Pose

Engineering

Cartography

Systems engineering

Archaeology

Waterfall

Computer science

Architecture

Image segmentation

Estimation

Pooling

Segmentation

Pattern recognition (psychology)

Mathematics

Geography

3D pose estimation

Artificial intelligence

Geometry

Pyramid (geometry)

Computer vision

We propose UniPose, a unified framework for human pose estimation, based on our “Waterfall” Atrous Spatial Pooling architecture, that achieves state-of-art-results on several pose estimation metrics. UniPose incorporates contextual segmentation and joint localization to estimate the human pose in a single stage, with high accuracy, without relying on statistical postprocessing methods. The Waterfall module in UniPose leverages the efficiency of progressive filtering in the cascade architecture, while maintaining multi-scale fields-of-view comparable to spatial pyramid configurations. Additionally, our method is extended to UniPose-LSTM for multi-frame processing and achieves state-of-the-art results for temporal pose estimation in Video. Our results on multiple datasets demonstrate that UniPose, with a ResNet backbone and Waterfall module, is a robust and efficient architecture for pose estimation obtaining state-of-the-art results in single person pose detection for both single images and videos.

UniPose: Unified Human Pose Estimation in Single Images and Videos