{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/step-spatial-temporal-graph-convolutional","title":"STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits","arxiv_id":"1910.12906","date":"2019-10-28","proceeding":null,"authors":["Uttaran Bhattacharya","Trisha Mittal","Rohan Chandra","Tanmay Randhavane","Aniket Bera","Dinesh Manocha"],"abstract":"We present a novel classifier network called STEP, to classify perceived human emotion from gaits, based on a Spatial Temporal Graph Convolutional Network (ST-GCN) architecture. Given an RGB video of an individual walking, our formulation implicitly exploits the gait features to classify the emotional state of the human into one of four emotions: happy, sad, angry, or neutral. We use hundreds of annotated real-world gait videos and augment them with thousands of annotated synthetic gaits generated using a novel generative network called STEP-Gen, built on an ST-GCN based Conditional Variational Autoencoder (CVAE). We incorporate a novel push-pull regularization loss in the CVAE formulation of STEP-Gen to generate realistic gaits and improve the classification accuracy of STEP. We also release a novel dataset (E-Gait), which consists of $2,177$ human gaits annotated with perceived emotions along with thousands of synthetic gaits. In practice, STEP can learn the affective features and exhibits classification accuracy of 89% on E-Gait, which is 14 - 30% more accurate over prior methods.","url_abs":"https://arxiv.org/abs/1910.12906v3","url_pdf":"https://arxiv.org/pdf/1910.12906v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"step-spatial-temporal-graph-convolutional","repo_url":"https://github.com/UttaranB127/STEP","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"gcn","method_name":"GCN"},{"method_slug":"ger","method_name":"GER"},{"method_slug":"cvae","method_name":"cVAE"}],"datasets_introduced":[],"methods_introduced":[{"slug":"ger","name":"GER","full_name":"Gait Emotion Recognition"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1910.12906","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}