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SuperFace uses human preference to improve facial expression estimation for animation

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed SuperFace, a new framework for estimating facial expressions that moves beyond relying on imperfect software-generated labels. This system uses human preference feedback to refine predictions, aiming for more visually faithful and expressive digital animations. By prioritizing perceptual judgments over pseudo-labels, SuperFace enhances the accuracy of ARKit blendshape coefficient prediction. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Improves realism in digital human animation by aligning facial expression models with human perception.

RANK_REASON This is a research paper detailing a new framework for facial expression estimation.

Read on arXiv cs.CV →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 · Zejian Kang, Xuanyang Xu, Wentao Yang, Kai Zheng, Yuanchen Fei, Hongyuan Zou, Hui Shan, Shuo Yang, Xiangru Huang ·

    SuperFace: Preference-Aligned Facial Expression Estimation Beyond Pseudo Supervision

    arXiv:2605.06179v1 Announce Type: new Abstract: Accurate facial estimation is crucial for realistic digital human animation, and ARKit blendshape coefficients offer an interpretable representation by mapping facial motions to semantic animation controls. However, learning high-qu…

  2. arXiv cs.CV TIER_1 · Xiangru Huang ·

    SuperFace: Preference-Aligned Facial Expression Estimation Beyond Pseudo Supervision

    Accurate facial estimation is crucial for realistic digital human animation, and ARKit blendshape coefficients offer an interpretable representation by mapping facial motions to semantic animation controls. However, learning high-quality ARKit coefficient prediction remains limit…

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