多尺度视觉增强语音驱动人脸生成

杨想彦; 梁慧慧; 陈希; 李凡

doi:10.7652/xjtuxb202506017

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多尺度视觉增强语音驱动人脸生成

—————————————————————— | 更新时间：2025-08-26

- 多尺度视觉增强语音驱动人脸生成
- Audio-Driven Talking Face Generation with Multi-Scale Visual Enhancement
- 西安交通大学学报 2025年59卷第6期页码：167-176
- 作者机构：
  
  1.新疆大学计算机科学与技术学院， 830046，乌鲁木齐
  2.西安交通大学电子与信息学部， 710049，西安
- 作者简介：
  
  杨想彦（1998—），女，硕士生；
  李凡，男，教授，博士生导师。
- 基金信息：
  
  国家自然科学基金资助项目(62471376)
- DOI：10.7652/xjtuxb202506017
  中图分类号： TP391
- 收稿：2024-11-09，
  
  网络首发：2025-02-07，
  
  纸质出版：2025-06-10
- 稿件说明：
移动端阅览
杨想彦, 梁慧慧, 陈希, 等. 多尺度视觉增强语音驱动人脸生成[J]. 西安交通大学学报, 2025,59(6):167-176.

YANG Xiangyan, LIANG Huihui, CHEN Xi, et al. Audio-Driven Talking Face Generation with Multi-Scale Visual Enhancement[J]. Journal of Xi’an Jiaotong University, 2025, 59(6): 167-176.
杨想彦, 梁慧慧, 陈希, 等. 多尺度视觉增强语音驱动人脸生成[J]. 西安交通大学学报, 2025,59(6):167-176. DOI： 10.7652/xjtuxb202506017.

YANG Xiangyan, LIANG Huihui, CHEN Xi, et al. Audio-Driven Talking Face Generation with Multi-Scale Visual Enhancement[J]. Journal of Xi’an Jiaotong University, 2025, 59(6): 167-176. DOI： 10.7652/xjtuxb202506017.

摘要

针对现有语音驱动人脸生成方法在生成人脸视频清晰度和真实感方面的不足，设计了一种多尺度视觉增强的端到端人脸视频生成模型——VisClearTalk，并提出包含视觉增强模块的人脸解码器。首先，采用人脸编码器处理包含随机参考帧和下半张脸被遮挡的先验帧，从中提取面部特征；接着，采用语音编码器从音频中提取特征，以指导面部内容生成；随后，采用人脸解码器融合上述特征，并通过卷积模块初步重建面部图像；最后，采用视觉增强模块将多尺度卷积和残差融合，进一步增强人脸下半部分区域的细节和边缘信息，从而提升生成人脸视频的视觉质量。使用公开唇语识别数据集对VisClearTalk模型进行实验验证，结果表明：通过引入视觉增强模块，该模型有效提升了面部视觉内容的细腻程度和真实感，能够生成清晰自然的人脸视频；在性能指标方面，峰值信噪比达到34.349 dB，结构相似度达到0.933，可学习感知图像块相似度降低至0.040。研究结果可为当前人脸生成需求提供一种解决方案。

Abstract

To address the limitations of existing audio-driven talking face generation methods in terms of video clarity and realism

an end-to-end talking face generation method called VisClearTalk which incorporates multi-scale visual enhancement is proposed in this paper

and a face decoder with a visual enhancement module is proposed. First

the face encoder processed a random reference frame and a prior frame with the lower half of the face occluded to extract facial features. Simultaneously

the audio encoder extracted features from the audio to guide facial content generation. Subsequently

the face decoder integrated these features and performed an initial reconstruction of facial images through convolutional modules. Finally

the visual enhancement module employed multi-scale convolution and residual fusion to further enhance the details and edge information of the lower face region

improving the visual quality of the generated talking face videos. The VisClearTalk model was experimentally validated using public lip-reading datasets

with both quantitative and qualitative results demonstrating that the introduction of the visual enhancement module effectively improves the fineness and realism of facial visual content

enabling the generation of clear and natural talking face videos. In terms of performance metrics

the peak signal-to-noise ratio reached 34.349 dB

structural similarity reached 0.933

and learnable perceptual image patch similarity was reduced to 0.040. The VisClearTalk model offers a viable solution for current talking face videos generation needs.

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references

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