Generating and executing whole-body motions for humanoid robots remains a challenging research question. In this paper, we present an approach that combines human motion data and QP-based control to generate humanoid motion. Following the contacts-before-motion paradigm, we first generate a sequence of stances based on our previous work on data-driven generation of whole-body multi-contact pose sequences from human motion data and their mapping to the target robot kinematics. In this paper, we address the next step of closed-loop execution of stance sequences based on QP controllers. We evaluated the approach in simulation on the humanoid robot ARMAR-4 and HRP4. The results show that our approach can successfully execute stance sequences generated by our previous work and thus the viability of learning locomotion patterns from human demonstrations.