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
翻译:人类运动预测是许多人类机器人应用中的一个基本部分。 尽管人类运动预测最近取得了进展, 但大多数研究都通过预测人类运动相对于固定联合和(或)仅限制其模型来预测未来可能的运动来简化问题。 由于人类运动的复杂性, 单项输出无法反映所有可能的行动。 另外, 对于任何机器人应用, 我们需要完整的人类运动, 包括用户轨迹, 而不是与臀部关节相对的三维构成。 在本文中, 我们试图通过提出基于变压器的基因化模型来预测多种人类运动, 来解决这两个问题。 我们的模型产生\ textit{N} 通过查询人类运动的历史来生成未来可能的动作。 我们的模型首先预测人体与臀部关节的关系。 然后, 我们的模型可以预测每个预测的姿势框架的臀部运动轨迹。 为了强调不同的未来运动, 我们引入的相似性损失, 从而惩罚对称样本距离。 我们的系统在人类运动史上超越了时程的状态, 而它可以预测多种动作。