DeepMotion vs Text-To-4D

DeepMotion offers AI-powered motion capture and body tracking that lets users create 3D animations from video quickly through any web browser. Its Animate 3D platform converts regular videos into full-body animations without the need for markers or special hardware. This makes motion capture accessible to indie creators, educators, and game developers who want to bring digital characters to life with realistic movement. The platform supports custom characters and integrates with popular 3D formats, enabling easy export for games, VR, and animation projects.

DeepMotion also features MotionGPT, a tool that generates animations from text prompts, expanding creative possibilities for users who want to create motion without filming. The company is building a large AI-generated 3D animation platform focused on democratizing digital human motion creation. Users can earn free animation credits by labeling and correcting animations, which helps improve the AI models.

The service supports facial and hand tracking, with additional credit costs for these features. It offers a Rotoscope Pose Editor for frame-by-frame animation corrections, allowing users to improve motion quality and earn back credits. DeepMotion’s API enables integration into other workflows, speeding up development cycles for partners.

Pricing plans include a freemium tier with monthly free credits for personal use and paid subscriptions offering higher credit limits, priority processing, and unlimited animation creation. The platform supports multiple input video formats and exports animations in FBX, BVH, MP4, and GLB formats. DeepMotion emphasizes ease of use, requiring no special equipment, and provides extensive documentation and community support through Discord.

Overall, DeepMotion stands out by making AI motion capture and animation generation accessible, affordable, and flexible for a wide range of users, from hobbyists to professionals. Its combination of video-based capture, text-to-animation, and credit-earning programs offers a unique ecosystem for creating digital human motion.

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.