4.4. Comparison with ATT3D

In this section, we compare our method with a con-
current work ATT3D [17]. Considering that the code of
ATT3D is not publicly available, we retrain and evaluate our
model on the dataset of ATT3D composed of 2,400 texts for
a better comparison. Although ATT3D also aims for fast
text-to-3D generation similar to this work, it requires per-
prompt fine-tuning after each network inference, leading to
lower efficiency. Moreover, with the techniques proposed in
Section 3, our network is more effective than the model of
ATT3D, which significantly improves the generation qual-
ity.
Qualitative Comparison. We compare our generated ob-
jects with those of ATT3D in Figure 13. The animals gen-
erated by ATT3D are blurry and lack essential details, and
some described items in the prompts are not distinguishable.

SOTA

ATT3D
Instant3D