DMMGAN: Diverse Multi Motion Prediction of 3D Human Joints using Attention-Based Generative Adverserial Network

• URL: http://arxiv.org/abs/2209.09124v1
• Date: Tue, 13 Sep 2022 23:22:33 GMT
• Title: DMMGAN: Diverse Multi Motion Prediction of 3D Human Joints using Attention-Based Generative Adverserial Network
• Authors: Payam Nikdel, Mohammad Mahdavian, Mo Chen
• Abstract summary: We propose a transformer-based generative model for forecasting multiple diverse human motions. Our model first predicts the pose of the body relative to the hip joint. Then the textitHip Prediction Module predicts the trajectory of the hip movement for each predicted pose frame. We show that our system outperforms the state-of-the-art in human motion prediction while it can predict diverse multi-motion future trajectories with hip movements.
• Score: 9.247294820004143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human motion prediction is a fundamental part of many human-robot applications. Despite the recent progress in human motion prediction, most studies simplify the problem by predicting the human motion relative to a fixed joint and/or only limit their model to predict one possible future motion. While due to the complex nature of human motion, a single output cannot reflect all the possible actions one can do. Also, for any robotics application, we need the full human motion including the user trajectory not a 3d pose relative to the hip joint. In this paper, we try to address these two issues by proposing a transformer-based generative model for forecasting multiple diverse human motions. Our model generates \textit{N} future possible motion by querying a history of human motion. Our model first predicts the pose of the body relative to the hip joint. Then the \textit{Hip Prediction Module} predicts the trajectory of the hip movement for each predicted pose frame. To emphasize on the diverse future motions we introduce a similarity loss that penalizes the pairwise sample distance. We show that our system outperforms the state-of-the-art in human motion prediction while it can predict diverse multi-motion future trajectories with hip movements

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