Sloyd vs Text-To-4D

Sloyd is a web-based 3D modeling tool that simplifies creating detailed 3D models using AI-driven text and image inputs. It caters to game developers, designers, and 3D printing enthusiasts by offering fast, intuitive model generation without requiring advanced modeling skills. Users can generate unique 3D assets from text prompts or images, customize models with style presets, and control polygon counts to fit specific game or animation needs. The platform combines generative AI with parametric templates, enabling quick iterations and easy editing while ensuring game-ready, optimized topology. Sloyd also supports importing models into platforms like Roblox for rigging and animation, expanding its use for character creation. Its web app and SDK provide flexible access for both hobbyists and professionals, with an API that integrates AI-powered 3D asset generation directly into games or applications. This approach reduces modeling time from hours to seconds, making 3D creation more accessible and efficient.

Text-To-4D, also known as MAV3D (Make-A-Video3D), generates three-dimensional dynamic scenes from simple text descriptions. It uses a 4D dynamic Neural Radiance Field (NeRF) optimized for consistent scene appearance, density, and motion by leveraging a Text-to-Video diffusion model. This allows the creation of dynamic videos that can be viewed from any camera angle and integrated into various 3D environments.

Unlike traditional 3D generation methods, MAV3D does not require any 3D or 4D training data. Instead, it relies on a Text-to-Video model trained solely on text-image pairs and unlabeled videos, making it accessible for users without specialized datasets. This approach opens up new possibilities for creators, developers, and researchers interested in generating immersive 3D dynamic content from text prompts.

The tool is designed for a broad audience including game developers, animators, and virtual reality content creators who want to quickly produce dynamic 3D scenes without manual modeling or animation. It offers a unique value by combining text-driven generation with 3D dynamic scene output, which can be used in interactive applications or visual storytelling.

Technically, the method integrates a 4D NeRF with a diffusion-based Text-to-Video model to ensure motion and appearance consistency over time and space. This results in smooth, realistic dynamic scenes that can be explored from multiple viewpoints. The system improves upon previous internal baselines by producing higher quality and more coherent 3D videos from textual input.

Overall, Text-To-4D stands out as the first known method to generate fully dynamic 3D scenes from text, bridging the gap between text-based video generation and 3D scene synthesis. It offers a flexible and innovative solution for creating immersive content without the need for complex 3D data or manual animation.