3,000 research outputs found
CASA: Category-agnostic Skeletal Animal Reconstruction
Recovering the skeletal shape of an animal from a monocular video is a
longstanding challenge. Prevailing animal reconstruction methods often adopt a
control-point driven animation model and optimize bone transforms individually
without considering skeletal topology, yielding unsatisfactory shape and
articulation. In contrast, humans can easily infer the articulation structure
of an unknown animal by associating it with a seen articulated character in
their memory. Inspired by this fact, we present CASA, a novel Category-Agnostic
Skeletal Animal reconstruction method consisting of two major components: a
video-to-shape retrieval process and a neural inverse graphics framework.
During inference, CASA first retrieves an articulated shape from a 3D character
assets bank so that the input video scores highly with the rendered image,
according to a pretrained language-vision model. CASA then integrates the
retrieved character into an inverse graphics framework and jointly infers the
shape deformation, skeleton structure, and skinning weights through
optimization. Experiments validate the efficacy of CASA regarding shape
reconstruction and articulation. We further demonstrate that the resulting
skeletal-animated characters can be used for re-animation.Comment: Accepted to NeurIPS 202
Novel Correspondence-based Approach for Consistent Human Skeleton Extraction
This paper presents a novel base-points-driven shape correspondence (BSC) approach to extract skeletons of articulated objects from 3D mesh shapes. The skeleton extraction based on BSC approach is more accurate than the traditional direct skeleton extraction methods. Since 3D shapes provide more geometric information, BSC offers the consistent information between the source shape and the target shapes. In this paper, we first extract the skeleton from a template shape such as the source shape automatically. Then, the skeletons of the target shapes of different poses are generated based on the correspondence relationship with source shape. The accuracy of the proposed method is demonstrated by presenting a comprehensive performance evaluation on multiple benchmark datasets. The results of the proposed approach can be applied to various applications such as skeleton-driven animation, shape segmentation and human motion analysis
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