Stacked Hourglass Networks for Human Pose Estimation
This addresses the problem of accurately estimating human poses in images, which is crucial for applications like human-computer interaction and motion analysis, representing a novel architectural advancement rather than an incremental improvement.
The paper tackles human pose estimation by introducing a stacked hourglass network architecture that processes features across scales with repeated bottom-up, top-down processing and intermediate supervision, achieving state-of-the-art results on FLIC and MPII benchmarks.
This work introduces a novel convolutional network architecture for the task of human pose estimation. Features are processed across all scales and consolidated to best capture the various spatial relationships associated with the body. We show how repeated bottom-up, top-down processing used in conjunction with intermediate supervision is critical to improving the performance of the network. We refer to the architecture as a "stacked hourglass" network based on the successive steps of pooling and upsampling that are done to produce a final set of predictions. State-of-the-art results are achieved on the FLIC and MPII benchmarks outcompeting all recent methods.