While conceptually simple, this network is substantially
challenging to design in practice. The main reason is that
the SDS loss provides relatively weak supervision signals,
making it difficult to learn an accurate connection between
the text condition and the 3D output for a common condi-
tion model. This stands in contrast to training models that
connect 2D images with texts, such as Stable Diffusion [29]
and CLIP [42], where numerous image-text pairs can pro-
vide strong supervision signals.
Instant3D