DreamPolisher: Towards High-Quality Text-to-3D Generation via Geometric
  Diffusion
Yuanze Lin Ronald Clark Philip Torr
We present DreamPolisher, a novel Gaussian Splatting based method with
geometric guidance, tailored to learn cross-view consistency and intricate
detail from textual descriptions. While recent progress on text-to-3D
generation methods have been promising, prevailing methods often fail to ensure
view-consistency and textural richness. This problem becomes particularly
noticeable for methods that work with text input alone. To address this, we
propose a two-stage Gaussian Splatting based approach that enforces geometric
consistency among views. Initially, a coarse 3D generation undergoes refinement
via geometric optimization. Subsequently, we use a ControlNet driven refiner
coupled with the geometric consistency term to improve both texture fidelity
and overall consistency of the generated 3D asset. Empirical evaluations across
diverse textual prompts spanning various object categories demonstrate the
efficacy of DreamPolisher in generating consistent and realistic 3D objects,
aligning closely with the semantics of the textual instructions.