Coherent Online Video Style Transfer Dongdong Chen, Jing Liao, Lu Yuan, Nenghai Yu, Gang Hua |May 2017 Publication Download BibTex Training a feed-forward network for fast neural style transfer of images is proven to be successful. However, the naive extension to process video frame by frame...
Training a feed-forward network for fast neural style transfer of images is proven to be successful. However, the naive extension to process video frame by frame is prone to producing flickering results. We propose the first end-to-end network for online video style transfer, which generates te...
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An unsupervised text style transfer method, system, and computer program product include classifying a style of an input message, translating the input message into a second style, re-writing the input message into a second message having the second style, and distributing the second message in ...
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Based on the ideas from online training, we propose a new coarse-to-fine training strategy for neural style transfer systems to adapt to social preference change. In coarse stage, a primary model is learned via normal training method. In fine stage, the model is updated with online learning ...
we propose a fast and convenient algorithm for human-motion style editing. We define the style of motion as statistic properties of mean and standard variance of joint quaternions in 4D unit sphere space. The proposed algorithm can transfer the style of a motion to another by transferring these...
Style-Transfer-App 开发技术 - 其它Mi**无痛 上传183.78MB 文件格式 zip Java 跑步 python init.py 对于512×512像素的内容文件,在GTX 1080 Ti上进行200次迭代需要5秒,在Maxwell Titan X上进行10秒,在Intel Core i7-5930K上需要10分钟。 强烈建议使用GPU,因为它具有巨大的加速性能。 要求 资料档案(MD5 8ee...
Fourthly, create a single image style transfer modelas described by Johnson et al.(you can also use a pre-trained model if it has the same style). Please remember settings for style and content weight, style image size and other parameters that change the appearance of the stylized image. ...
overcome the challenge of limited parallel data for style transfer. STEER involves automatically generating a corpus of style-transfer pairs using a product of experts during decoding. The generated offline data is then used to pre-train an initial policy before switching to online, off-policy ...