WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion
Soyong Shin, Juyong Kim, Eni Halilaj, Michael J. Black

TL;DR
WHAM is a novel method that accurately reconstructs 3D human motion in a global coordinate system from video, overcoming limitations of previous approaches by integrating motion capture data, visual features, and SLAM-based camera motion estimation.
Contribution
It introduces a new approach that combines 2D keypoint lifting, motion capture data, and SLAM to improve 3D human motion reconstruction in global coordinates from video.
Findings
Outperforms existing methods on multiple benchmarks.
Effectively captures diverse human motions like climbing stairs.
Provides accurate global trajectory estimation.
Abstract
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…
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Taxonomy
TopicsHuman Pose and Action Recognition · Advanced Vision and Imaging · Video Surveillance and Tracking Methods
