单目摄像头下实时追踪多人3D姿态，抗遮挡能力强。
CoMotion: Concurrent Multi-person 3D Motion
- 直接从新帧更新姿态，无需跨帧匹配
- 在复杂遮挡场景中保持时序连贯性
- 速度快且跟踪精度优于现有方法
我们提出一种从单目视频流中检测并追踪多人精细3D姿态的方法。系统在密集人群、复杂姿态和遮挡场景中仍能保持时序一致的预测结果。模型结合强健的帧内检测与学习到的姿态更新机制，实现帧间连续追踪。不同于传统通过跨帧匹配检测结果的方式，本方法直接从新输入图像中更新姿态，从而支持遮挡下的在线追踪。我们在多个图像与视频数据集上训练，利用伪标注标签生成模型，在3D姿态估计精度上达到当前最优水平，同时在多人追踪的时效性与准确性上表现更优。代码与权重已公开于 https://github.com/apple/ml-comotion。
原文摘要 · Abstract (English)
We introduce an approach for detecting and tracking detailed 3D poses of multiple people from a single monocular camera stream. Our system maintains temporally coherent predictions in crowded scenes filled with difficult poses and occlusions. Our model performs both strong per-frame detection and a learned pose update to track people from frame to frame. Rather than match detections across time, poses are updated directly from a new input image, which enables online tracking through occlusion. We train on numerous image and video datasets leveraging pseudo-labeled annotations to produce a model that matches state-of-the-art systems in 3D pose estimation accuracy while being faster and more accurate in tracking multiple people through time. Code and weights are provided at https://github.com/apple/ml-comotion
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