Full-body ego-pose estimation from head and hand poses alone has become an
active area of research to power articulate avatar representation on
headset-based platforms. However, existing methods over-rely on the confines of
the motion-capture spaces in which datasets were recorded, while simultaneously
assuming continuous capture of joint motions and uniform body dimensions. In
this paper, we propose EgoPoser, which overcomes these limitations by 1)
rethinking the input representation for headset-based ego-pose estimation and
introducing a novel motion decomposition method that predicts full-body pose
independent of global positions, 2) robustly modeling body pose from
intermittent hand position and orientation tracking only when inside a
headset's field of view, and 3) generalizing across various body sizes for
different users. Our experiments show that EgoPoser outperforms
state-of-the-art methods both qualitatively and quantitatively, while
maintaining a high inference speed of over 600 fps. EgoPoser establishes a
robust baseline for future work, where full-body pose estimation needs no
longer rely on outside-in capture and can scale to large-scene environments.

本文提出了一种名为 EgoPoser 的方法，通过重新思考基于头戴式设备的自我姿态估计的输入表示以及引入一种新的运动分解方法，在不依赖全局位置的情况下预测全身姿态，仅通过头戴式设备的视野内的间歇性手部位置和方向跟踪鲁棒地建模身体姿势，并且针对不同用户的各种身体尺寸进行了泛化。实验证明，EgoPoser 在质量和数量上都胜过了现有的方法，并且推断速度高达每秒超过 600 帧。EgoPoser 为未来的工作奠定了坚实的基础，不再需要依赖外部捕捉并能在大场景环境中扩展全身姿势估计。

EgoPoser：大场景下鲁棒实时自我身体姿势估计

EgoPoser: Robust Real-Time Ego-Body Pose Estimation in Large Scenes

Ego-pose estimation and dynamic object tracking are two key issues in an
autonomous driving system. Two assumptions are often made for them, i.e. the
static world assumption of simultaneous localization and mapping (SLAM) and the
exact ego-pose assumption of object tracking, respectively. However, these
assumptions are difficult to hold in highly dynamic road scenarios where SLAM
and object tracking become correlated and mutually beneficial. In this paper,
DL-SLOT, a dynamic Lidar SLAM and object tracking method is proposed. This
method integrates the state estimations of both the ego vehicle and the static
and dynamic objects in the environment into a unified optimization framework,
to realize SLAM and object tracking (SLOT) simultaneously. Firstly, we
implement object detection to remove all the points that belong to potential
dynamic objects. Then, LiDAR odometry is conducted using the filtered point
cloud. At the same time, detected objects are associated with the history
object trajectories based on the time-series information in a sliding window.
The states of the static and dynamic objects and ego vehicle in the sliding
window are integrated into a unified local optimization framework. We perform
SLAM and object tracking simultaneously in this framework, which significantly
improves the robustness and accuracy of SLAM in highly dynamic road scenarios
and the accuracy of objects' states estimation. Experiments on public datasets
have shown that our method achieves better accuracy than A-LOAM.

本文提出 DL-SLOT，一种动态激光雷达 (Lidar) SLAM 和动态目标跟踪方法，该方法将车辆和静态 / 动态物体的状态估计融合到统一的优化框架中，同时进行 SLAM 和目标跟踪，以改善高度动态路况下 SLAM 的鲁棒性和准确性以及物体状态估计的准确性。