Talking Head Generation Based on 3D Morphable Facial Model

被引:1
|
作者
Shen, Hsin-Yu [1 ]
Tsai, Wen-Jiin [1 ]
机构
[1] Natl Yang Ming Chiao Tung Univ, Dept Comp Sci, Hsinchu, Taiwan
关键词
talking-head generation; 3DMM; image-to-image translation; self-attention; deep learning;
D O I
10.1109/PCS60826.2024.10566437
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a framework for one-shot talking-head video generation which takes a single person image and audio clips as input and synthesizes photo-realistic videos with natural head-poses and lip motion synced to the driving audio. The main idea behind this framework is to use 3D Morphable Model (3DMM) parameters as intermediate representation in generating the videos. We design an Expression Predictor and a Head Pose Predictor to predict facial expression and head-pose parameters from audio, respectively, and adopt a 3DMM model to extract identity and texture parameters from the reference image. With these parameters, facial images are rendered as an auxiliary to guide video generation. Compared to widely used facial landmarks, 3DMM parameters are more powerful in representing facial details. Experimental results show that our method can generate realistic talking-head videos and outperform many state-of-the-art methods.
引用
收藏
页数:5
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