The model takes a single 2D image of an object and generates a photorealistic image of that same object from a different, user-specified camera angle.
Uses a diffusion-based generative AI architecture that is conditioned on both the input image and a relative camera pose, enabling controlled generation of high-quality outputs.
Can generate a sequence of images from multiple viewpoints around the object, which are geometrically consistent with each other.
The full codebase, model weights, and training datasets are publicly released, allowing for full transparency, replication, and modification.
Serves as a critical first step in a pipeline that converts 2D images into usable 3D assets for games, VR/AR, and digital twins.
Industrial designers and concept artists can take a single sketch or photo of a new product concept and use Zero-1-to-3 to quickly generate a turntable of views. This provides a 3D-like visualization for early-stage reviews and presentations without needing to build a detailed 3D CAD model from scratch, accelerating the iteration cycle and stakeholder feedback.
Online retailers can use the model to create interactive 3D views of products from existing catalog photography. By generating a set of consistent views around an item, they can feed these into a 3D reconstruction tool to create a spin model, improving customer engagement and potentially reducing return rates by giving a better sense of the product's form.
Researchers training computer vision models for robotics (like object manipulation or navigation) often need vast amounts of labeled 3D data. Zero-1-to-3 can synthesize novel viewpoints of objects from limited real-world images, creating diverse training data that improves a model's robustness to different perspectives and lighting conditions, reducing data collection costs.
Indie game developers and digital artists can transform reference images or concept art into base 3D models. By generating multiple consistent views of a character, prop, or environment asset, they provide the necessary input for photogrammetry-style 3D reconstruction pipelines, speeding up asset production for games, VR experiences, and virtual worlds.
Museums and archaeologists can create digital 3D records of artifacts from historical photographs where only one angle is available. The model can hypothesize the object's appearance from other sides, aiding in digital restoration, scholarly analysis, and the creation of virtual museum exhibits that allow online visitors to examine items from all angles.
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15Five operates in the people analytics and employee experience space, where platforms aggregate HR and feedback data to give organizations insight into their workforce. These tools typically support engagement surveys, performance or goal tracking, and dashboards that help leaders interpret trends. They are intended to augment HR and management decisions, not to replace professional judgment or context. For specific information about 15Five's metrics, integrations, and privacy safeguards, you should refer to the vendor resources published at https://www.15five.com.
20-20 Technologies is a comprehensive interior design and space planning software platform primarily serving kitchen and bath designers, furniture retailers, and interior design professionals. The company provides specialized tools for creating detailed 3D visualizations, generating accurate quotes, managing projects, and streamlining the entire design-to-sales workflow. Their software enables designers to create photorealistic renderings, produce precise floor plans, and automatically generate material lists and pricing. The platform integrates with manufacturer catalogs, allowing users to access up-to-date product information and specifications. 20-20 Technologies focuses on bridging the gap between design creativity and practical business needs, helping professionals present compelling visual proposals while maintaining accurate costing and project management. The software is particularly strong in the kitchen and bath industry, where precision measurements and material specifications are critical. Users range from independent designers to large retail chains and manufacturing companies seeking to improve their design presentation capabilities and sales processes.
3D Generative Adversarial Network (3D-GAN) is a pioneering research project and framework for generating three-dimensional objects using Generative Adversarial Networks. Developed primarily in academia, it represents a significant advancement in unsupervised learning for 3D data synthesis. The tool learns to create volumetric 3D models from 2D image datasets, enabling the generation of novel, realistic 3D shapes such as furniture, vehicles, and basic structures without explicit 3D supervision. It is used by researchers, computer vision scientists, and developers exploring 3D content creation, synthetic data generation for robotics and autonomous systems, and advancements in geometric deep learning. The project demonstrates how adversarial training can be applied to 3D convolutional networks, producing high-quality voxel-based outputs. It serves as a foundational reference implementation for subsequent work in 3D generative AI, often cited in papers exploring 3D shape completion, single-view reconstruction, and neural scene representation. While not a commercial product with a polished UI, it provides code and models for the research community to build upon.
