  Text-to-3D generation from a single-view image is a popular but challenging
task in 3D vision. Although numerous methods have been proposed, existing works
still suffer from the inconsistency issues, including 1) semantic
inconsistency, 2) geometric inconsistency, and 3) saturation inconsistency,
resulting in distorted, overfitted, and over-saturated generations. In light of
the above issues, we present Consist3D, a three-stage framework Chasing for
semantic-, geometric-, and saturation-Consistent Text-to-3D generation from a
single image, in which the first two stages aim to learn parameterized
consistency tokens, and the last stage is for optimization. Specifically, the
semantic encoding stage learns a token independent of views and estimations,
promoting semantic consistency and robustness. Meanwhile, the geometric
encoding stage learns another token with comprehensive geometry and
reconstruction constraints under novel-view estimations, reducing overfitting
and encouraging geometric consistency. Finally, the optimization stage benefits
from the semantic and geometric tokens, allowing a low classifier-free guidance
scale and therefore preventing oversaturation. Experimental results demonstrate
that Consist3D produces more consistent, faithful, and photo-realistic 3D
assets compared to previous state-of-the-art methods. Furthermore, Consist3D
also allows background and object editing through text prompts.
