Live Portrait
Efficient and Controllable Video-Driven Portrait Animation

Advanced Diffusion-Based Human Image Animation with Temporal Consistency.
MagicAnimate, developed by ByteDance's Show Lab, represents a significant breakthrough in the temporal consistency of AI-generated human animation. In the 2026 market landscape, it serves as a foundational open-source architecture for high-fidelity character motion. The framework utilizes a novel diffusion-based pipeline that integrates an Appearance Encoder to preserve the intricate details of a reference image, and a Video ControlNet to precisely map motion sequences (DensePose) onto that subject. Unlike earlier models that suffered from flickering or identity drifting, MagicAnimate ensures the subject's features remain stable across long-form video generations. It is designed to work with Stable Diffusion backbones, allowing for wide compatibility with the generative AI ecosystem. As of 2026, the model is extensively used in virtual try-on solutions, digital twin animation, and decentralized content creation pipelines where high identity preservation is non-negotiable. Its architecture allows for zero-shot animation, meaning it can animate any person in any style from a single image without requiring fine-tuning for specific subjects.
Uses a dedicated UNet-based encoder to extract high-level semantic and low-level structural features from the reference image.
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.
Integrates DensePose sequences to provide high-fidelity anatomical control over character limbs and torso.
Custom temporal cross-attention layers inserted between spatial layers to ensure frame-to-frame smoothness.
Trained on massive datasets to handle unseen subjects without per-image fine-tuning.
Supports upscaling to 512x512 and 1024x1024 resolutions using tile-based diffusion processes.
Allows swapping of base SD models to change the artistic style (e.g., anime, photorealistic).
Built-in compatibility with RIFE and other interpolation models for 60FPS output.
Creating marketing videos for clothing without expensive model shoots.
Registry Updated:2/7/2026
Animating 2D concept art into in-game sprites or cutscenes.
Making static photos perform viral dance trends.