Sloyd vs Text-To-4D

Sloyd is a revolutionary web application and SDK that makes 3D modeling easier than ever before. With Sloyd, you can seamlessly generate stunning 3D models with just a few clicks. Whether you're a professional designer or an amateur enthusiast, Sloyd provides the tools and flexibility you need to bring your visions to life.

One of the standout features of Sloyd is its wide selection of generators. With these generators, you can quickly create intricate 3D models without having to start from scratch. Simply choose a generator that suits your needs, and then easily tweak it to achieve the desired result. Whether you're looking to design architectural structures, characters for video games, or objects for 3D printing, Sloyd has you covered.

What sets Sloyd apart from other 3D modeling software is its real-time generation capabilities. As you make changes to your model, you can instantly see the results in real-time. This allows for a more intuitive and efficient workflow, as there is no need to constantly wait for renders or previews. With Sloyd, you can make adjustments on the fly and see how they affect your model in an instant.

Sloyd is accessible through both the web app and the SDK. The web app provides a user-friendly interface that allows you to easily navigate and utilize the various features of Sloyd. On the other hand, the SDK empowers developers to integrate the power of Sloyd directly into their own applications or workflows. This flexibility makes Sloyd suitable for professionals, hobbyists, and developers alike.

In conclusion, Sloyd revolutionizes the world of 3D modeling by making it more accessible, efficient, and enjoyable. Its generator options, real-time generation capabilities, and user-friendly interfaces set it apart from other software in the market. Whether you're a seasoned professional or a beginner, Sloyd is the go-to tool for all your 3D modeling needs.

Meta present MAV3D (Make-A-Video3D), a method for generating three-dimensional dynamic scenes from text descriptions. Our approach uses a 4D dynamic Neural Radiance Field (NeRF), which is optimized for scene appearance, density, and motion consistency by querying a Text-to-Video (T2V) diffusion-based model.

The dynamic video output generated from the provided text can be viewed from any camera location and angle, and can be composited into any 3D environment. MAV3D does not require any 3D or 4D data and the T2V model is trained only on Text-Image pairs and unlabeled videos.