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- W4309376484 abstract "Inverse Kinematics (IK) systems are often rigid with respect to their input character, thus requiring user intervention to be adapted to new skeletons. In this paper we aim at creating a flexible, learned IK solver applicable to a wide variety of human morphologies. We extend a state-of-the-art machine learning IK solver to operate on the well known Skinned Multi-Person Linear model (SMPL). We call our model SMPL-IK, and show that when integrated into real-time 3D software, this extended system opens up opportunities for defining novel AI-assisted animation workflows. For example, when chained with existing pose estimation algorithms, SMPL-IK accelerates posing by allowing users to bootstrap 3D scenes from 2D images while allowing for further editing. Additionally, we propose a novel SMPL Shape Inversion mechanism (SMPL-SI) to map arbitrary humanoid characters to the SMPL space, allowing artists to leverage SMPL-IK on custom characters. In addition to qualitative demos showing proposed tools, we present quantitative SMPL-IK baselines on the H36M and AMASS datasets. Our code is publicly available https://github.com/boreshkinai/smpl-ik." @default.
- W4309376484 created "2022-11-26" @default.
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- W4309376484 date "2022-11-22" @default.
- W4309376484 modified "2023-10-04" @default.
- W4309376484 title "SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows" @default.
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- W4309376484 doi "https://doi.org/10.1145/3550340.3564227" @default.
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