Learning 3D Appearance Models from Video

Abstract

Within the past few years, there has been a great interest in face modeling for analysis (e.g. facial expression recognition) and synthesis (e.g. virtual avatars). Two primary approaches are appearance models (AM) and structure from motion (SFM). While extensively studied, both approaches have limitations. We introduce a semi-automatic method for 3D facial appearance modeling from video that addresses previous problems. Four main novelties are proposed: A 3D generative facial appearance model integrates both structure and appearance. The model is learned in a semi-unsupervised manner from video sequences, greatly reducing the need for tedious manual pre-processing. A constrained flow-based stochastic sampling technique improves specificity in the learning process. In the appearance learning step, we automatically select the most representative images from the sequence. By doing so, we avoid biasing the linear model, speed up processing and enable more tractable computations. Preliminary experiments of learning 3D facial appearance models from video are reported.