  We explore the task of text to 3D object generation using CLIP. Specifically,
we use CLIP for guidance without access to any datasets, a setting we refer to
as pure CLIP guidance. While prior work has adopted this setting, there is no
systematic study of mechanics for preventing adversarial generations within
CLIP. We illustrate how different image-based augmentations prevent the
adversarial generation problem, and how the generated results are impacted. We
test different CLIP model architectures and show that ensembling different
models for guidance can prevent adversarial generations within bigger models
and generate sharper results. Furthermore, we implement an implicit voxel grid
model to show how neural networks provide an additional layer of
regularization, resulting in better geometrical structure and coherency of
generated objects. Compared to prior work, we achieve more coherent results
with higher memory efficiency and faster training speeds.
