Temporal Action Co-Segmentation
in 3D Motion Capture Data & Videos

 Given two action sequences, we are interested in spotting/co-segmenting all pairs of sub-sequences that represent the same action. We propose a totally unsupervised solution to this problem. No a-priori model of the actions is assumed to be available. The number of common subsequences may be unknown. The sub-sequences can be located anywhere in the original sequences, may differ in duration and the corresponding actions may be performed by a different person, in different style. We treat this type of temporal action co-segmentation as a stochastic optimization problem that is solved by employing Particle Swarm Optimization (PSO). The objective function that is minimized by PSO capitalizes on Dynamic TimeWarping (DTW) to compare two action sequences. Due to the generic problem formulation and solution, the proposed method can be applied to motion capture (i.e., 3D skeletal) data or to conventional RGB video data acquired in the wild. We present extensive quantitative experiments on several standard, ground truthed datasets. The obtained results demonstrate that the proposed method achieves a remarkable increase in co-segmentation quality compared to all tested existing state of the art methods.

Given two image sequences that share common actions, our goal is to automatically co-segment them in a totally unsupervised manner. In this example, there are four common actions and two non-common actions. Notice that there are two instances of the 1st action (green segments) of sequence A in sequence B. Each point of the grayscale background encodes the pairwise distance of the corresponding sequence frames.