---
pretty_name: FluidGen Benchmark
task_categories:
- image-to-video
tags:
- physics
- fluid-simulation
- video-generation
- benchmark
size_categories:
- n<1K
---
🌊 FluidGen Benchmark
Fluid-object interaction scenes with full physics simulation rollouts
The benchmark introduced in **Fluid-Gen-Zero: Grounding Pretrained Video
Generators in Physics without Training**. Each of its **43 scenes** pairs an
input image and a prompt with a physics simulation rollout of that scene, so
generated videos can be measured against where the object and the fluid
*should* go, not only against how plausible they look.
| | |
|---|---|
| 🎬 Scenes | 43, across 10 primary fluid categories |
| ⏱️ Rollout | 401 frames per scene (0.01 s of simulated time per frame) |
| 🧊 3D points | object and fluid particle trajectories, every frame |
| 🎭 Masks | projected object and fluid masks, every frame |
| 🌀 Optical flow | projected simulation optical flow, every frame |
| 🏷️ Annotations | fluid category, object material, interaction intensity, camera view, background, tags |
## 🚀 Quick start
```python
import json, numpy as np
from huggingface_hub import snapshot_download
root = snapshot_download("hhkka/FluidGen-Benchmark", repo_type="dataset")
samples = json.load(open(f"{root}/benchmark.json"))
s = samples[0]
print(s["prompt"], s["primary_fluid_category"])
obj = np.load(f"{root}/{s['simulation_dir']}/fg_traj.npz")["xyz"] # (401, N_obj, 3)
fluid = np.load(f"{root}/{s['simulation_dir']}/fluid_traj.npz")["xyz"] # (401, N_fluid, 3)
```
To generate videos on the benchmark with Fluid-Gen-Zero, see the
[code repository](https://github.com/hongh0/Fluid-Gen-Zero).
## 📁 Layout
```
benchmark.json one entry per scene (below)
samples//
image.png input image I_0 (= first frame)
simulation/ simulation rollout, in the layout the code reads as a "bundle"
fg_traj.npz xyz (401, N_obj, 3) object particles, world frame (z up, metres)
fluid_traj.npz xyz (401, N_fluid, 3) fluid particles, world frame
segmap/fg_mask.mp4 projected object mask, 401 frames, image resolution
segmap/fluid_mask.mp4 projected fluid mask, 401 frames
segmap/segmap.mp4 both masks in one colour-coded video
optical_flow.mp4 projected simulation optical flow (Middlebury colours), 400 frames
trajectory_2d.npz object_xy, fluid_xy (401, 2): projected centroids, pixels
object_trajectory.txt same object centroid track as "x,y" lines
fluid_trajectory.txt same fluid centroid track
simulation.mp4 simulation preview (point rendering)
warped_reference/motion_signal.mp4 appearance-preserving warped reference video (480x832)
warped_reference/mask.mp4 its object mask
warped_reference/{first_frame,background,mask0}.png, disp_vis.mp4 intermediate products
rollout.json simulator settings: particles, substeps, domain,
external-force schedule
scene/
scene.json camera (K, T_world_cam, width, height), object pose / scale,
materials, simulation domain, metric scale
object.glb reconstructed object mesh (SAM 3D Objects), object frame
object_material.json, fluid_material.json
```
benchmark.json entry
```json
{
"sample_id": "000_birdseye_view_showing_a_gold_coin_resting_calmly",
"prompt": "Bird's-eye view showing a gold coin resting calmly on ...",
"image": "samples/000_.../image.png",
"simulation_dir": "samples/000_.../simulation",
"scene_json": "samples/000_.../scene/scene.json",
"num_frames": 401,
"primary_fluid_category": "K. MULTI_PHASE_MIXING_SWIRL",
"annotations": {"object_type": "METAL", "interaction_intensity": "WEAK",
"camera_view": "aerial_top_down", "background_style": "minimalist_studio",
"secondary_tags": ["coin", "..."], "object_shape_hint": "coin/disk", "...": "..."}
}
```
## 🌀 Dense optical flow
The dense flow (`(400, 2, H, W)` float32 pixel displacements, about 3 GB per
scene) is not stored. It is a deterministic projection of the particle
trajectories through the camera in `scene.json`, and is re-rendered with the
[Fluid-Gen-Zero code](https://github.com/hongh0/Fluid-Gen-Zero), byte-identical
to the flow used in the paper:
```bash
python fluidgen/simulation/render_conditions.py \
--scene samples//scene/scene.json \
--sim_dir samples//simulation \
--out_dir //s9_cond # -> optical_flow.npy
```
## 📊 Statistics
| Primary fluid category | Scenes |
|---|:---:|
| High-viscosity fluid | 10 |
| Paint / ink / dye | 9 |
| Splash / impact / collision | 8 |
| Water stream / downpour | 5 |
| Multi-phase mixing / swirl | 3 |
| Pool / tank / container water | 3 |
| Ocean / sea waves | 2 |
| Rain / mist / spray droplets | 1 |
| River / stream flow | 1 |
| Other fluid | 1 |
Object material: organic 23, metal 7, plastic 5, ceramic 4, wood / stone / fabric / unknown 1 each.
Interaction intensity: weak 10, medium 23, strong 10.
Camera view: aerial top-down 28, oblique 8, close-up macro 4, frontal 2, side 1.
## 📝 Citation
```bibtex
@article{fluidgenzero2026,
title = {Fluid-Gen-Zero: Grounding Pretrained Video Generators in Physics without Training},
author = {Huang, Hong and Liu, Yuqiu and You, Chenyu and Martin, Daniel and Zou, Chuhang and Chen, Wuyang},
journal = {arXiv preprint arXiv:2610.10984},
year = {2026},
}
```