FIND: An Unsupervised Implicit 3D Model of Articulated Human Feet

• URL: http://arxiv.org/abs/2210.12241v1
• Date: Fri, 21 Oct 2022 20:47:16 GMT
• Title: FIND: An Unsupervised Implicit 3D Model of Articulated Human Feet
• Authors: Oliver Boyne, James Charles, Roberto Cipolla
• Abstract summary: We present a high fidelity and articulated 3D human foot model. The model is parameterised by a disentangled latent code in terms of shape, texture and articulated pose.
• Score: 27.85606375080643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper we present a high fidelity and articulated 3D human foot model. The model is parameterised by a disentangled latent code in terms of shape, texture and articulated pose. While high fidelity models are typically created with strong supervision such as 3D keypoint correspondences or pre-registration, we focus on the difficult case of little to no annotation. To this end, we make the following contributions: (i) we develop a Foot Implicit Neural Deformation field model, named FIND, capable of tailoring explicit meshes at any resolution i.e. for low or high powered devices; (ii) an approach for training our model in various modes of weak supervision with progressively better disentanglement as more labels, such as pose categories, are provided; (iii) a novel unsupervised part-based loss for fitting our model to 2D images which is better than traditional photometric or silhouette losses; (iv) finally, we release a new dataset of high resolution 3D human foot scans, Foot3D. On this dataset, we show our model outperforms a strong PCA implementation trained on the same data in terms of shape quality and part correspondences, and that our novel unsupervised part-based loss improves inference on images.

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