Audience
Companies searching for a text-to-3D content creation tool that creates 3D mesh models with unprecedented quality
About Magic3D
Together with image conditioning techniques as well as prompt-based editing approach, we provide users with new ways to control 3D synthesis, opening up new avenues to various creative applications. Magic3D can create high-quality 3D textured mesh models from input text prompts. It utilizes a coarse-to-fine strategy leveraging both low- and high-resolution diffusion priors for learning the 3D representation of the target content. Magic3D synthesizes 3D content with 8× higher-resolution supervision than DreamFusion while also being 2× faster. Given a coarse model generated with a base text prompt, we can modify parts of the text in the prompt, and then fine-tune the NeRF and 3D mesh models to obtain an edited high-resolution 3D mesh.
Other Popular Alternatives & Related Software
Tripo AI
Tripo is an AI-powered 3D workspace that enables users to generate production-ready 3D models from text, images, or sketches in seconds. The platform simplifies the entire 3D creation process by combining model generation, segmentation, texturing, rigging, and animation into one seamless workflow. With text-to-3D and image-to-3D capabilities, Tripo produces clean geometry and solid topology suitable for real-time engines and professional tools. Intelligent segmentation allows creators to split complex models into structured, editable parts with precision and control. AI texturing applies high-resolution, PBR-ready materials instantly, with Magic Brush enabling detailed local refinements. Automatic rigging and animation transform static meshes into animated assets without manual setup. Overall, Tripo dramatically reduces production time while making advanced 3D creation accessible to creators of all skill levels.
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Text2Mesh
Text2Mesh produces color and geometric details over a variety of source meshes, driven by a target text prompt. Our stylization results coherently blend unique and ostensibly unrelated combinations of text, capturing both global semantics and part-aware attributes. Our framework, Text2Mesh, stylizes a 3D mesh by predicting color and local geometric details which conform to a target text prompt. We consider a disentangled representation of a 3D object using a fixed mesh input (content) coupled with a learned neural network, which we term neural style field network. In order to modify style, we obtain a similarity score between a text prompt (describing style) and a stylized mesh by harnessing the representational power of CLIP. Text2Mesh requires neither a pre-trained generative model nor a specialized 3D mesh dataset. It can handle low-quality meshes (non-manifold, boundaries, etc.) with arbitrary genus, and does not require UV parameterization.
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Fast3D
Fast3D is a lightning‑fast AI‑powered 3D model generator that transforms text prompts or single/multi‑view images into professional‑grade mesh assets with customizable texture synthesis, mesh density, and style presets, all in under ten seconds without any modeling experience. It combines high‑fidelity PBR material generation with seamless tiling and intelligent style transfer, delivers precise geometric accuracy for realistic structures, and supports both text‑to‑3D and image‑to‑3D workflows. Outputs are compatible with any pipeline, offering export in GLB/GLTF, FBX, OBJ/MTL, and STL formats, while its intuitive web interface requires no login or setup. Whether for gaming, 3D printing, AR/VR, metaverse content, product design, or rapid prototyping, Fast3D’s AI core enables creators to explore diverse ideas through batch uploads, random inspiration galleries, and adjustable quality tiers, bringing concepts to 3D reality in seconds rather than days.
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GET3D
We generate a 3D SDF and a texture field via two latent codes. We utilize DMTet to extract a 3D surface mesh from the SDF and query the texture field at surface points to get colors. We train with adversarial losses defined on 2D images. In particular, we use a rasterization-based differentiable renderer to obtain RGB images and silhouettes. We utilize two 2D discriminators, each on RGB image, and silhouette, respectively, to classify whether the inputs are real or fake. The whole model is end-to-end trainable. As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident. In our work, we aim to train performant 3D generative models that synthesize textured meshes which can be directly consumed by 3D rendering engines, thus immediately usable in downstream applications.
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Magic3D
research.nvidia.com/labs/dir/magic3d/
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