# ChaLearn Looking at People: IsoGD and ConGD Large-scale RGB-D Gesture Recognition

arXiv: https://arxiv.org/abs/1907.12193 · HTML: https://mlsanity.com/paper/1907.12193/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition](https://mlsanity.com/paper/2112.09129/) (2021)
- [A Unified Multimodal De- and Re-coupling Framework for RGB-D Motion Recognition](https://mlsanity.com/paper/2211.09146/) (2022)
- [A Transformer-Based Multi-Stream Approach for Isolated Iranian Sign Language Recognition](https://mlsanity.com/paper/2407.09544/) (2024)
- [Gestura: A LVLM-Powered System Bridging Motion and Semantics for Real-Time Free-Form Gesture Understanding](https://mlsanity.com/paper/2510.21814/) (2025)
- [Prompt-to-Gesture: Measuring the Capabilities of Image-to-Video Deictic Gesture Generation](https://mlsanity.com/paper/2604.14953/) (2026)

## Abstract (arXiv)

The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams round the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR.