Estimating human and camera trajectories with accurate scale in the world
coordinate system from a monocular video is a highly desirable yet challenging
and ill-posed problem. In this study, we aim to recover expressive parametric
human models (i.e., SMPL-X) and corresponding camera poses jointly, by
leveraging the synergy between three critical players: the world, the human,
and the camera. Our approach is founded on two key observations. Firstly,
camera-frame SMPL-X estimation methods readily recover absolute human depth.
Secondly, human motions inherently provide absolute spatial cues. By
integrating these insights, we introduce a novel framework, referred to as
WHAC, to facilitate world-grounded expressive human pose and shape estimation
(EHPS) alongside camera pose estimation, without relying on traditional
optimization techniques. Additionally, we present a new synthetic dataset,
WHAC-A-Mole, which includes accurately annotated humans and cameras, and
features diverse interactive human motions as well as realistic camera
trajectories. Extensive experiments on both standard and newly established
benchmarks highlight the superiority and efficacy of our framework. We will
make the code and dataset publicly available.

利用摄像头视频准确地估计人体和摄像头在世界坐标系中的轨迹是一个既希望实现又具有挑战性和没有确切解的问题。该研究旨在通过充分利用世界、人体和摄像头之间的协同作用，共同恢复富有表现力的参数化人体模型（即 SMPL-X）和相应的摄像头姿势，基于两个关键观察结果，即相机框架的 SMPL-X 估计方法可以方便地恢复人体的绝对深度，以及人体动作本质上提供绝对空间线索。通过整合这些洞察，我们引入了一种新的框架，称为 WHAC，以促进基于世界的富有表现力的人体姿势和形状估计，并且无需依赖传统的优化技术进行相机姿势估计。此外，我们还提供了一个新的合成数据集，WHAC-A-Mole，其中包括准确标注的人体和摄像头，并具有多样化的人体交互动作和逼真的摄像头轨迹。对标准和新建立的基准的广泛实验证明了我们框架的优越性和效果。我们将公开提供代码和数据集。