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This work focuses on complete 3D facial geometry prediction, including 3Dfacial alignment via 3D face modeling and face orientation estimation using theproposed multi-task, multi-modal, and multi-representation landmark refinementnetwork (M$^3$-LRN). Our focus is on the important facial attributes, 3Dlandmarks, and we fully utilize their embedded information to guide 3D facialgeometry learning. We first propose a multi-modal and multi-representationfeature aggregation for landmark refinement. Next, we are the first to study3DMM regression from sparse 3D landmarks and utilize multi-representationadvantage to attain better geometry prediction. We attain the state of the artfrom extensive experiments on all tasks of learning 3D facial geometry. Weclosely validate contributions of each modality and representation. Our resultsare robust across cropped faces, underwater scenarios, and extreme poses.Specially we adopt only simple and widely used network operations in M$^3$-LRNand attain a near 20\% improvement on face orientation estimation over thecurrent best performance. See our project page here.

Accurate 3D Facial Geometry Prediction by Multi-Task, Multi-Modal, and Multi-Representation Landmark Refinement Network