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Attention Mesh: High-fidelity Face Mesh Prediction in Real-time

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arxiv 2006.10962 v1 pith:GO53ST7I submitted 2020-06-19 cs.CV

classification cs.CV
keywords meshattentionarchitecturefacelandmarksnetworkpredictionreal-time
verification ladder T0 review T1 audit T2 compute T3 formal
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We present Attention Mesh, a lightweight architecture for 3D face mesh prediction that uses attention to semantically meaningful regions. Our neural network is designed for real-time on-device inference and runs at over 50 FPS on a Pixel 2 phone. Our solution enables applications like AR makeup, eye tracking and AR puppeteering that rely on highly accurate landmarks for eye and lips regions. Our main contribution is a unified network architecture that achieves the same accuracy on facial landmarks as a multi-stage cascaded approach, while being 30 percent faster.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. JOLT3D: Joint Learning of Talking Heads and 3DMM Parameters with Application to Lip-Sync

    cs.CV 2025-07 conditional novelty 6.0 of 10

    JOLT3D jointly trains a 3DMM reconstruction network with a talking head generator, then uses FACS mouth blendshapes from a diffusion model to lip-sync videos while preserving the original chin contour.

  2. OPEN: A Benchmark Dataset and Baseline for Older Adult Patient Engagement Recognition in Virtual Rehabilitation Learning Environments

    cs.CV 2025-07 conditional novelty 6.0 of 10

    OPEN releases landmark and feature data from 35 hours of older adult virtual rehab sessions with engagement, affect, behavior, and context annotations, plus baselines reaching up to 81% accuracy.

  3. Towards High-Fidelity, Identity-Preserving Real-Time Makeup Transfer: Decoupling Style Generation

    cs.CV 2025-09 reject novelty 5.0 of 10

    A decoupled makeup-transfer pipeline with synthetic pseudo-ground-truth training achieves impressive numbers, but the evaluation is circular and code is not released.

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