Scale HUMAN animations with OmniHuman-1 (Technical Review)
Last Updated on February 12, 2025 by Editorial Team
Author(s): Deltan Lobo
Originally published on Towards AI.
Rethinking the Scaling-Up of One-Stage Conditioned Human Animation Models
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Video generation models have been fun till now. We’ve seen several video generation models that create content — especially HUMAN ANIMATIONS.
But those models focus only on either facial expressions or body movement — but not both.
TikTok’s parent company ByteDance bridges this gap by releasing OmniHuman[1]… Now we are able to try out a broader range of animations, from subtle lip-syncing to full-body motion by maintaining consistency in different body proportions.
Of course yeah, traditional models were capable of creating human videos. But as I said it was only limited to a specific body movement and also even if it was created, the characters in the video would look lifeless.
I hope you might be aware of some portrait videos giving explanations about something.
But OmniHuman solves this up to a certain extent. Whether you’re animating a portrait, half-body, or full-body character, this model adapts seamlessly. Supports both talking and singing, handles human-object interactions and challenging body poses, and accommodates different image styles.
Video generation has made huge leaps in recent years… Thanks to diffusion models[2] and transformer architectures[3]. These models work incredibly well for general video generation (like text-to-video systems).
But,… Read the full blog for free on Medium.
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