras + 1 dynamic camera

Note: Camera IDs are not fixed across cases. The camera index mapping for each case is stored in static_camera_list.txt under that case directory.

Camera JSON structure

Per camera:

  • frames: list of per-frame 4×4 camera-to-world (C2W) transformation matrices in Blender coordinate system
  • camera_angle_x: horizontal field of view (FOV) in radians, can be used to compute intrinsic focal length
  • img_w, img_h: image width and height in pixels
  • trajectory_name: name of the camera trajectory
  • total_frames, fps: number of frames and frames per second
Dynamic camera behavior
  • The dynamic camera moves along a path randomly sampled on a hemisphere surrounding the center of the main objects, providing diverse viewpoints.
  • JSON frame indices correspond directly to video frame indices.

•Point Clouds

Each scene should include points.ply.

The initial point cloud was obtained by back-projecting the pixels from each camera view in the first frame based on depth, followed by randomly sampling 100,000 points from the resulting data.

🧩 Data Fields

•Physical Phenomenon and Abbreviations

ID Physical Phenomenon Abbreviation Related Physical Laws
1 Object collide with static, stationary objects MovingHitsFixed Laws of Momentum
2 Moving objects collide with non-static stationary objects MovingHitsStationary Laws of Momentum
3 Two moving objects collide MovingHitsMoving Laws of Momentum
... ... ... ...
Click to expand full abbreviation table
ID Physical Phenomenon Abbreviation Related Physical Laws
4 Objects in equilibrium of wind and gravity WindGravityBalance Equilibrium, Aerodynamics, Gravity
5 Wind applied to a stationary object WindPushStationary Aerodynamics
6 Wind applied to objects moving in same direction WindPushSameDir Aerodynamics
7 Wind applied to objects moving in the opposite direction WindPushOppDir Aerodynamics
8 Wind applied to moving objects changes its velocity (applied at an angle) WindDeflectMotion Aerodynamics
9 Object thrown up with angle ObliqueProjectile Gravity
10 Objects falling straight down VerticalFall Gravity
11 Objects rolling down a straight slope RollDownSlope Gravity, Friction
12 Objects rolling up a slope RollUpSlope Gravity, Friction
13 Magnetic Attraction MagnetAttract Magnetism
14 Magnetic Repulsion MagnetRepel Magnetism
15 Objects near uniformly rotating pillar UniformPanelSpin Laws of Rotation
16 Objects near acceleratingly rotating pillar AccelPanelSpin Laws of Rotation
17 Objects inside uniformly rotating bowl UniformConcaveSpin Equilibrium, Laws of Rotation, Gravity, Friction
18 Objects inside acceleratingly rotating bowl AccelConcaveSpin Equilibrium, Laws of Rotation, Gravity, Friction
19 Objects on uniformly rotating plane UniformSurfaceSpin Laws of Rotation, Friction
20 Objects on acceleratingly rotating plane AccelSurfaceSpin Laws of Rotation, Friction
21 Objects on coarse surface with friction FrictionStop Friction
22 Spring is compressed SpringCompress Laws of Elasticity
23 Spring is stretched SpringStretch Laws of Elasticity
24 Breakable object shatters ImpactFracture Laws of Plasticity
25 Mirror shatters MirrorFragmentReflect Laws of Plasticity, Law of Reflection
26 Elastic rope connection ElasticCouple Laws of Elasticity
27 Object bounces off spring board SpringboardRebound Laws of Elasticity, Laws of Momentum
28 Objects interact with a balanced seesaw SeesawCenterPivot Laws of Torque
29 Objects interact with an imbalanced seesaw SeesawOffsetPivot Laws of Torque
30 Free balloon floats to ceiling BalloonFloat Laws of Buoyancy
31 Tethered balloon pulls string taut BalloonTether Laws of Buoyancy, Rope Restraint
32 Multiple balloons lifting BalloonLift Laws of Buoyancy, Rope Restraint, Gravity
33 Laser hits flat mirror and reflects FixedPlanarRedirect Law of Reflection
34 Laser reflects off multiple mirrors FixedArrayRedirect Law of Reflection
35 Laser hits and reflects off concave mirror FixedConcaveRedirect Law of Reflection
36 Laser hits and reflects off convex mirror FixedConvexRedirect Law of Reflection
37 Mirror sweeps beam DynMirrorRedirect Law of Reflection
38 Laser blocked by object LaserBlock Light Obstruction
39 Mirror reflection MirrorReflect Law of Reflection
40 Cart moving forward with rolling wheels CartMove Complex Mechanical Structure Constraints
41 Objects on rotating turntable flies off RotTurnableInertia Laws of Rotation, Friction, Laws of Inertia
42 Rotating block pushes another objects RotBoardInertia Laws of Inertia, Laws of Rotation, Laws of Momentum
43 One object carrying another LinCarryInertia Laws of Inertia, Friction
44 Catapult launches objects CatapultLaunch Special Mechanical Structure, Gravity
45 Chain suspends objects ChainSuspend Complex Mechanical Structure Constraints, Gravity
46 Objects Swing SimplePendulum Laws of Pendulum Motion, Gravity
47 Double Pendulum Moves DoublePendulum Laws of Multiple Pendulum Motion, Gravity
48 Crank push objects CrankPush Special Mechanical Structure
49 Wall composed of square blocks collapses BlockWallCollapse Gravity, Structural Stability
50 Wooden board supported by sticks collapses StickSupportFail Gravity, Structural Stability
51 Objects float on the fluid surface FloatOnLiquid Laws of Buoyancy, Fluid Dynamics
52 Objects drop into the fluid DropInLiquid Laws of Buoyancy, Fluid Dynamics
53 Objects' movement causes fluid motion MovingObjDriveLiquid Fluid Dynamics
54 Flowing fluid carries objects along LiquidCarryMovingObj Laws of Buoyancy, Fluid Dynamics
55 Fluid flows against stationary objects LiquidHitFixedObj Fluid Dynamics
56 Fluid transfers from one container to another LiquidTransfer Fluid Dynamics, Conservation of Mass, Surface Tension
57 Fluid passes through several connected containers LiquidMultiTransfers Fluid Dynamics, Conservation of Mass, Surface Tension
58 Fluid flows through grid-like structures LiquidThroughGrid Fluid Dynamics, Conservation of Mass
59 Fluid moves across mountainous or uneven landscapes LiquidAcrossUneven Fluid Dynamics
60 Increasing fluid volume elevates the surface level LiquidRise Fluid Dynamics
61 Fluid flows along the contours of an object's surface LiquidAlongContours Fluid Dynamics
62 Jet-like fluid projection upward or outward JetLiquid Fluid Dynamics
63 Fluid exhibits surface tension LiquidTension Fluid Dynamics, Laws of Surface Tension
64 Fluid refracts light when crossing media LiquidRefraction Fluid Dynamics, Optics, Law of Refraction (Snell's Law)
65 Sticky fluid drips and accumulates on objects StickyToObjects Laws of Cohesion, Viscous Flow
66 Sticky fluid falls from an object's surface StickyFromObjects Laws of Cohesion, Laws of Viscous Flow (Navier-Stokes)
67 An elastic object falls and bounces on another surface ElasticFall Laws of Elasticity
68 A plasticine object falls and deforms on a surface PlasticineFall Laws of Plasticity
69 A Newtonian fluid falls and spreads across a surface NewtonianFluidFall Laws of Viscous Flow (Navier-Stokes)
70 A Non-Newtonian fluid falls and flows with variable resistance NonNewtonianFluidFall Laws of Viscoplastics Flow
71 A granular substance falls and disperses across a surface GranularFall Laws of Friction

Example: MovingHitsStationary_WindDeflectMotion_BalloonFloat__bg140__TZlWl1

  • The first three components correspond to specific physical phenomenon abbreviations (see table above).
  • bg140 indicates that the scene uses background number 140.
  • The last six-character string (TZlWl1) is a unique hash code generated for this scene to guarantee uniqueness.

This naming convention allows users to quickly parse scene metadata and link it to the corresponding physical phenomena, background, and unique identifier.

📜 License

All 3D assets and materials included in PhysInOne have been sourced from publicly available platforms and verified to carry licenses compatible with non-commercial use. These include:

  • SketchFab: assets under various licenses, verified that AI-related usage is allowed.
  • Fab: assets under CC BY or Unreal Engine Standard License, explicitly permitting AI-related usage.
  • BlenderKit: distributed under Royalty-Free (RF) license.
  • ShareTextures: textures under CC0 license.

In total, assets comply with licenses including CC BY-NC, CC BY-SA, CC BY-NC-SA, CC0, CC BY, and RF, ensuring all files can be legally used for building a non-commercial dataset. Users must adhere to the original licenses for any redistribution or derivative work.

📚 Citation

If you use PhysInOne in your research, please cite:

@misc{zhou2026physinonevisualphysicslearning,
      title={PhysInOne: Visual Physics Learning and Reasoning in One Suite}, 
      author={Siyuan Zhou and Hejun Wang and Hu Cheng and Jinxi Li and Dongsheng Wang and Junwei Jiang and Yixiao Jin and Jiayue Huang and Shiwei Mao and Shangjia Liu and Yafei Yang and Hongkang Song and Shenxing Wei and Zihui Zhang and Peng Huang and Shijie Liu and Zhengli Hao and Hao Li and Yitian Li and Wenqi Zhou and Zhihan Zhao and Zongqi He and Hongtao Wen and Shouwang Huang and Peng Yun and Bowen Cheng and Pok Kazaf Fu and Wai Kit Lai and Jiahao Chen and Kaiyuan Wang and Zhixuan Sun and Ziqi Li and Haochen Hu and Di Zhang and Chun Ho Yuen and Bing Wang and Zhihua Wang and Chuhang Zou and Bo Yang},
      year={2026},
      eprint={2604.09415},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2604.09415}, 
}

📮 Contact

For questions about the dataset, please contact:
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