MagicAnimate Reviews:A model framework to transform images to dynamic videos
About MagicAnimate
MagicAnimate from ByteDance Inc team allows you to animate a human image following a given motion sequence.Temporally Consistent Human Image Animation using Diffusion Model
TL;DR: We propose MagicAnimate, a diffusion-based human image animation framework that aims at enhancing temporal consistency, preserving reference image faithfully, and improving animation fidelity.
Just as a joke, it’s nice to see that the dancing girls in the picture at least had blurred faces. ? But it’s a really nice one!Congrats on the launch
Video Results
▶ Animating Human Image
MagicAnimate aims at animating the reference image adhering to the motion sequences with temporal consistency.
▶ Qualitative Comparisons
Video resutls for comparisons between MagicAnimate and baselines.
▶ Cross-ID Animation
Comprisons between MagicAnimate and SOTA baselines for cross-ID animation, i.e., aniamting reference images using motion sequences from different videos. We show video results for three identities and two motion sequences.
Motion Sequence 1 | Motion Sequence 2 |
Reference | MRAA* | DisCo | Ours | MRAA* | DisCo | Ours | ||
Applications
▶ Unseen Domain Animation
Animating unseen domain images such as oil painting and movie character to perform running or doing Yoga.
Reference | Motion | Animation | Reference | Motion | Animation |
▶ Combining MagicAnimate with T2I Diffusion Model
Animating reference images generated by DALLE3 to perform various actions. Text prompt for each reference image is shown below each row of the video.
Reference | Motion | Animation | Reference | Motion | Animation |
“A woman doing yoga in the universe, surrounded by supernova.” |
“a man standing on top of a mountain, surrounded by ancient remains.” |
“A woman researcher in the space station.” |
▶ Multi-person Animation
Animating multi-person following the given motion.
Reference | Motion | Animation |
Pipeline
Given a reference image and the target DensePose motion sequence, MagicAnimate employs a video diffusion model and an appearance encoder for temporal modeling and identity preserving, respectively (left panel). To support long video animation, we devise a simple video fusion strategy that produces smooth video transition during inference (right panel).