PoseDaten

PoseDaten is a term used to describe datasets and data streams that convey the pose of humans or articulated objects over time. In practice, pose data consists of the spatial coordinates of joints and the orientation of body segments, typically organized per frame. Data can be 2D, capturing joint locations in the image plane, or 3D, providing world or camera coordinates along with limb orientations and confidence scores for each joint.

Pose data is acquired through various methods. Marker-based motion capture uses reflective markers tracked by cameras to produce precise 3D coordinates. Markerless approaches estimate pose from video using computer vision and deep learning. Additional sources include inertial measurement units and depth sensors. Common file formats for pose data include BVH (for hierarchical joint data), C3D, as well as JSON, CSV or custom formats that pair coordinates with joint names and frame indices.

Applications of pose data span animation, film and game production, biomechanics and sports analytics, ergonomics, rehabilitation, augmented and virtual reality, and robotics. In animation and gaming, pose data supports motion capture and retargeting to digital characters. In science and medicine, it enables gait analysis and movement assessment.

Challenges include data quality issues such as noise and occlusions, missing data, and sensor drift. Interoperability remains a problem due to varying skeleton definitions and coordinate systems. Privacy and consent concerns may arise when recording individuals. Standardization efforts and benchmarks aim to improve comparability across datasets and tools.

Prominent tools and datasets include marker-based systems, and open-source pose estimation frameworks that output 2D or 3D pose. Researchers often publish PoseDaten in conjunction with metadata describing frame rate, camera setup, and skeleton topology.