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. Current pose estimation methods utilizing standard CNN architectures heavily rely on statistical postprocessing or predefined anchor poses for joint localization. UniPose incorporates contextual seg- mentation 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 filter- ing in the cascade architecture, while maintaining multi- scale fields-of-view comparable to spatial pyramid config- urations. 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 re- sults 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 sin- gle images and videos.
Hi I understand it's a new work - and you published your pretrained model which is great.
But as an end user who wants to check out your model quality for his purposes there is no simple code snippet to run inference, and no demo. It looks you have already written the test function and also have all the components for this.
Yet no readme nor some basic indication how to infer the model on an image.
Hi I understand it's a new work - and you published your pretrained model which is great.
But as an end user who wants to check out your model quality for his purposes there is no simple code snippet to run inference, and no demo. It looks you have already written the test function and also have all the components for this.
Yet no readme nor some basic indication how to infer the model on an image.