基于Python的可变形深度人脸特征插值编解码网络.zip

上传者: sheziqiong | 上传时间: 2022-07-05 09:04:11 | 文件大小: 4.11MB | 文件类型: ZIP
资源包含文件:课程报告word+项目源码 人脸属性编辑是一个具有挑战性的图像处理任务,使用传统的图像处理软件手工编辑,操作成本高。深度特征插值方法是通过深度学习将图像空间非线性的语义映射到隐空间线性特征再进行语义属性编辑的技术。该方法通用性较强,不需要设计特定网络结构,图像处理的速度较快,效果也比较好。遗憾的是,该方法由于难以将特征完全解耦,导致线性插值之后生成图像模糊或者存在伪影。 详细介绍参考:https://blog.csdn.net/sheziqiong/article/details/125598963

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( 63 个子文件 4.11MB ) 基于Python的可变形深度人脸特征插值编解码网络.zip
paper.pdf 962.03KB
visualize.py 2.55KB
课程论文.docx 654.33KB
纹理.png 75.91KB
dependency
age_getter.py 2.07KB
smile 91.71KB
surprise 91.71KB
__init__.py 0B
age 91.71KB
face_getter.py 1.14KB
fear 91.71KB
mustache_getter.py 2.26KB
mustache 91.71KB
angry 91.71KB
sad 91.71KB
expression_getter.py 3.60KB
disgust 91.71KB
img
11.png 204.17KB
16.png 79.50KB
0607.png 138.99KB
0910.png 128.82KB
1.png 108.68KB
635.png 66.64KB
14.png 19.45KB
2.png 267B
12.png 111.62KB
05.png 26.59KB
4.png 212B
13.png 118.81KB
3.png 18.08KB
08.png 104.56KB
interpolator.py 4.82KB
tools
utils.py 18.00KB
DatasetLoader.py 2.48KB
__init__.py 0B
DatasetUtil.py 1.82KB
model
DenseNet.py 5.07KB
Loss.py 2.80KB
UnetDAE.py 15.53KB
__init__.py 0B
重建.png 68.02KB
LICENSE 1.05KB
trainer.py 5.91KB
.gitignore 1.77KB
haha.png 582.38KB
demo
improve 20B
rects.png 29.10KB
evaluation.py 7.71KB
age_interpolation.py 4.33KB
age.txt 441B
mustache.txt 274B
hist.png 23.31KB
origin_scores 38.92KB
reconstruct.py 1.22KB
mustache_interpolation.py 3.40KB
origin 23B
expression_interpolation.py 5.67KB
improve_scores 40.33KB
README.md 27.16KB
setting
parameter.py 3.65KB
__init__.py 0B
train_option.py 378B
ԭͼ.png 67.04KB
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