Unsupervised 3D Pose Estimation with Geometric Self-Supervision

Learning deformable 3D objects from 2D images is an extremely ill-posed problem. Existing methods rely on explicit supervision to establish multi-view correspondences, such as template shape models and keypoint annotations, which restricts their applicability on objects "in the wild". In this paper, we propose to use monocular videos, which naturally provide correspondences across time, allowing us to learn 3D shapes of deformable object categories without explicit keypoints or template shapes. Specifically, we present DOVE, which learns to predict 3D canonical shape, deformation, viewpoint and texture from a single 2D image of a bird, given a bird video collection as well as automatically obtained silhouettes and optical flows as training data. Our method reconstructs temporally consistent 3D shape and deformation, which allows us to animate and re-render the bird from arbitrary viewpoints from a single image. IntroductionRecently, with the adoption of machine learning, reconstructing 3D shapes from 2D images has advanced considerably. While this task traditionally requires establishing correspondences between multiple views [13], learning-based approaches have demonstrated the possibility of inferring 3D shapes from a single image, by learning priors for a specific object category [

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Unsupervised 3D Pose Estimation with Geometric Self-Supervision

Cited by 2 publications

References 50 publications

Self-Attentive 3D Human Pose and Shape Estimation from Videos

Self-Attentive 3D Human Pose and Shape Estimation from Videos

DOVE: Learning Deformable 3D Objects by Watching Videos

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