IEEE Trans Vis Comput Graph - Live Speech Driven Head-and-Eye Motion Generators.

Tópicos

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Resumo

This paper describes a fully automated framework to generate realistic head motion, eye gaze, and eyelid motion simultaneously based on live (or recorded) speech input. Its central idea is to learn separate yet interrelated statistical models for each component (head motion, gaze, or eyelid motion) from a prerecorded facial motion data set: 1) Gaussian Mixture Models and gradient descent optimization algorithm are employed to generate head motion from speech features; 2) Nonlinear Dynamic Canonical Correlation Analysis model is used to synthesize eye gaze from head motion and speech features, and 3) nonnegative linear regression is used to model voluntary eye lid motion and log-normal distribution is used to describe involuntary eye blinks. Several user studies are conducted to evaluate the effectiveness of the proposed speech-driven head and eye motion generator using the well-established paired comparison methodology. Our evaluation results clearly show that this approach can significantly outperform the state-of-the-art head and eye motion generation algorithms. In addition, a novel mocap+video hybrid data acquisition technique is introduced to record high-fidelity head movement, eye gaze, and eyelid motion simultaneously.

Resumo Limpo

paper describ fulli autom framework generat realist head motion eye gaze eyelid motion simultan base live record speech input central idea learn separ yet interrel statist model compon head motion gaze eyelid motion prerecord facial motion data set gaussian mixtur model gradient descent optim algorithm employ generat head motion speech featur nonlinear dynam canon correl analysi model use synthes eye gaze head motion speech featur nonneg linear regress use model voluntari eye lid motion lognorm distribut use describ involuntari eye blink sever user studi conduct evalu effect propos speechdriven head eye motion generat use wellestablish pair comparison methodolog evalu result clear show approach can signific outperform stateoftheart head eye motion generat algorithm addit novel mocapvideo hybrid data acquisit techniqu introduc record highfidel head movement eye gaze eyelid motion simultan

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