  Computer graphics, 3D computer vision and robotics communities have produced
multiple approaches to represent and generate 3D shapes, as well as a vast
number of use cases. However, single-view reconstruction remains a challenging
topic that can unlock various interesting use cases such as interactive design.
In this work, we propose a novel framework that leverages the intermediate
latent spaces of Vision Transformer (ViT) and a joint image-text
representational model, CLIP, for fast and efficient Single View Reconstruction
(SVR). More specifically, we propose a novel mapping network architecture that
learns a mapping between deep features extracted from ViT and CLIP, and the
latent space of a base 3D generative model. Unlike previous work, our method
enables view-agnostic reconstruction of 3D shapes, even in the presence of
large occlusions. We use the ShapeNetV2 dataset and perform extensive
experiments with comparisons to SOTA methods to demonstrate our method's
effectiveness.
