Abstract
We propose TRAM, a two-stage method to reconstruct a human's global trajectory and motion from in-the-wild videos. TRAM robustifies SLAM to recover the camera motion in the presence of dynamic humans and uses the scene background to derive the motion scale. Using the recovered camera as a metric-scale reference frame, we introduce a video transformer model (VIMO) to regress the kinematic body motion of a human. By composing the two motions, we achieve accurate recovery of 3D humans in the world space, reducing global motion errors by 60% from prior work. this https URL
Abstract (translated)
我们提出了TRAM，一种从野视频中的全局轨迹和运动重建人类的方法。TRAM对SLAM进行了鲁棒，以在存在动态人类的情况下恢复相机运动，并使用场景背景来计算运动规模。将恢复的相机作为指标尺度参考框架，我们引入了一个视频变换模型（VIMO）来预测人类的三维姿态。通过将两个运动组合起来，我们在世界空间中实现了对3D人类的准确重构，将全局运动误差减少60%以上。这个链接：https://
URL
https://arxiv.org/abs/2403.17346
