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
admin_assigned
2024-03-27T09:55:07.270Z
633be9e4c0fb6fd232ee679d
/avatars/04984852e6d139db5975015d1a7c9d5f.svg
Yuanze Lin
user
Yuanze
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.
Gaussian Splatting geometric guidance cross-view consistency textual descriptions text-to-3D generation geometric consistency coarse 3D generation refinement geometric optimization ControlNet texture fidelity semantic instructions