DreamPolisher: Towards High-Quality Text-to-3D Generation via Geometric
  Diffusion
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.
6603982c9b248dec28e972c2
Yuanze Lin
claimed_verified
2025-06-05T15:10:47.824Z
684178483b8c48d81babcbf5
https://cdn-avatars.huggingface.co/v1/production/uploads/684178483b8c48d81babcbf5/cFmEFnm_CPaqxb7yw17zv.jpeg
Yuanze Lin
user
YuanzeLin
6603982c9b248dec28e972c3
Ronald Clark
admin_assigned
2024-03-27T09:55:22.892Z
6305ee63d70693fdf1c7dbb8
/avatars/0e81ed3757b4e65be82063b538c3fe49.svg
Ronald Clark
user
r0nn13
6603982c9b248dec28e972c4
Philip Torr
admin_assigned
2024-03-27T09:55:33.385Z
6565ed28a5ec0231cb07225f
/avatars/7f95bba9aa7811d56eecb380827abfac.svg
prof philip torr
user
philiptorr
DreamPolisher: Towards High-Quality Text-to-3D Generation via Geometric
  Diffusion
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.
DreamPolisher uses Gaussian Splatting with geometric guidance to generate detailed and consistent 3D objects from text descriptions, improving upon existing text-to-3D methods.
Gaussian Splatting geometric guidance cross-view consistency textual descriptions text-to-3D generation coarse 3D generation geometric optimization ControlNet texture fidelity 3D objects textual prompts object categories