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Accurate and temporally consistent modeling of human bodies is essential fora wide range of applications, including character animation, understandinghuman social behavior and AR/VR interfaces. Capturing human motion accuratelyfrom a monocular image sequence is still challenging and the modeling qualityis strongly influenced by the temporal consistency of the captured body motion.Our work presents an elegant solution for the integration of temporalconstraints in the fitting process. This does not only increase temporalconsistency but also robustness during the optimization. In detail, we deriveparameters of a sequence of body models, representing shape and motion of aperson, including jaw poses, facial expressions, and finger poses. We optimizethese parameters over the complete image sequence, fitting one consistent bodyshape while imposing temporal consistency on the body motion, assuming linearbody joint trajectories over a short time. Our approach enables the derivationof realistic 3D body models from image sequences, including facial expressionand articulated hands. In extensive experiments, we show that our approachresults in accurately estimated body shape and motion, also for challengingmovements and poses. Further, we apply it to the special application of signlanguage analysis, where accurate and temporal consistent motion modelling isessential, and show that the approach is well-suited for this kind ofapplication.

Imposing Temporal Consistency on Deep Monocular Body Shape and Pose Estimation