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Automatically estimating 3D skeleton, shape, camera viewpoints, and partarticulation from sparse in-the-wild image ensembles is a severelyunder-constrained and challenging problem. Most prior methods rely onlarge-scale image datasets, dense temporal correspondence, or human annotationslike camera pose, 2D keypoints, and shape templates. We propose Hi-LASSIE,which performs 3D articulated reconstruction from only 20-30 online images inthe wild without any user-defined shape or skeleton templates. We follow therecent work of LASSIE that tackles a similar problem setting and make twosignificant advances. First, instead of relying on a manually annotated 3Dskeleton, we automatically estimate a class-specific skeleton from the selectedreference image. Second, we improve the shape reconstructions with novelinstance-specific optimization strategies that allow reconstructions tofaithful fit on each instance while preserving the class-specific priorslearned across all images. Experiments on in-the-wild image ensembles show thatHi-LASSIE obtains higher fidelity state-of-the-art 3D reconstructions despiterequiring minimum user input.

Hi-LASSIE: High-Fidelity Articulated Shape and Skeleton Discovery from Sparse Image Ensemble