We present a generative model that learns to synthesize human motion from limited training sequences. In contrast to existing methods, our framework provides stylistic control across multiple temporal resolutions. The model adeptly captures human motion patterns by integrating skeletal convolution layers and a multi-scale architecture. Our framework contains a set generative and adversarial networks, along with style embedding modules, each tailored for generating motions at specific frame rates while exerting control over their style. Through direct synthesis of SMPL pose parameters, our approach avoids test-time adjustments to fit human body meshes. Experimental results showcase our model's ability to achieve extensive coverage of training examples, while generating diverse motions, as indicated by local and global diversity metrics.