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.
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---
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: [REDACTED]
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