Deep Fully-Connected Part-Based Models for Human Pose Estimation

Rodrigo de Bem, Anurag Arnab, Stuart Golodetz, Michael Sapienza and Philip H. S. Torr


We propose a 2D multi-level appearance representation of the human body in RGB images, spatially modelled using a fully-connected graphical model. The appearance model is based on a CNN body part detector, which uses shared features in a cascade architecture to simultaneously detect body parts with different levels of granularity. We use a fully-connected Conditional Random Field (CRF) as our spatial model, over which approximate inference is efficiently performed using the Mean-Field algorithm, implemented as a Recurrent Neural Network (RNN). The stronger visual support from body parts with different levels of granularity, along with the fully-connected pairwise spatial relations, which have their weights learnt by the model, improve the performance of the bottom-up part detector. The use of the spatial model assists in locating the body parts, especially in cases where the visual cues for the joints are weak, or even absent. We adopt an end-to-end training strategy to leverage the potential of both our appearance and spatial models, and achieve competitive results on the MPII and LSP datasets.

Paper Links

University of Oxford