Shap-E

While recent work on text-conditional 3D object generation has shown promising results, the state-of-the-art methods typically require multiple GPU-hours to produce a single sample. This is in stark contrast to state-of-the-art generative image models, which produce samples in a number of seconds or minutes. In this paper, we explore an alternative method for 3D object generation which produces 3D models in only 1-2 minutes on a single GPU. Our method first generates a single synthetic view using a text-to-image diffusion model and then produces a 3D point cloud using a second diffusion model which conditions the generated image. While our method still falls short of the state-of-the-art in terms of sample quality, it is one to two orders of magnitude faster to sample from, offering a practical trade-off for some use cases. We release our pre-trained point cloud diffusion models, as well as evaluation code and models, at this https URL.

Virtuall is a cloud-native, browser-based 3D collaboration workspace engineered for modern studios, creators, and AI-enhanced teams, bringing human and generative AI workflows together in one unified platform. It offers end-to-end asset and project management with tools like task assignment, production tracking, cloud storage, version control, tagging, revision logs, and audit capabilities. Artists can generate 3D models in seconds from sketches, images, text, or multi-view inputs, compare outputs from multiple AI engines, and export high- or low-poly versions for any purpose. Users can render, preview, annotate, share, and inspect models directly in the browser with seamless versioning, Slack and Jira integration, one-click sharing, and full 3D conversion for over 400 format combinations. Built-in collaboration features include real‑time annotations, structured feedback loops, stakeholder reviews, and production performance insights to track pipelines with clarity.