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arXiv · 2606.31115

JacobianAvatar: Temporally Consistent Semi-rigid Avatar Reconstruction from a Monocular Video

Changyeon Won·Min-Gyu Park·Seonghwan Park·Ju Hong Yoon·Hae-Gon Jeon
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Abstract

Generating realistic human avatars in complex motions--such as clothing dynamics--requires modeling of global and local deformations which remains challenging in monocular settings. We address this problem by leveraging neural Jacobian fields (NJFs) for representing semi-rigid deformations. We train self-supervised neural networks for predicting Jacobian matrices that give the pose-dependent deformations, by solving a Poisson equation. However, monocular input presents several difficulties such as self-occluded regions and invisible surfaces. To address these issues, we introduce three key components: a constrained Poisson solver, signed distance-based Jacobian regularization, and a deformation-guided residual flow loss, which together suppress boundary artifacts, recover frequently occluded regions such as armpits and thighs, and enforce temporal consistency during motion. Experiments on benchmark and in-the-wild videos demonstrate that our method generates temporally stable and geometrically coherent avatars, outperforming state-of-the-art approaches.

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Changyeon Won, Min-Gyu Park, Seonghwan Park, Ju Hong Yoon, Hae-Gon Jeon. 2026-06-30. JacobianAvatar: Temporally Consistent Semi-rigid Avatar Reconstruction from a Monocular Video. https://arxiv.org/abs/2606.31115

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