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We introduce a novel robust hybrid 3D face tracking framework from RGBD videostreams, which is capable of tracking head pose and facial actions withoutpre-calibration or intervention from a user. In particular, we emphasize onimproving the tracking performance in instances where the tracked subject is ata large distance from the cameras, and the quality of point cloud deterioratesseverely. This is accomplished by the combination of a flexible 3D shaperegressor and the joint 2D+3D optimization on shape parameters. Our approachfits facial blendshapes to the point cloud of the human head, while beingdriven by an efficient and rapid 3D shape regressor trained on generic RGBdatasets. As an on-line tracking system, the identity of the unknown user isadapted on-the-fly resulting in improved 3D model reconstruction andconsequently better tracking performance. The result is a robust RGBD facetracker, capable of handling a wide range of target scene depths, beyond thosethat can be afforded by traditional depth or RGB face trackers. Lastly, sincethe blendshape is not able to accurately recover the real facial shape, we usethe tracked 3D face model as a prior in a novel filtering process to furtherrefine the depth map for use in other tasks, such as 3D reconstruction.

Robust Performance-driven 3D Face Tracking in Long Range Depth Scenes