# Towards More Realistic Human-Robot Conversation: A Seq2Seq-based Body Gesture Interaction System

arXiv: https://arxiv.org/abs/1905.01641 · HTML: https://mlsanity.com/paper/1905.01641/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2019.

## Built on by (most cited first, 2 of 2)

- [Didn't see that coming: a survey on non-verbal social human behavior forecasting](https://mlsanity.com/paper/2203.02480/) (2022)
- [Comparison of Spatio-Temporal Models for Human Motion and Pose Forecasting in Face-to-Face Interaction Scenarios](https://mlsanity.com/paper/2203.03245/) (2022)

## Abstract (arXiv)

This paper presents a novel system that enables intelligent robots to exhibit realistic body gestures while communicating with humans. The proposed system consists of a listening model and a speaking model used in corresponding conversational phases. Both models are adapted from the sequence-to-sequence (seq2seq) architecture to synthesize body gestures represented by the movements of twelve upper-body keypoints. All the extracted 2D keypoints are firstly 3D-transformed, then rotated and normalized to discard irrelevant information. Substantial videos of human conversations from Youtube are collected and preprocessed to train the listening and speaking models separately, after which the two models are evaluated using metrics of mean squared error (MSE) and cosine similarity on the test dataset. The tuned system is implemented to drive a virtual avatar as well as Pepper, a physical humanoid robot, to demonstrate the improvement on conversational interaction abilities of our method in practice.