MetaDreamer: Efficient Text-to-3D Creation With Disentangling Geometry
  and Texture
Generative models for 3D object synthesis have seen significant advancements
with the incorporation of prior knowledge distilled from 2D diffusion models.
Nevertheless, challenges persist in the form of multi-view geometric
inconsistencies and slow generation speeds within the existing 3D synthesis
frameworks. This can be attributed to two factors: firstly, the deficiency of
abundant geometric a priori knowledge in optimization, and secondly, the
entanglement issue between geometry and texture in conventional 3D generation
methods.In response, we introduce MetaDreammer, a two-stage optimization
approach that leverages rich 2D and 3D prior knowledge. In the first stage, our
emphasis is on optimizing the geometric representation to ensure multi-view
consistency and accuracy of 3D objects. In the second stage, we concentrate on
fine-tuning the geometry and optimizing the texture, thereby achieving a more
refined 3D object. Through leveraging 2D and 3D prior knowledge in two stages,
respectively, we effectively mitigate the interdependence between geometry and
texture. MetaDreamer establishes clear optimization objectives for each stage,
resulting in significant time savings in the 3D generation process. Ultimately,
MetaDreamer can generate high-quality 3D objects based on textual prompts
within 20 minutes, and to the best of our knowledge, it is the most efficient
text-to-3D generation method. Furthermore, we introduce image control into the
process, enhancing the controllability of 3D generation. Extensive empirical
evidence confirms that our method is not only highly efficient but also
achieves a quality level that is at the forefront of current state-of-the-art
3D generation techniques.
655ac4d04a49906b2c7f5aed
Lincong Feng
admin_assigned
2023-11-20T09:31:54.232Z
64fecad241444912cdd07b5d
/avatars/a5953a31850e18afd16437c92a86ba67.svg
Lincong Feng
user
Lincong
655ac4d04a49906b2c7f5aee
Muyu Wang
655ac4d04a49906b2c7f5aef
Maoyu Wang
admin_assigned
2023-11-20T10:05:48.878Z
64c097f5f613170e7bea2fe4
/avatars/0b1bfbf3d119791a02208d13d0c9455a.svg
Wang Maoyu
user
bstkael
655ac4d04a49906b2c7f5af0
Kuo Xu
admin_assigned
2023-11-20T10:05:36.965Z
65499cd0a46359771485ed62
/avatars/cc00354891814e9be61af1fa8f2a0baf.svg
KuoXu
user
XKuo
655ac4d04a49906b2c7f5af1
Xiaoli Liu
MetaDreamer: Efficient Text-to-3D Creation With Disentangling Geometry
  and Texture
Generative models for 3D object synthesis have seen significant advancements
with the incorporation of prior knowledge distilled from 2D diffusion models.
Nevertheless, challenges persist in the form of multi-view geometric
inconsistencies and slow generation speeds within the existing 3D synthesis
frameworks. This can be attributed to two factors: firstly, the deficiency of
abundant geometric a priori knowledge in optimization, and secondly, the
entanglement issue between geometry and texture in conventional 3D generation
methods.In response, we introduce MetaDreammer, a two-stage optimization
approach that leverages rich 2D and 3D prior knowledge. In the first stage, our
emphasis is on optimizing the geometric representation to ensure multi-view
consistency and accuracy of 3D objects. In the second stage, we concentrate on
fine-tuning the geometry and optimizing the texture, thereby achieving a more
refined 3D object. Through leveraging 2D and 3D prior knowledge in two stages,
respectively, we effectively mitigate the interdependence between geometry and
texture. MetaDreamer establishes clear optimization objectives for each stage,
resulting in significant time savings in the 3D generation process. Ultimately,
MetaDreamer can generate high-quality 3D objects based on textual prompts
within 20 minutes, and to the best of our knowledge, it is the most efficient
text-to-3D generation method. Furthermore, we introduce image control into the
process, enhancing the controllability of 3D generation. Extensive empirical
evidence confirms that our method is not only highly efficient but also
achieves a quality level that is at the forefront of current state-of-the-art
3D generation techniques.
MetaDreammer, a two-stage optimization approach, integrates 2D and 3D prior knowledge to generate high-quality, multi-view consistent 3D objects from textual prompts efficiently.
generative models 3D object synthesis 2D diffusion models multi-view geometric inconsistencies 3D prior knowledge optimization geometry texture MetaDreammer text-to-3D generation image control