MetaDreamer: Efficient Text-to-3D Creation With Disentangling Geometry and Texture

• URL: http://arxiv.org/abs/2311.10123v1
• Date: Thu, 16 Nov 2023 11:35:10 GMT
• Title: MetaDreamer: Efficient Text-to-3D Creation With Disentangling Geometry and Texture
• Authors: Lincong Feng, Muyu Wang, Maoyu Wang, Kuo Xu, Xiaoli Liu
• Abstract summary: 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.
• Score: 1.5601951993287981
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Retrieval-Augmented Score Distillation for Text-to-3D Generation [30.57225047257049]
  We introduce novel framework for retrieval-based quality enhancement in text-to-3D generation. We conduct extensive experiments to demonstrate that ReDream exhibits superior quality with increased geometric consistency.
  arXiv  Detail & Related papers  (2024-02-05T12:50:30Z)
• Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D Prior [52.44678180286886]
  2D diffusion models find a distillation approach that achieves excellent generalization and rich details without any 3D data. We propose Sherpa3D, a new text-to-3D framework that achieves high-fidelity, generalizability, and geometric consistency simultaneously.
  arXiv  Detail & Related papers  (2023-12-11T18:59:18Z)
• Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors [16.93758384693786]
  Bidirectional Diffusion(BiDiff) is a unified framework that incorporates both a 3D and a 2D diffusion process. Our model achieves high-quality, diverse, and scalable 3D generation.
  arXiv  Detail & Related papers  (2023-12-07T10:00:04Z)
• X-Dreamer: Creating High-quality 3D Content by Bridging the Domain Gap Between Text-to-2D and Text-to-3D Generation [61.48050470095969]
  X-Dreamer is a novel approach for high-quality text-to-3D content creation. It bridges the gap between text-to-2D and text-to-3D synthesis.
  arXiv  Detail & Related papers  (2023-11-30T07:23:00Z)
• GeoDream: Disentangling 2D and Geometric Priors for High-Fidelity and Consistent 3D Generation [66.46683554587352]
  We present GeoDream, a novel method that incorporates explicit generalized 3D priors with 2D diffusion priors. Specifically, we first utilize a multi-view diffusion model to generate posed images and then construct cost volume from the predicted image. We further propose to harness 3D geometric priors to unlock the great potential of 3D awareness in 2D diffusion priors via a disentangled design.
  arXiv  Detail & Related papers  (2023-11-29T15:48:48Z)
• Guide3D: Create 3D Avatars from Text and Image Guidance [55.71306021041785]
  Guide3D is a text-and-image-guided generative model for 3D avatar generation based on diffusion models. Our framework produces topologically and structurally correct geometry and high-resolution textures.
  arXiv  Detail & Related papers  (2023-08-18T17:55:47Z)
• Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors [104.79392615848109]
  We present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes from a single unposed image. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture.
  arXiv  Detail & Related papers  (2023-06-30T17:59:08Z)
• XDGAN: Multi-Modal 3D Shape Generation in 2D Space [60.46777591995821]
  We propose a novel method to convert 3D shapes into compact 1-channel geometry images and leverage StyleGAN3 and image-to-image translation networks to generate 3D objects in 2D space. The generated geometry images are quick to convert to 3D meshes, enabling real-time 3D object synthesis, visualization and interactive editing. We show both quantitatively and qualitatively that our method is highly effective at various tasks such as 3D shape generation, single view reconstruction and shape manipulation, while being significantly faster and more flexible compared to recent 3D generative models.
  arXiv  Detail & Related papers  (2022-10-06T15:54:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.