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We present Semantify: a self-supervised method that utilizes the semanticpower of CLIP language-vision foundation model to simplify the control of 3Dmorphable models. Given a parametric model, training data is created byrandomly sampling the model's parameters, creating various shapes and renderingthem. The similarity between the output images and a set of word descriptors iscalculated in CLIP's latent space. Our key idea is first to choose a small setof semantically meaningful and disentangled descriptors that characterize the3DMM, and then learn a non-linear mapping from scores across this set to theparametric coefficients of the given 3DMM. The non-linear mapping is defined bytraining a neural network without a human-in-the-loop. We present results onnumerous 3DMMs: body shape models, face shape and expression models, as well asanimal shapes. We demonstrate how our method defines a simple slider interfacefor intuitive modeling, and show how the mapping can be used to instantly fit a3D parametric body shape to in-the-wild images.

Semantify: Simplifying the Control of 3D Morphable Models using CLIP