Evaluates cross-dataset generalization of co-speech gesture generation on a standard English benchmark, measuring gesture quality, beat consistency, and diversity. Use when the user wants to benchmark on BEATv2, or asks about evaluating this task. Reports FGD.
Scanned 9/11/2026
Install to Claude Code
npx -y skills add qhjqhj00/research-skills-pool --skill beatv2-eval --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Beatv2 Eval?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/qhjqhj00-beatv2-eval)More formats (shields.io, HTML) on the badges page.
---
name: beatv2-eval
description: Evaluates cross-dataset generalization of co-speech gesture generation on a standard English benchmark, measuring gesture quality, beat consistency, and diversity. Use when the user wants to benchmark on BEATv2, or asks about evaluating this task. Reports FGD.
metadata:
skill_kind: dataset_eval
source_arxiv: 2604.02908
bibtex_key: jin2026sentiavatar
confidence: high
---
# beatv2-eval
> SentiAvatar: Towards Expressive and Interactive Digital Humans — Chuhao Jin et al. (2026) (arXiv:2604.02908, 2026)
## What this evaluates
Evaluates cross-dataset generalization of co-speech gesture generation on a standard English benchmark, measuring gesture quality, beat consistency, and diversity.
## Datasets
- **BEATv2** — total ?; splits: test (-1)
## Metrics
- `FGD` **(primary)** — range: other
- Fréchet Gesture Distance measuring gesture quality distribution match. Lower is better.
- `BC` — range: [0, 1]
- Beat Consistency measuring synchronization with audio beats. Higher is better.
- `Diversity` — range: other
- Mean pairwise L2 distance among motion latent features. Higher is better.
## Input / output format
**Input**: Audio and text conditioning.
**Output**: Generated 3D co-speech gesture sequences.
## Scoring recipe
```python
FGD: Compute mean and covariance of real vs generated gesture latent features; FGD = ||μ_r - μ_g||^2 + Tr(Σ_r + Σ_g - 2(Σ_r Σ_g)^0.5).
BC: Measure consistency of generated gestures with audio beat events.
Diversity: Compute mean pairwise L2 distance among generated gesture latent features.
```
## Common pitfalls
- FGD and BC are standard BEATv2 metrics; lower FGD and higher BC indicate better performance.
- Diversity is computed on BEATv2's latent space, not raw coordinates.
- Model is retrained on BEATv2 before evaluation to verify cross-dataset generalization.
## Evidence (verbatim from paper)
> On BEATv2, we follow the standard protocol and report FGD ($\downarrow$, Fréchet Gesture Distance), BC ($\uparrow$, Beat Consistency), and Diversity.
## Citation
```bibtex
@misc{jin2026sentiavatar,
title={SentiAvatar: Towards Expressive and Interactive Digital Humans},
author={Chuhao Jin et al. (2026)},
year={2026},
note={arXiv:2604.02908}
}
```
- arXiv: 2604.02908
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!