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Point-e is a system for 3D model synthesis that generates three-dimensional point clouds from natural language descriptions and two-dimensional images. It utilizes diffusion models to synthesize these spatial representations based on text prompts or source images. The project includes specialized tools for refining these outputs, such as a point cloud upsampler to increase the density and resolution of low-resolution models. It also provides a mesh converter that uses distance function regression to transform raw point cloud data into structured 3D meshes. The broader capability surface cove
Shap-E is a generative 3D modeling system that creates three-dimensional digital assets from natural language descriptions or two-dimensional images. It functions as a generative model capable of producing three-dimensional implicit functions and assets. The project includes a 3D latent encoder that converts trimeshes and 3D models into latent representations using point clouds and multiview renders. It utilizes an image-to-3D generator to produce assets from synthetic view images and a text-to-3D generator to build shapes from text prompts. The system implements a pipeline involving latent
ImageBind is a multi-modal embedding model and joint representation learner that maps images, text, audio, and other modalities into a single shared vector space. It functions as a cross-modal retrieval framework designed to bind multiple sensory inputs into one cohesive mathematical embedding. The system uses a contrastive learning architecture to align disparate data types by maximizing the similarity between related samples. This allows the model to perform zero-shot multimodal classification and execute cross-modal data retrieval, such as locating visual content via natural language descr
Threestudio is a 3D generative AI framework designed to create three-dimensional assets from text prompts and images. It provides specialized pipelines for text-to-3D generation and image-to-3D reconstruction, utilizing a neural radiance field trainer to produce geometry and textures. The framework is distinguished by its support for hybrid geometry backends, including signed distance functions, tetrahedra grids, and volume grids. It employs score distillation sampling to guide the generation process and features a modular plugin system for loading custom modules and nodes. The system covers
Generating shapes using natural language can enable new ways of imagining and creating the things around us. While significant recent progress has been made in text-to-image generation, text-to-shape generation remains a challenging problem due to the unavailability of paired text and shape data…
The main features of autodeskailab/clip-forge are: 3D Generation, Cross-Modal Models, Generative 3D Modeling.
Open-source alternatives to autodeskailab/clip-forge include: openai/point-e — Point-e is a system for 3D model synthesis that generates three-dimensional point clouds from natural language… threestudio-project/threestudio — Threestudio is a 3D generative AI framework designed to create three-dimensional assets from text prompts and images.… openai/shap-e — Shap-E is a generative 3D modeling system that creates three-dimensional digital assets from natural language… facebookresearch/imagebind — ImageBind is a multi-modal embedding model and joint representation learner that maps images, text, audio, and other… microsoft/trellis — TRELLIS is a 3D generative AI model and latent diffusion framework designed to transform natural language descriptions… nv-tlabs/get3d — GET3D is a generative 3D mesh model and rendering framework designed to synthesize high-quality textured shapes and…