Kavli Affiliate: Feng Wang
| First 5 Authors: Zilong Chen, Feng Wang, Yikai Wang, Huaping Liu,
| Summary:
Automatic text-to-3D generation that combines Score Distillation Sampling
(SDS) with the optimization of volume rendering has achieved remarkable
progress in synthesizing realistic 3D objects. Yet most existing text-to-3D
methods by SDS and volume rendering suffer from inaccurate geometry, e.g., the
Janus issue, since it is hard to explicitly integrate 3D priors into implicit
3D representations. Besides, it is usually time-consuming for them to generate
elaborate 3D models with rich colors. In response, this paper proposes GSGEN, a
novel method that adopts Gaussian Splatting, a recent state-of-the-art
representation, to text-to-3D generation. GSGEN aims at generating high-quality
3D objects and addressing existing shortcomings by exploiting the explicit
nature of Gaussian Splatting 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 3D point
cloud diffusion prior along with the ordinary 2D SDS optimization, ensuring a
sensible and 3D-consistent rough shape. Subsequently, the obtained Gaussians
undergo an iterative appearance refinement to enrich texture 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 assets with delicate details and accurate geometry. Extensive
evaluations demonstrate the effectiveness of our method, especially for
capturing high-frequency components. Our code is available at
https://github.com/gsgen3d/gsgen
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