LASSIE: LearningArticulatedShapesfromSparse ImageEnsemblevia3DPartDiscovery

Therefore,techniquestoreconstruct articulated 3D objects from 2D images are crucial and highly useful. In this work, we propose a practical problem setting to estimate 3D pose and shape of animals given only a few (10-30) in-the-wild images of a particular animal species (say,horse). Contrary toexisting worksthatrelyonpre-defined template shapes, we do not assume any form of 2D or 3D ground-truth annotations, nor do we leverage any multi-view or temporal information. Moreover, each input image ensemble can contain animal instances with varying poses, backgrounds, illuminations, and textures. Our key insight is that 3D parts have much simpler shape compared totheoverall animal and that theyarerobustw.r.t.