Text-to-3D using Gaussian Splatting
In this paper, we present Gaussian Splatting based text-to-3D generation
(GSGEN), a novel approach for generating high-quality 3D objects. Previous
methods suffer from inaccurate geometry and limited fidelity due to the absence
of 3D prior and proper representation. We leverage 3D Gaussian Splatting, a
recent state-of-the-art representation, to address existing shortcomings by
exploiting the explicit nature that enables the incorporation of 3D prior.
Specifically, our method adopts a progressive optimization strategy, which
includes a geometry optimization stage and an appearance refinement stage. In
geometry optimization, a coarse representation is established under a 3D
geometry prior along with the ordinary 2D SDS loss, ensuring a sensible and
3D-consistent rough shape. Subsequently, the obtained Gaussians undergo an
iterative refinement to enrich details. In this stage, we increase the number
of Gaussians by compactness-based densification to enhance continuity and
improve fidelity. With these designs, our approach can generate 3D content with
delicate details and more accurate geometry. Extensive evaluations demonstrate
the effectiveness of our method, especially for capturing high-frequency
components. Video results are provided at https://gsgen3d.github.io. Our code
is available at https://github.com/gsgen3d/gsgen
6516483aa73c1d264877b945
Zilong Chen
admin_assigned
2023-09-29T11:47:42.726Z
644e3e5f030210812f413073
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/uW8TKV2sds97lFBwnt6JK.jpeg
Zilong Chen
user
heheyas
6516483aa73c1d264877b946
Feng Wang
6516483aa73c1d264877b947
Huaping Liu
admin_assigned
2023-09-29T11:47:33.685Z
63cb64351b705cc951e98fc8
/avatars/eea059d9295767d7695b5a4d5ff8f53c.svg
Liu
user
Huaping
Text-to-3D using Gaussian Splatting
In this paper, we present Gaussian Splatting based text-to-3D generation
(GSGEN), a novel approach for generating high-quality 3D objects. Previous
methods suffer from inaccurate geometry and limited fidelity due to the absence
of 3D prior and proper representation. We leverage 3D Gaussian Splatting, a
recent state-of-the-art representation, to address existing shortcomings by
exploiting the explicit nature that enables the incorporation of 3D prior.
Specifically, our method adopts a progressive optimization strategy, which
includes a geometry optimization stage and an appearance refinement stage. In
geometry optimization, a coarse representation is established under a 3D
geometry prior along with the ordinary 2D SDS loss, ensuring a sensible and
3D-consistent rough shape. Subsequently, the obtained Gaussians undergo an
iterative refinement to enrich details. In this stage, we increase the number
of Gaussians by compactness-based densification to enhance continuity and
improve fidelity. With these designs, our approach can generate 3D content with
delicate details and more accurate geometry. Extensive evaluations demonstrate
the effectiveness of our method, especially for capturing high-frequency
components. Video results are provided at https://gsgen3d.github.io. Our code
is available at https://github.com/gsgen3d/gsgen
Gaussian Splatting based text-to-3D generation improves geometry accuracy and fidelity through progressive optimization, including geometry optimization and appearance refinement.
Gaussian Splatting 3D prior progressive optimization geometry optimization appearance refinement Gaussian densification