Image-Guided Shape-from-Template Using Mesh Inextensibility Constraints

• URL: http://arxiv.org/abs/2507.22699v1
• Date: Wed, 30 Jul 2025 14:09:39 GMT
• Title: Image-Guided Shape-from-Template Using Mesh Inextensibility Constraints
• Abstract summary: Shape-from-Template (SfT) refers to the class of methods that reconstruct the 3D shape of a deforming object from images/videos using a 3D template.<n>Traditional SfT methods require point correspondences between images and the texture of the 3D template in order to reconstruct 3D shapes from images/videos in real time.<n>Modern SfT methods use a correspondence-free approach by incorporating deep neural networks to reconstruct 3D objects.
• Score: 5.504853768307439
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Shape-from-Template (SfT) refers to the class of methods that reconstruct the 3D shape of a deforming object from images/videos using a 3D template. Traditional SfT methods require point correspondences between images and the texture of the 3D template in order to reconstruct 3D shapes from images/videos in real time. Their performance severely degrades when encountered with severe occlusions in the images because of the unavailability of correspondences. In contrast, modern SfT methods use a correspondence-free approach by incorporating deep neural networks to reconstruct 3D objects, thus requiring huge amounts of data for supervision. Recent advances use a fully unsupervised or self-supervised approach by combining differentiable physics and graphics to deform 3D template to match input images. In this paper, we propose an unsupervised SfT which uses only image observations: color features, gradients and silhouettes along with a mesh inextensibility constraint to reconstruct at a $400\times$ faster pace than (best-performing) unsupervised SfT. Moreover, when it comes to generating finer details and severe occlusions, our method outperforms the existing methodologies by a large margin. Code is available at https://github.com/dvttran/nsft.

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