Filling the gaps: Predicting missing joints of human poses using denoising autoencoders

State of the art pose estimators are able to deal with different challenges present in real-world scenarios, such as varying body appearance, lighting conditions and rare body poses. However, when body parts are severely occluded by objects or other people, the resulting poses might be incomplete, negatively affecting applications where estimating a full body pose is important (e.g. gesture and pose-based behavior analysis). In this work, we propose a method for predicting the missing joints from incomplete human poses. In our model we consider missing joints as noise in the input and we use an autoencoder-based solution to enhance the pose prediction. The method can be easily combined with existing pipelines and, by using only 2D coordinates as input data, the resulting model is small and fast to train, yet powerful enough to learn a robust representation of the low dimensional domain. Finally, results show improved predictions over existing pose estimation algorithms.