MagicAnimate

MagicAnimate is an open-source diffusion-based framework for human image animation. It produces temporally consistent animated videos from a single reference image and a motion video. The tool maintains high temporal consistency, faithfully preserves the reference image, and enhances animation fidelity. It supports cross-ID animations and can animate unseen domains such as oil paintings and movie characters. MagicAnimate integrates with text-to-image diffusion models like DALLE3 to bring text-prompted images to life. Built by Show Lab at National University of Singapore and Bytedance, it offers the highest consistency among current dance video solutions.

Key Features

• Temporal Consistency: Maintains smooth motion across frames.
• Reference Image Preservation: Faithfully retains the appearance of the input image.
• Cross-ID Animation: Animate different identities with the same motion sequence.
• Domain Generalization: Works on anime, oil paintings, and movie characters.
• T2I Integration: Seamlessly works with models like DALLE3.

Use Cases

• Dance video generation from a single photo.
• Animating characters from various art styles.
• Creating motion sequences for virtual avatars.

Who It’s For

Researchers, developers, and AI artists interested in human animation and video generation.

Workflow

1. Provide a reference image and a motion video (or OpenPose keypoints).
2. The model generates an animated video maintaining the reference appearance.
3. Optionally, use OpenPose to convert any video into motion data.

Requirements

• Python >= 3.8, CUDA >= 11.3, ffmpeg.
• Pretrained StableDiffusion V1.5 and MSE-finetuned VAE models.
• MagicAnimate checkpoints from HuggingFace.