Awesome Text-to-3D Project Overview
Awesome Text-to-3D is an engaging project that brings together the latest advancements in converting text descriptions into 3D models. This curated collection showcases a variety of innovative approaches in the field of Text-to-3D and Diffusion-to-3D technologies. The project has taken inspiration from the well-regarded awesome-NeRF repository and stands as the first comprehensive curation in this emerging domain.
Recent Updates
The project is continuously evolving, with several key updates enriching its content:
- February 4, 2024: Links to project pages and codes have been added, enhancing accessibility to the latest developments and resources.
- September 2, 2023: The introduction of Level One Categorization aids in organizing the multitude of projects more efficiently.
- November 11, 2023: Tutorial videos were included to provide in-depth guidance on working with various tools and techniques.
- August 5, 2023: Citations in BibTeX format were made available for better academic referencing.
- July 6, 2023: The initial list of resources and projects was created, marking the genesis of this collection.
Papers and Projects
The repository holds a treasure trove of research papers that span various aspects of Text-to-3D conversions:
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Zero-Shot Text-Guided Object Generation with Dream Fields: This paper presents methodologies to generate objects from text descriptions using zero prior knowledge, contributing significantly to the CVPR 2022 conference.
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CLIP-Forge: This project pushes the boundaries in zero-shot text-to-shape generation by leveraging CLIP models to create 3D shapes directly from textual prompts.
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PureCLIPNERF: It dissects the role of CLIP guidance in voxel grid models, enhancing understanding in this niche area of NeRF models.
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SDFusion: Showcasing multimodal capabilities, this research delves into 3D shape completion, reconstruction, and generation, showcasing work presented at CVPR 2023.
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DreamFusion and Dream3D: These projects blend text with 2D diffusion methodologies to create 3D models, demonstrating the multifaceted approaches presented at ICLR 2023.
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NeuralLift-360 and Point-E: Both of these works explore the creation of 3D objects from minimal input, whether a single photo or a complex textual description.
Highlights of Other Projects
The collection further includes innovative approaches like Magic3D, which focuses on high-resolution text-to-3D creation, and RealFusion, which explores 360° reconstructions from a single image. Projects like Latent-NeRF and Magic123 emphasize advancements in both shape-guided generation and the combination of 2D and 3D diffusion models for object creation.
Learning Resources
To aid learning and development, the repository includes tutorial videos and provides links to source codes, facilitating users in experimenting and contributing to this dynamic field. It also supports academia by offering BibTeX citations for scholarly use.
This curated showcase is an invaluable resource for anyone interested in the intersection of text descriptions and 3D model generation, offering insight into both pioneering and refined techniques in the burgeoning field of AI-generated 3D content.