Datasets:

facebook
/

actionbench

license: other
task_categories:
  - image-to-3d
  - video-classification
tags:
  - 3d
  - video
  - point-cloud
  - animation
  - benchmark
  - synthetic
pretty_name: ActionBench

🎬 ActionBench: Paired Video-3D Synthetic Benchmark

📖 Overview

ActionBench is a benchmark dataset of 128 paired video ↔ animated point-cloud samples for evaluating animated 3D mesh generation from video.

Each sample contains:

Video: 16 RGBA frames with alpha mask
Animated Point Cloud: Surface points sampled on the animated object with shape (T, V, 6) where:
- T=16: number of keyframes
- V: number of vertices (points randomly sampled on the mesh surface)
- 6: position (x, y, z) + normal (nx, ny, nz) for each point
Note: The point cloud is tracked: each point index corresponds to the same surface point deformed across timesteps, providing dense correspondences over time.

The dataset consists of synthetic scenes of animated objects from ObjaverseXL, rendered using Blender 3.5.1.

📊 Evaluation

To evaluate on ActionBench, produce a list of animated meshes saved as .glb files.

Each subdirectory must be named with the corresponding uid from ActionBench:

predictions/
├── <uid_1>/
│   ├── mesh_00.glb
│   ├── mesh_01.glb
│   └── ...
├── <uid_2>/
│   ├── mesh_00.glb
│   └── ...
└── ...

Download the ActionBench dataset:

pip install huggingface_hub
huggingface-cli download facebook/actionbench --repo-type dataset --local-dir data/actionbench/

Then run the evaluation script in ActionMesh:

python actionbench/evaluate.py \
    --pred_root predictions/ \
    --gt_root data/actionbench/data/ \
    --output_csv results.csv \
    --device cuda

Metrics are described in the ActionMesh paper:

CD-3D: Chamfer Distance 3D — measures geometric accuracy per frame
CD-4D: Chamfer Distance 4D — measures spatio-temporal consistency
CD-M: Motion Chamfer Distance — measures motion fidelity

🏛️ License

See the LICENSE file for details about the license under which this dataset is made available.

📚 Citation

If you use ActionBench, please cite the following paper:

@inproceedings{ActionMesh2025,
  author = {Remy Sabathier and David Novotny and Niloy Mitra and Tom Monnier},
  title = {ActionMesh: Animated 3D Mesh Generation with Temporal 3D Diffusion},
  year = {2025},
}