Beyond Skeletons: Learning Animation Directly from Driving Videos with Same2X Training Strategy
arXiv:2606.06903v1 Announce Type: cross Abstract: Human image animation aims to generate a video from a static reference image, guided by pose information extracted from a driving video. Existing approaches often rely on pose estimators to extract intermediate representations, but such signals are prone to errors under occlusion or complex poses. Building on these observations, we present DirectAnimator, a framework that bypasses pose extraction and directly learns from raw driving videos. We introduce a Driving Cue Triplet consisting of pose, face, and location cues that captures motion, expr
The paper addresses a current limitation in AI-driven human animation, particularly the fragility of pose estimators, and proposes a direct learning approach that improves robustness.
This development could significantly enhance the realism and reliability of AI-generated human animations, impacting various applications from entertainment to virtual assistants and potentially AI agents.
The reliance on intermediate pose extraction steps is reduced, leading to more robust and potentially higher-quality animation synthesis directly from raw video data.
- · AI animation developers
- · Metaverse platforms
- · Virtual content creators
- · Gaming industry
- · Developers of less robust pose estimation models
Improved human image animation quality and efficiency in AI-driven content generation.
Accelerated development of realistic digital humans and AI-powered virtual avatars with more natural movement.
Enhanced immersion and believability in virtual environments and human-AI interaction, potentially blurring the lines between real and simulated.
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Read at arXiv cs.AI