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We present a system to recover the 3D shape and motion of a wide variety ofquadrupeds from video. The system comprises a machine learning front-end whichpredicts candidate 2D joint positions, a discrete optimization which findskinematically plausible joint correspondences, and an energy minimization stagewhich fits a detailed 3D model to the image. In order to overcome the limitedavailability of motion capture training data from animals, and the difficultyof generating realistic synthetic training images, the system is designed towork on silhouette data. The joint candidate predictor is trained onsynthetically generated silhouette images, and at test time, deep learningmethods or standard video segmentation tools are used to extract silhouettesfrom real data. The system is tested on animal videos from several species, andshows accurate reconstructions of 3D shape and pose.

Creatures great and SMAL: Recovering the shape and motion of animals from video