  The estimation of 3D human motion from video has progressed rapidly but
current methods still have several key limitations. First, most methods
estimate the human in camera coordinates. Second, prior work on estimating
humans in global coordinates often assumes a flat ground plane and produces
foot sliding. Third, the most accurate methods rely on computationally
expensive optimization pipelines, limiting their use to offline applications.
Finally, existing video-based methods are surprisingly less accurate than
single-frame methods. We address these limitations with WHAM (World-grounded
Humans with Accurate Motion), which accurately and efficiently reconstructs 3D
human motion in a global coordinate system from video. WHAM learns to lift 2D
keypoint sequences to 3D using motion capture data and fuses this with video
features, integrating motion context and visual information. WHAM exploits
camera angular velocity estimated from a SLAM method together with human motion
to estimate the body's global trajectory. We combine this with a contact-aware
trajectory refinement method that lets WHAM capture human motion in diverse
conditions, such as climbing stairs. WHAM outperforms all existing 3D human
motion recovery methods across multiple in-the-wild benchmarks. Code will be
available for research purposes at http://wham.is.tue.mpg.de/
