  We introduce the first approach to solve the challenging problem of
unsupervised 4D visual scene understanding for complex dynamic scenes with
multiple interacting people from multi-view video. Our approach simultaneously
estimates a detailed model that includes a per-pixel semantically and
temporally coherent reconstruction, together with instance-level segmentation
exploiting photo-consistency, semantic and motion information. We further
leverage recent advances in 3D pose estimation to constrain the joint semantic
instance segmentation and 4D temporally coherent reconstruction. This enables
per person semantic instance segmentation of multiple interacting people in
complex dynamic scenes. Extensive evaluation of the joint visual scene
understanding framework against state-of-the-art methods on challenging indoor
and outdoor sequences demonstrates a significant (approx 40%) improvement in
semantic segmentation, reconstruction and scene flow accuracy.
