DIH: Depth Images with Humans

Description

The DIH dataset has been created for human body landmark detection and human pose estimation from depth images. Training and deploying good models for these tasks require large amounts of data with high quality annotations. Unfortunately, obtaining precise manual annotation of depth images with body parts is hampered by the fact that people appear roughly as blobs and the annotation task is very time consuming. Synthesizing images provides an easy way to introduce variability in body pose, view perspective and high quality annotations can be easily generated.

However, synthetic depth images does not match real depth data in several aspects: visual characteristics that arise from the depth image generation, i.e. measurement noise, the most problematically depth discontinuity and missing measurements. Hence, real data with annotations is required to fill the detection performance gap this data mismatch can provoke in real data. With this is mind, the DIH dataset also provides real data with annotations for Kinect 2. This real data can be used for both finetuning and testing.

References

Angel Martínez-González, Michael Villamizar, Olivier Canévet and Jean-Marc Odobez, "Real-time Convolutional Networks for Depth-based Human Pose Estimation", in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018

Angel Martínez-González, Michael Villamizar, Olivier Canévet and Jean-Marc Odobez, "Investigating Depth Domain Adaptation for Efficient Human Pose Estimation", in European Conference of Computer Vision - Workshops (ECCV), 2018