Live Portrait
Efficient and Controllable Video-Driven Portrait Animation
AI-driven motion capture and 3D character animation for the spatial computing era.
DeepMotion is a pioneer in the field of markerless motion capture and real-time 3D body tracking, utilizing a sophisticated Neural Motion Engine to bridge the gap between physical movement and digital avatars. By 2026, DeepMotion has solidified its position in the market by offering a browser-based, hardware-agnostic platform that translates 2D video inputs into high-fidelity 3D animations (FBX, GLB, BVH) with advanced physics-based constraints. Its technical architecture leverages deep learning models to handle complex skeletal retargeting, multi-person tracking, and intricate hand/face articulation without the need for traditional mocap suits or specialized sensors. This democratization of high-end animation workflows makes it a critical tool for indie game developers, digital marketers, and Metaverse architects. The platform's 2026 iteration includes enhanced 'SayMotion' capabilities, allowing users to generate complex procedural animations through natural language prompts, and a more robust API for real-time integration into engines like Unreal Engine 5.4+ and Unity. By prioritizing accessible AI-driven rigging and motion synthesis, DeepMotion reduces production time for character-based content by up to 80% compared to traditional manual keyframing.
Generative AI model that synthesizes 3D skeletal movement from natural language descriptions.
Efficient and Controllable Video-Driven Portrait Animation
Turn 2D images and videos into immersive 3D spatial content with advanced depth-mapping AI.
High-Quality Video Generation via Cascaded Latent Diffusion Models
The ultimate AI creative lab for audio-reactive video generation and motion storytelling.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Algorithms that detect ground contact points to eliminate the 'sliding' effect common in AI mocap.
Direct integration with Ready Player Me (RMP) avatars for instant rigging and deployment.
Mono-camera depth estimation for finger articulation with 24 degrees of freedom.
Deep learning models capable of segmenting and tracking multiple skeletal structures in a single video feed.
Supports 52 ARKit-compatible blendshapes for emotional expression.
Parallel processing of video files via AWS-backed GPU clusters.
Lack of budget for expensive mocap suits for minor character animations.
Registry Updated:2/7/2026
Complex hardware requirements for real-time 3D streaming.
Difficulty in quantifying biomechanical movements without sensors.