We present an approach to reconstruct humans and track them over time. At the core of our approach, we propose a fully "transformerized" version of a network for human mesh recovery. This network, HMR 2.0, advances the state of the art and shows the capability to analyze unusual poses that have in the past been difficult to reconstruct from single images. To analyze video, we use 3D reconstructions from HMR 2.0 as input to a tracking system that operates in 3D. This enables us to deal with multiple people and maintain identities through occlusion events. Our complete approach, 4DHumans, achieves state-of-the-art results for tracking people from monocular video. Furthermore, we demonstrate the effectiveness of HMR 2.0 on the downstream task of action recognition, achieving significant improvements over previous pose-based action recognition approaches. Our code and models are available on the project website: https://shubham-goel.github.io/4dhumans/.

翻译：我们提出了一种对人体进行重建并随时间追踪的方法。该方法的核心是一个完全"Transformer化"的人体网格恢复网络。该网络HMR 2.0推动了该领域的技术发展，展示了分析以往难以从单张图像重建的异常姿态的能力。在视频分析中，我们采用HMR 2.0的三维重建结果作为输入，输入至运行于三维空间中的追踪系统，从而能够处理多人场景并在遮挡事件中保持身份识别。我们提出的完整方法——4DHumans——在单目视频人物追踪任务上取得了领先结果。此外，我们验证了HMR 2.0在下游动作识别任务中的有效性，相较以往基于姿态的动作识别方法实现了显著提升。我们的代码和模型已发布于项目网站：https://shubham-goel.github.io/4dhumans/。