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ByteDance's OmniHuman-1 Turns a Single Image into a Realistic Video

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The scoop:

  • One image is all it takes. AI generates full-motion human videos.
  • Multiple input options. Works with audio video or both.
  • Supports different styles. Portrait half-body full-body formats.
  • Lifelike details. Motion lighting and textures feel real.
  • Handles gestures well. More natural human movements.

ByteDance is at it again, dropping this new tool (well, a paper and demo videos so far) called OmniHuman-1 (quite hyped for OmniHuman-2 now). This one takes just one image and turns it into a lifelike video by using motion cues like audio or video. That means a static photo can suddenly move talk and even change angles like a real person (so including 'deefake' videos obviously).

It works across various size formats—portrait half-body or full-body—and keeps things looking super realistic. The secret? A mixed-training approach that makes use of different motion inputs solving the usual data issues that AI models face.

What People Are Saying

Redditors had a lot to say after seeing OmniHuman-1 in action, while watching Taylor Swift singing:

@GeraltOfRiga: “The hair movement is insane.”

@vinigrae: “I just looked at it again it was so good I didn't even notice how realistic the hair was at first.”

@EternalOptimister: “Based on one image? Oh god! Nice. Where can we try it?”

Some users are already worried about how AI-generated videos will affect trust in media. @WhyIsSocialMedia pointed out how it can now re-render the video from another angle making fake videos harder to detect. Others debated whether video signing systems would help at all.

Albert Einstein Speaking

Albert Einstein the famous theoretical physicist is seen giving a speech on emotions. Again, video form a single image by OmniHuman.

Not Available to the Public Yet

For now OmniHuman-1 isn’t something you can download and use. There’s no pricing info and no official release but the white paper is out for researchers and AI enthusiasts to check out.

Want to see more? You can dig into their research at OMNIHUMAN-LAB.GITHUB.IO.

Last modified 10 February 2025 at 23:15

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