  We present Semantify: a self-supervised method that utilizes the semantic
power of CLIP language-vision foundation model to simplify the control of 3D
morphable models. Given a parametric model, training data is created by
randomly sampling the model's parameters, creating various shapes and rendering
them. The similarity between the output images and a set of word descriptors is
calculated in CLIP's latent space. Our key idea is first to choose a small set
of semantically meaningful and disentangled descriptors that characterize the
3DMM, and then learn a non-linear mapping from scores across this set to the
parametric coefficients of the given 3DMM. The non-linear mapping is defined by
training a neural network without a human-in-the-loop. We present results on
numerous 3DMMs: body shape models, face shape and expression models, as well as
animal shapes. We demonstrate how our method defines a simple slider interface
for intuitive modeling, and show how the mapping can be used to instantly fit a
3D parametric body shape to in-the-wild images.
