爱可可AI论文推介(10月14日) LG-机器学习CV-计算机视觉CL-计算与语

LG - 机器学习 CV - 计算机视觉 CL - 计算与语言
1、[CV]Resolution Dependant GAN Interpolation for Controllable Image Synthesis Between Domains
J N. M. Pinkney, D Adler
用依赖分辨率的GAN插值实现域间可控图像合成，以依赖分辨率的方式在StyleGAN架构生成模型间进行插值，可以从全新领域生成图像，并在一定程度上控制输出的性质，实现一定程度上艺术创作方向的控制。
GANs can generate photo-realistic images from the domain of their training data. However, those wanting to use them for creative purposes often want to generate imagery from a truly novel domain, a task which GANs are inherently unable to do. It is also desirable to have a level of control so that there is a degree of artistic direction rather than purely curation of random results. Here we present a method for interpolating between generative models of the StyleGAN architecture in a resolution dependant manner. This allows us to generate images from an entirely novel domain and do this with a degree of control over the nature of the output.
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2、[CV]High-Fidelity 3D Digital Human Creation from RGB-D Selfies
X Lin, Y Chen, L Bao, H Zhang, S Wang, X Zhe, X Jiang, J Wang, D Yu, Z Zhang
[Tencent AI Labit should have a shading field that, outside the inserted fragment, is the same as the target scene's shading field; and composite surface normals are consistent with the final rendering's shading field. The result is a simple procedure that produces convincing and realistic shading. Moreover, our procedure does not require rendered images or image-decomposition from real images in the training or labeled annotations. In fact, our only use of simulated ground truth is our use of a pre-trained normal estimator. Qualitative results are strong, supported by a user study comparing against the state-of-the-art image harmonization baseline.
【爱可可AI论文推介(10月14日)】
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