Markerless multi-camera head tracking achieves 2.32 mm and 2.01° median accuracy versus marker-based systems in 50 subjects, sufficient for transcranial magnetic stimulation.
Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
abstract
We present an end-to-end neural network-based model for inferring an approximate 3D mesh representation of a human face from single camera input for AR applications. The relatively dense mesh model of 468 vertices is well-suited for face-based AR effects. The proposed model demonstrates super-realtime inference speed on mobile GPUs (100-1000+ FPS, depending on the device and model variant) and a high prediction quality that is comparable to the variance in manual annotations of the same image.
years
2026 4representative citing papers
ID-ControlNet conditions latent diffusion models on facial identity embeddings and uses consistency losses to improve identity preservation in face inpainting.
Deep learning models analyzing temporal facial expressions and head movements in interview videos explained 91% and 84% of variance in self-reported honest and deceptive impression management, outperforming human interviewers' correlations with the same self-reports.
Facial emotion embeddings improve short-term pose forecasting accuracy for emotion-driven motions when fused via normalized gating in a lightweight LSTM world model, but not with simple multimodal fusion.
citing papers explorer
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Markerless Head Tracking for Accurate and Accessible Neuronavigation
Markerless multi-camera head tracking achieves 2.32 mm and 2.01° median accuracy versus marker-based systems in 50 subjects, sufficient for transcranial magnetic stimulation.
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Face inpainting with Identity Preserving Latent Diffusion Models
ID-ControlNet conditions latent diffusion models on facial identity embeddings and uses consistency losses to improve identity preservation in face inpainting.
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Artificial Intelligence can Recognize Whether a Job Applicant is Selling and/or Lying According to Facial Expressions and Head Movements Much More Correctly Than Human Interviewers
Deep learning models analyzing temporal facial expressions and head movements in interview videos explained 91% and 84% of variance in self-reported honest and deceptive impression management, outperforming human interviewers' correlations with the same self-reports.
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Emotion-Conditioned Short-Horizon Human Pose Forecasting with a Lightweight Predictive World Model
Facial emotion embeddings improve short-term pose forecasting accuracy for emotion-driven motions when fused via normalized gating in a lightweight LSTM world model, but not with simple multimodal fusion.