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

SSP-Net: Scalable Sequential Pyramid Networks for Real-Time 3D Human Pose Regression