hhkka/FluidGen-Benchmark

🌊 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

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.

📁 Layout

benchmark.json                      one entry per scene (below)
samples/<sample_id>/
  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

{
  "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, byte-identical to the flow used in the paper:

python fluidgen/simulation/render_conditions.py \
    --scene   samples/<id>/scene/scene.json \
    --sim_dir samples/<id>/simulation \
    --out_dir <work>/<id>/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

@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},
}