  In this paper we propose a highly scalable convolutional neural network,
end-to-end trainable, for real-time 3D human pose regression from still RGB
images. We call this approach the Scalable Sequential Pyramid Networks
(SSP-Net) as it is trained with refined supervision at multiple scales in a
sequential manner. Our network requires a single training procedure and is
capable of producing its best predictions at 120 frames per second (FPS), or
acceptable predictions at more than 200 FPS when cut at test time. We show that
the proposed regression approach is invariant to the size of feature maps,
allowing our method to perform multi-resolution intermediate supervisions and
reaching results comparable to the state-of-the-art with very low resolution
feature maps. We demonstrate the accuracy and the effectiveness of our method
by providing extensive experiments on two of the most important publicly
available datasets for 3D pose estimation, Human3.6M and MPI-INF-3DHP.
Additionally, we provide relevant insights about our decisions on the network
architecture and show its flexibility to meet the best precision-speed
compromise.
