  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.
