actionbench

<div align="center"> <h1>🎬 ActionBench: Paired Video-3D Synthetic Benchmark</h1> <img src="actionbench.gif" alt="ActionBench" width="100%"> </div> ## 📖 Overview ActionBench is a benchmark dataset of **128 paired video ↔ animated point-cloud samples** for evaluating animated 3D mesh generation from video. The dataset consists of synthetic scenes of animated objects from [ObjaverseXL](https://objaverse.allenai.org/), rendered using **Blender 3.5.1**. Each sample contains: - **Video**: 16 RGBA frames with alpha mask - **Camera** (`camera.json`): Camera parameters using Blender convention (`X_cam = X @ R^T + T`, camera looks along -Z). See [`projection.py`](projection.py) for how to project the point cloud onto the image plane. - **Animated Point Cloud**: Surface points sampled on the animated object with shape `(T, V, 6)` where: - `T=16`: number of keyframes - `V=100_000`: 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 animation lie in normalized space `[-1., 1.]^3`. ## 📊 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 Actionbench dataset, then run the evaluation script in [ActionMesh](https://github.com/facebookresearch/actionmesh): ```bash python actionbench/evaluate.py \ --pred_root predictions/ \ --gt_root data/actionbench/data/ \ --output_csv results.csv \ --device cuda ``` > **Note:** Evaluation requires the same dependencies as [ActionMesh](../README.md) plus [PyTorch3D](https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md). Metrics are described in the [ActionMesh paper](https://arxiv.org/abs/2601.16148): - **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: ```bibtex @inproceedings{ActionMesh2026, author = {Remy Sabathier and David Novotny and Niloy Mitra and Tom Monnier}, title = {ActionMesh: Animated 3D Mesh Generation with Temporal 3D Diffusion}, year = {2026}, } ```