AI Models for 3D Artists
Creating 3D models from text or images now takes minutes. With tools like Meshy, Tripo AI, Luma Genie, Spline AI, and Kaedim you can generate text-to-3D models, and with open-source models such as Point-E, Shap-E, and TRELLIS you can build your own pipeline. Curated for game developers, 3D artists, architectural designers, and AR/VR content creators, this collection comprehensively covers tools with FBX, OBJ, and GLB export support and automatic texture generation capabilities.
Tools
Models
Point-E
Point-E is a 3D generation system developed by OpenAI that produces colored 3D point clouds from text descriptions through a two-stage cascading approach. Released in December 2022, it was one of the first publicly available text-to-3D models from a major AI lab. The system works in two stages: first, a text-conditioned DALL-E-based image generation model creates a synthetic view of the described object, then a second diffusion model generates a 3D point cloud conditioned on that image. This cascading design produces results in just one to two minutes on a single GPU, dramatically faster than optimization-based methods like DreamFusion which require hours of processing. The generated point clouds consist of thousands of colored points representing the 3D shape and appearance of objects. While point clouds are less immediately usable than meshes for production 3D applications, they can be converted to meshes through standard reconstruction algorithms like Poisson surface reconstruction. Point-E supports generation of a wide variety of objects including animals, vehicles, furniture, and everyday items. The model is fully open source under the MIT license with code and pre-trained weights available on GitHub. As a pioneering early contribution to fast text-to-3D generation, Point-E demonstrated that trading some quality for dramatically improved speed was a viable approach, directly influencing the development of subsequent models like Shap-E. The system remains valuable for researchers exploring 3D generation pipelines and for rapid concept visualization where speed matters more than production-ready quality.
Shap-E
Shap-E is a 3D generation model developed by OpenAI that creates 3D objects directly from text descriptions or input images by generating the parameters of implicit neural representations. Unlike its predecessor Point-E which produces point clouds, Shap-E generates Neural Radiance Fields (NeRF) and textured meshes that can be directly rendered and used in 3D applications. The model employs a two-stage training approach where an encoder first learns to map 3D assets to implicit function parameters, then a conditional diffusion model learns to generate those parameters from text or image inputs. This architecture enables fast generation times of just a few seconds on a modern GPU. Shap-E supports both text-to-3D and image-to-3D workflows, making it versatile for different creative pipelines. The generated 3D objects include color and texture information, producing more complete results than geometry-only approaches. Released under the MIT license in May 2023, the model is fully open source with pre-trained weights available on GitHub. While the output quality may not match optimization-heavy methods like DreamFusion that take minutes per object, Shap-E offers a practical balance between speed and quality for rapid prototyping and concept exploration. The model is particularly useful for game developers, 3D artists, and researchers who need quick 3D visualizations from text prompts. As one of OpenAI's contributions to open-source 3D AI research, Shap-E has influenced subsequent work in fast feed-forward 3D generation approaches.
TRELLIS
TRELLIS is a revolutionary AI model developed by Microsoft Research that generates high-quality 3D assets from text descriptions or single 2D images using a novel Structured Latent Diffusion architecture. Released in December 2024, TRELLIS represents a fundamental advancement in 3D content generation by operating in a structured latent space that encodes geometry, texture, and material properties simultaneously rather than treating them as separate stages. The model produces complete 3D meshes with detailed PBR (Physically Based Rendering) textures, enabling direct use in game engines, 3D rendering pipelines, and AR/VR applications without extensive manual post-processing. TRELLIS supports both text-to-3D generation where users describe desired objects in natural language and image-to-3D reconstruction where a single photograph is converted into a full 3D model with inferred geometry from occluded viewpoints. The structured latent representation ensures geometric consistency and prevents the common artifacts seen in other 3D generation approaches such as floating geometry, texture seams, and unrealistic proportions. TRELLIS outputs standard 3D formats including GLB and OBJ with UV-mapped textures, making integration with professional tools like Blender, Unity, and Unreal Engine straightforward. Released under the MIT license, the model is fully open source and available on GitHub. Key applications include rapid 3D asset prototyping for game development, architectural visualization, product design mockups, virtual staging for real estate, educational 3D content creation, and metaverse asset generation. The model particularly benefits indie developers and small studios who lack resources for traditional 3D modeling workflows.