# Integrating Representational Gestures into Automatically Generated Embodied Explanations and its Effects on Understanding and Interaction Quality

arXiv: https://arxiv.org/abs/2406.12544 · HTML: https://mlsanity.com/paper/2406.12544/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 102 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 18 of 18)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [DataComp: In search of the next generation of multimodal datasets](https://mlsanity.com/paper/2304.14108/) (2023)
- [BEAT: A Large-Scale Semantic and Emotional Multi-Modal Dataset for Conversational Gestures Synthesis](https://mlsanity.com/paper/2203.05297/) (2022)
- [Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization](https://mlsanity.com/paper/2012.04456/) (2020)
- [One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer](https://mlsanity.com/paper/2303.16160/) (2023)
- [The GENEA Challenge 2022: A large evaluation of data-driven co-speech gesture generation](https://mlsanity.com/paper/2208.10441/) (2022)
- [Evaluating gesture generation in a large-scale open challenge: The GENEA Challenge 2022](https://mlsanity.com/paper/2303.08737/) (2023)
- [The GENEA Challenge 2023: A large scale evaluation of gesture generation models in monadic and dyadic settings](https://mlsanity.com/paper/2308.12646/) (2023)
- [AQ-GT: a Temporally Aligned and Quantized GRU-Transformer for Co-Speech Gesture Synthesis](https://mlsanity.com/paper/2305.01241/) (2023)
- [Forms of Understanding for XAI-Explanations](https://mlsanity.com/paper/2311.08760/) (2023)
- [Augmented Co-Speech Gesture Generation: Including Form and Meaning Features to Guide Learning-Based Gesture Synthesis](https://mlsanity.com/paper/2307.09597/) (2023)

## Abstract (arXiv)

In human interaction, gestures serve various functions such as marking speech rhythm, highlighting key elements, and supplementing information. These gestures are also observed in explanatory contexts. However, the impact of gestures on explanations provided by virtual agents remains underexplored. A user study was carried out to investigate how different types of gestures influence perceived interaction quality and listener understanding. This study addresses the effect of gestures in explanation by developing an embodied virtual explainer integrating both beat gestures and iconic gestures to enhance its automatically generated verbal explanations. Our model combines beat gestures generated by a learned speech-driven synthesis module with manually captured iconic gestures, supporting the agent's verbal expressions about the board game Quarto! as an explanation scenario. Findings indicate that neither the use of iconic gestures alone nor their combination with beat gestures outperforms the baseline or beat-only conditions in terms of understanding. Nonetheless, compared to prior research, the embodied agent significantly enhances understanding.