标签归档:GANs

HumanGAN: A Generative Model of Humans Images

Generative adversarial networks achieve great performance in photorealistic image synthesis in various domains, including human images. However, they usually employ latent vectors that encode the sampled outputs globally. This does not allow convenient control of semantically-relevant individual parts of the image, and is not able to draw samples that only differ in partial aspects, such as clothing style. We address these limitations and present a generative model for images of dressed humans offering control over pose, local body part appearance and garment style. This is the first method to solve various aspects of human image generation such as global appearance sampling, pose transfer, parts and garment transfer, and parts sampling jointly in a unified framework. As our model encodes part-based latent appearance vectors in a normalized pose-independent space and warps them to different poses, it preserves body and clothing appearance under varying posture. Experiments show that our flexible and general generative method outperforms task-specific baselines for pose-conditioned image generation, pose transfer and part sampling in terms of realism and output resolution.

https://arxiv.org/abs/2103.06902

生成对抗网络将图像生成拓展许多应用中并取得了良好的反响。但是,它们往往使用隐矢量对采样输出进行编码,这使得对于独立部分的编辑工作变得很不方便,也无法控制部分单独变量例如服饰的风格。我们通过提出一个新的生成模型来解决这个问题,我们提出的模型可以控制姿态,局部身体部位以及服装风格。这是第一个从多方面解决人体图像生成的方法,它由全局外观采样,姿态转移,部位和服饰转移,以及部位联合采样几个部分组成。当我们的编码器编码完成隐外观向量到一个标准化的姿态无关的空间之后我们将它映射到不同的姿态,这不会影响身体和服饰的外观。实验表明我们的模型在条件图像生成,姿态转移以及部分采样等任务中获得了优异的性能。

Training Generative Adversarial Networks in One Stage

Generative Adversarial Networks (GANs) have demonstrated unprecedented success in various image generation tasks. The encouraging results, however, come at the price of a cumbersome training process, during which the generator and discriminator are alternately updated in two stages. In this paper, we investigate a general training scheme that enables training GANs efficiently in only one stage. Based on the adversarial losses of the generator and discriminator, we categorize GANs into two classes, Symmetric GANs and Asymmetric GANs, and introduce a novel gradient decomposition method to unify the two, allowing us to train both classes in one stage and hence alleviate the training effort. Computational analysis and experimental results on several datasets and various network architectures demonstrate that, the proposed one-stage training scheme yields a solid 1.5× acceleration over conventional training schemes, regardless of the network architectures of the generator and discriminator. Furthermore, we show that the proposed method is readily applicable to other adversarial-training scenarios, such as data-free knowledge distillation.

https://arxiv.org/pdf/2103.00430.pdf

生成对抗网络(GANs)已经在不同的图像生成任务中展示了它史无前例的成功。但是这样的成功是来自于复杂的训练流程,这样的训练流程由生成器和判别器通过两阶段交替更新完成。在本文中,我们提出了一种GANs单步训练流程。根据对抗损失类型分类,我们把GANs分成对称GANs和非对称GANs两种,同时我们还提出了一种新的梯度分解方法去统一两种GANs使得我们可以在单步内完成训练。计算量分析和实验结果表明单步训练的GANs可以得到1.5倍的加速。