DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content
  Creation
Recent advances in 3D content creation mostly leverage optimization-based 3D
generation via score distillation sampling (SDS). Though promising results have
been exhibited, these methods often suffer from slow per-sample optimization,
limiting their practical usage. In this paper, we propose DreamGaussian, a
novel 3D content generation framework that achieves both efficiency and quality
simultaneously. Our key insight is to design a generative 3D Gaussian Splatting
model with companioned mesh extraction and texture refinement in UV space. In
contrast to the occupancy pruning used in Neural Radiance Fields, we
demonstrate that the progressive densification of 3D Gaussians converges
significantly faster for 3D generative tasks. To further enhance the texture
quality and facilitate downstream applications, we introduce an efficient
algorithm to convert 3D Gaussians into textured meshes and apply a fine-tuning
stage to refine the details. Extensive experiments demonstrate the superior
efficiency and competitive generation quality of our proposed approach.
Notably, DreamGaussian produces high-quality textured meshes in just 2 minutes
from a single-view image, achieving approximately 10 times acceleration
compared to existing methods.
65164b016137c31730198cda
Jiaxiang Tang
65164b016137c31730198cdb
Jiawei Ren
claimed_verified
2023-09-29T07:04:18.963Z
623f533a28672458f749b8e9
https://cdn-avatars.huggingface.co/v1/production/uploads/1651044799068-623f533a28672458f749b8e9.png
Jiawe Ren
user
jiawei011
65164b016137c31730198cdc
Hang Zhou
65164b016137c31730198cdd
Ziwei Liu
admin_assigned
2023-09-29T07:07:58.041Z
62ab1ac1d48b4d8b048a3473
https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
Ziwei Liu
user
liuziwei7
65164b016137c31730198cde
Gang Zeng
DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content
  Creation
Recent advances in 3D content creation mostly leverage optimization-based 3D
generation via score distillation sampling (SDS). Though promising results have
been exhibited, these methods often suffer from slow per-sample optimization,
limiting their practical usage. In this paper, we propose DreamGaussian, a
novel 3D content generation framework that achieves both efficiency and quality
simultaneously. Our key insight is to design a generative 3D Gaussian Splatting
model with companioned mesh extraction and texture refinement in UV space. In
contrast to the occupancy pruning used in Neural Radiance Fields, we
demonstrate that the progressive densification of 3D Gaussians converges
significantly faster for 3D generative tasks. To further enhance the texture
quality and facilitate downstream applications, we introduce an efficient
algorithm to convert 3D Gaussians into textured meshes and apply a fine-tuning
stage to refine the details. Extensive experiments demonstrate the superior
efficiency and competitive generation quality of our proposed approach.
Notably, DreamGaussian produces high-quality textured meshes in just 2 minutes
from a single-view image, achieving approximately 10 times acceleration
compared to existing methods.
DreamGaussian is a 3D content generation framework that combines generative Gaussian splatting with mesh extraction and texture refinement for high efficiency and quality.
score distillation sampling DreamGaussian generative 3D Gaussian Splatting companioned mesh extraction UV space occupancy pruning Neural Radiance Fields progressive densification textured meshes single-view image