  Self-supervised detection and segmentation of foreground objects aims for
accuracy without annotated training data. However, existing approaches
predominantly rely on restrictive assumptions on appearance and motion. For
scenes with dynamic activities and camera motion, we propose a multi-camera
framework in which geometric constraints are embedded in the form of multi-view
consistency during training via coarse 3D localization in a voxel grid and
fine-grained offset regression. In this manner, we learn a joint distribution
of proposals over multiple views. At inference time, our method operates on
single RGB images. We outperform state-of-the-art techniques both on images
that visually depart from those of standard benchmarks and on those of the
classical Human3.6M dataset.
