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SuperFace uses human preference to improve facial expression estimation for animation

By PulseAugur Editorial · [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

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 →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

SuperFace uses human preference to improve facial expression estimation for animation

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This is a research paper detailing a new framework for facial expression estimation.
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154 days old
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · 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 English(EN) · 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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