  The development of generative models that create 3D content from a text
prompt has made considerable strides thanks to the use of the score
distillation sampling (SDS) method on pre-trained diffusion models for image
generation. However, the SDS method is also the source of several artifacts,
such as the Janus problem, the misalignment between the text prompt and the
generated 3D model, and 3D model inaccuracies. While existing methods heavily
rely on the qualitative assessment of these artifacts through visual inspection
of a limited set of samples, in this work we propose more objective
quantitative evaluation metrics, which we cross-validate via human ratings, and
show analysis of the failure cases of the SDS technique. We demonstrate the
effectiveness of this analysis by designing a novel computationally efficient
baseline model that achieves state-of-the-art performance on the proposed
metrics while addressing all the above-mentioned artifacts.
