Section: New Results

Robust human body shape and pose tracking

This work considers markerless human performance capture from multiple camera videos and, in particular, the recovery of both shape and parametric motion information, as often required in applications that produce and manipulate animated 3D contents using multiple videos. To this aim, an approach is proposed that jointly estimates skeleton joint positions and surface deformations by fitting a reference surface model to 3D point reconstructions. The approach is based on a probabilistic deformable surface registration framework coupled with a bone binding energy. The former makes soft assignments between the model and the observations while the latter guides the skeleton fitting. The main benefit of this strategy lies in its ability to handle outliers and erroneous observations frequently present in multi view data. For the same purpose, we also introduce a learning based method that partitions the point cloud observations into different rigid body parts that further discriminate input data into classes in addition to reducing the complexity of the association between the model and the observations. We argue that such combination of a learning based matching and of a probabilistic fitting framework efficiently handle unreliable observations with fake geometries or missing data and hence, it reduces the need for tedious manual interventions. The work was presented at the 3DV conference [7] where it received the best paper runner up award.

Figure 4. Human pose recovery with 3 different standard datasets.