Immersity (formerly LeiaPix Converter) vs Text-To-4D

Immersity, formerly LeiaPix Converter, turns flat photos and videos into depth animations you can view on a normal screen or XR headset. You upload a 2D file, the Neural Depth Engine builds a depth map, and you export formats like side-by-side 3D, depth maps, spatial video for Apple Vision Pro, or animated Immersive Motion clips.

Generic 3D photo apps often stop at a single parallax wiggle. Immersity separates foreground and background into distinct layers with Neural Depth Rendering 4.0, which fills in obscured details when you pan or zoom the viewpoint. It also converts video, not just stills, with export options up to 4K on paid plans.

Creators, marketers, and XR hobbyists use it for social clips, travel footage, and stereoscopic content without building depth maps by hand. The free tier allows unlimited watermarked conversions up to 720p; paid image plans start at $4.99 per month for 500 credits and 4K output without watermarks.

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.