论文标题
长期的人类运动预测与场景环境
Long-term Human Motion Prediction with Scene Context
论文作者
论文摘要
人类运动是目标定向和受到现场物体空间布局的影响。要计划未来的人类运动，感知环境至关重要 - 想象一下灯熄灭的新房间是多么困难。现有的预测人类运动的作品不关注场景环境，因此在长期预测中挣扎。在这项工作中，我们提出了一个新颖的三阶段框架，该框架利用场景上下文来应对这项任务。鉴于单个场景图像和2D姿势历史，我们的方法首先采样了多个人类运动目标，然后将3D人类的路径计划针对每个目标，最后预测按照每个路径的3D人类姿势序列。对于稳定的培训和严格的评估，我们用干净的注释贡献了多样化的合成数据集。在合成数据集和实际数据集中，我们的方法对现有方法显示出一致的定量和定性改进。
Human movement is goal-directed and influenced by the spatial layout of the objects in the scene. To plan future human motion, it is crucial to perceive the environment -- imagine how hard it is to navigate a new room with lights off. Existing works on predicting human motion do not pay attention to the scene context and thus struggle in long-term prediction. In this work, we propose a novel three-stage framework that exploits scene context to tackle this task. Given a single scene image and 2D pose histories, our method first samples multiple human motion goals, then plans 3D human paths towards each goal, and finally predicts 3D human pose sequences following each path. For stable training and rigorous evaluation, we contribute a diverse synthetic dataset with clean annotations. In both synthetic and real datasets, our method shows consistent quantitative and qualitative improvements over existing methods.
