Upload ActionBench dataset (imgs + surfaces)

README.md

@@ -11,6 +11,7 @@ tags:
   - benchmark
   - synthetic
 pretty_name: ActionBench
+viewer: false
 ---
 
 <div align="center">
@@ -55,14 +56,7 @@ predictions/
 └── ...
 ```
 
-Download the ActionBench dataset:
-
-```bash
-pip install huggingface_hub
-huggingface-cli download facebook/actionbench --repo-type dataset --local-dir data/actionbench/
-```
-
-Then run the evaluation script in [ActionMesh](https://github.com/facebookresearch/actionmesh):
+Download Actionbench dataset, then run the evaluation script in [ActionMesh](https://github.com/facebookresearch/actionmesh):
 
 ```bash
 python actionbench/evaluate.py \
@@ -72,6 +66,8 @@ python actionbench/evaluate.py \
     --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
@@ -86,9 +82,9 @@ See the LICENSE file for details about the license under which this dataset is m
 If you use ActionBench, please cite the following paper:
 
 ```bibtex
-@inproceedings{ActionMesh2025,
+@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 = {2025},
+  year = {2026},
 }
 ```

data/000-000_03b69da8d2c94b5999bcf2605ee2ecd9/surfaces.npy

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