Kaedim vs Text-To-4D

Kaedim is a platform designed to speed up 3D asset creation and team collaboration for professional 3D artists and studios. It uses AI to generate both low-poly and high-poly 3D models quickly, providing starting points that artists can refine. The platform supports generating missing perspectives from reference images and creating tileable textures, helping artists ship assets faster without losing creative control.

Beyond asset generation, Kaedim offers tools to plan and track 3D production pipelines, making it easier for leads and managers to coordinate tasks, assign work, and monitor progress in one place. This helps teams stay organized and meet deadlines more efficiently.

Kaedim targets a range of users including individual artists, small teams, game studios, product designers, e-commerce businesses, and 3D printing companies. Its on-demand platform enables these users to produce production-ready 3D assets up to ten times faster than traditional workflows.

The platform combines proprietary AI models with an in-house team of 3D artists to ensure assets meet professional quality standards. Users can also outsource specific steps of their 3D production to Kaedim’s expert team, blending automation with human expertise.

Kaedim’s user interface supports uploading reference images, managing art direction notes, and tracking asset progress through collaboration features. It offers flexible pricing plans that scale from individuals organizing personal projects to enterprises requiring advanced security, integrations, and dedicated support.

Overall, Kaedim streamlines the 3D production process by accelerating asset creation and improving team coordination, helping studios and artists deliver high-quality 3D content faster and more efficiently.

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.