Height and weight determination using machine learning


We present the first single-network approach for 2D whole-body pose estimation which entails simultaneous localization of body face hands and feet keypoints. Due to the bottom-up formulation our method maintains constant real-time performance regardless of the number of people in the image. The network is trained in a single stage using multi-task learning through an improved architecture which can handle scale differences between body/foot and face/hand keypoints. Our approach considerably improves upon OpenPose [9] the only work so far capable of wholebody pose estimation both in terms of speed and global accuracy. Unlike [9] our method does not need to run an additional network for each hand and face candidate making it substantially faster for multi-person scenarios. This work directly results in a reduction of computational complexity for applications that require 2D whole-body information (e.g. VR/AR re-targeting). In addition it yields higher accuracy especially for occluded blurry and low resolution faces and hands. For code trained models and validation benchmarks visit our project page



Openpose-random forest


machine learning


₹12000 (INR)


IEEE 2019