论文标题
DMMGAN：使用基于注意力的生成对手网络对3D人类关节的多种多运动预测
DMMGAN: Diverse Multi Motion Prediction of 3D Human Joints using Attention-Based Generative Adverserial Network
论文作者
论文摘要
人类运动预测是许多人类机器人应用的基本组成部分。尽管人类运动预测的最新进展，但大多数研究通过预测相对于固定关节的人类运动和/或仅限于其模型来预测可能的未来运动来简化问题。尽管由于人类运动的复杂性质，但单个输出无法反映人们可以做的所有可能的动作。同样，对于任何机器人应用，我们都需要全部人类运动，包括用户轨迹而不是相对于髋关节的3D姿势。 在本文中，我们试图通过提出基于变压器的生成模型来预测多种人类动作来解决这两个问题。我们的模型通过查询人类运动的历史来生成\ textit {n}未来可能的运动。我们的模型首先预测身体相对于髋关节的姿势。然后，\ textit {hip预测模块}预测每个预测姿势框架的髋部运动的轨迹。为了强调未来的各种动议，我们引入了相似性损失，从而惩罚了成对样本距离。我们表明，我们的系统在人类运动预测中的最先进，而它可以通过髋关节运动来预测各种多动运动的未来轨迹
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
