Hierarchical facial expression animation by motion capture data

Mapping facial tracking data to avatars is very challenging and time consuming, where a simple, yet efficient approach is strongly required. State-of-the-art methods are either vulnerable to noise or heavily reliant on complicated sensor devices. To deal with the noisy data, and without using a motion capture device, we present a novel vision-based facial expression animation framework by applying facial hierarchical model on pre-processed Motion Capture (MoCap) data. Our approach uses a facial tracking algorithm to extract rigid head pose and a set of expression motion parameters from each video frame. We factorize the MoCap data as prior knowledge to filter the low-quality 2D signals. In addition, a facial hierarchical model is established by the Hierarchical Gaussian Process Latent Variable Model (HGPLVM) to synthesize the holistic facial expression. Experimental results demonstrate the effectiveness of our system.