Summary MetaDreamer Text-to-3D Creation with Disentangling Geometry and Texture arxiv.org
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One Line
MetaDreamer is a text-to-3D method that enhances generation by resolving geometric inconsistencies and slow speeds, resulting in efficient and high-quality outcomes.
Slides
Slide Presentation (9 slides)
Key Points
- MetaDreamer is an efficient and high-quality text-to-3D generation method.
- It leverages the disentangling of geometric and texture priors.
- MetaDreamer consists of two stages: the geometry stage and the texture stage.
- It achieves multi-view consistency and accuracy of 3D objects.
- MetaDreamer can generate high-quality 3D objects based on textual prompts within 20 minutes.
- It outperforms existing text-to-3D methods in terms of both efficiency and quality.
- MetaDreamer addresses the entanglement issue between geometry and texture to enhance overall quality.
- Future work includes addressing limitations in multi-object generation tasks.
Summaries
20 word summary
MetaDreamer is a text-to-3D method that improves generation by addressing geometric inconsistencies and slow speeds, achieving efficient and high-quality results.
65 word summary
MetaDreamer is a text-to-3D generation method that improves upon existing frameworks by addressing geometric inconsistencies and slow generation speeds. It consists of two stages: geometry and texture. The geometry stage establishes strong multi-view consistency and complete geometry, while the texture stage refines the model and enhances its texture. By disentangling geometry and texture, MetaDreamer achieves equilibrium in learning, resulting in efficient and high-quality 3D generation.
137 word summary
MetaDreamer is a text-to-3D generation method that overcomes challenges in existing frameworks by addressing multi-view geometric inconsistencies and slow generation speeds. It consists of two stages: the geometry stage and the texture stage. The geometry stage establishes the geometric structure of the 3D object using 2D and 3D prior knowledge, resulting in strong multi-view consistency and complete geometry. The texture stage refines the geometric model and enhances its texture by transferring prior knowledge from 2D images. By disentangling the interaction between geometry and texture, MetaDreamer achieves equilibrium in learning and significantly reduces the time required for 3D generation. It outperforms state-of-the-art methods in terms of efficiency and quality, achieving higher CLIP similarity scores and the highest scores according to T3 Bench benchmarks. MetaDreamer leverages the disentangling of geometric and texture priors to generate high-quality 3D objects efficiently.
375 word summary
MetaDreamer is a text-to-3D generation method that addresses the challenges of multi-view geometric inconsistencies and slow generation speeds in existing frameworks. It consists of two stages: the geometry stage and the texture stage. In the geometry stage, the emphasis is on optimizing the geometric representation for multi-view consistency and accuracy. The texture stage focuses on fine-tuning the geometry and optimizing the texture.
The first stage of MetaDreamer rapidly establishes the fundamental geometric structure of the 3D object using 2D and 3D prior knowledge. It leverages a pretrained view-dependent diffusion model to guide the optimization process, resulting in 3D objects with strong multi-view consistency and complete geometry.
In the second stage, MetaDreamer refines the geometric model and enhances its texture. It uses pretrained text-to-image diffusion models to transfer prior knowledge from 2D images into the 3D model. The texture optimization stage improves both the geometry and textures of the 3D object.
By disentangling the interaction between geometry and texture, MetaDreamer achieves equilibrium in learning and makes the optimization objectives more explicit. This leads to significant time savings in the 3D generation process. MetaDreamer can generate high-quality 3D objects based on textual prompts within 20 minutes, making it the most efficient text-to-3D generation method available.
Comparisons with state-of-the-art methods show that MetaDreamer outperforms them in terms of both efficiency and quality. It achieves higher CLIP similarity scores, indicating better consistency between the generated 3D objects and the input text prompts. MetaDreamer also achieves the highest scores in terms of quality and alignment according to T3 Bench benchmarks.
MetaDreamer addresses the entanglement issue between geometry and texture by using only geometry priors in the coarse stage and only texture priors in the fine stage. This disentanglement allows for more effective optimization and enhances the overall quality of the generated 3D objects.
In future work, MetaDreamer aims to improve multi-object generation tasks by incorporating more multi-object geometric prior knowledge into the model.
In conclusion, MetaDreamer is an efficient and high-quality text-to-3D generation method that leverages the disentangling of geometric and texture priors. It achieves state-of-the-art results in terms of both efficiency and quality, outperforming existing methods. The two-stage optimization approach and the incorporation of 2D and 3D prior knowledge contribute to significant time savings and improved 3D object generation.
462 word summary
MetaDreamer is an efficient and high-quality text-to-3D generation method that leverages the disentangling of geometric and texture priors. It addresses the challenges of multi-view geometric inconsistencies and slow generation speeds in existing 3D synthesis frameworks. The method consists of two stages: the geometry stage and the texture stage. In the geometry stage, the emphasis is on optimizing the geometric representation to ensure multi-view consistency and accuracy of 3D objects. In the texture stage, the focus is on fine-tuning the geometry and optimizing the texture to achieve a more refined 3D object.
The first stage of MetaDreamer utilizes 2D and 3D prior knowledge to rapidly establish the fundamental geometric structure of the 3D object. It leverages a pretrained view-dependent diffusion model to guide the optimization process. The resulting 3D objects demonstrate strong multi-view consistency and possess complete geometry.
In the second stage, MetaDreamer further refines the geometric model obtained in the first stage and enhances its texture. It employs pretrained text-to-image diffusion models to transfer prior knowledge from 2D images into the 3D model through score distillation sampling. The texture optimization stage focuses on improving both the geometry and textures of the 3D object.
MetaDreamer achieves equilibrium in learning between geometry and texture by incorporating two distinct sources of prior knowledge. By disentangling the interaction between geometry and texture, the optimization objectives for each stage become more explicit, leading to significant time savings in the 3D generation process. MetaDreamer can generate high-quality 3D objects based on textual prompts within 20 minutes, making it the most efficient text-to-3D generation method currently available.
Quantitative and qualitative comparisons with state-of-the-art text-to-3D methods demonstrate that MetaDreamer outperforms these methods in terms of both efficiency and quality. It achieves higher CLIP similarity scores, indicating better consistency between the generated 3D objects and the input text prompts. The T3 Bench benchmarks also show that MetaDreamer achieves the highest scores in terms of quality and alignment.
MetaDreamer also addresses the entanglement issue between geometry and texture by using only geometry priors in the coarse stage and only texture priors in the fine stage. This disentanglement allows for more effective optimization and enhances the overall quality of the generated 3D objects.
In terms of future work, MetaDreamer has limitations in multi-object generation tasks due to the lack of prior knowledge about multiple objects in geometric priors. The authors plan to address this challenge by introducing more multi-object geometric prior knowledge into the model.
In conclusion, MetaDreamer is an efficient and high-quality text-to-3D generation method that leverages the disentangling of geometric and texture priors. It achieves state-of-the-art results in terms of both efficiency and quality, outperforming existing text-to-3D methods. The two-stage optimization approach and the incorporation of 2D and 3D prior knowledge contribute to significant time savings and improved 3D object generation.