基于感知约束和STN对齐的图像变形
Image Morphing with Perceptual Constraints and STN Alignment
摘要：
在图像变形中，一系列看似合理的帧被合成并合成在一起，以形成给定实例之间的平滑变换。中间体必须忠实于输入，作为集合的成员独立存在，并保持从一个到下一个的节奏良好的视觉过渡。在本文中，我们提出了一个条件GAN变形框架上的一对输入图像操作。该网络被训练成沿着变换合成与时间样本相对应的帧，并学习适当的形状先验，以增强中间帧的可扩展性。虽然单个帧的可扩展性通过对抗性设置来提高，但产生帧序列的特殊训练协议结合感知相似性损失，随着时间的推移促进平滑变换。显式陈述的对应关系被替换为基于网格的自由变形空间Transformer，预测输入之间的几何扭曲，通过使形状初始对齐来实现平滑的几何效果。我们提供了与经典和潜在空间变形技术的比较，并证明了，给定一组用于自我监督的图像，我们的网络学习生成视觉上令人愉悦的变形效果，具有可信的中间效果，对形状和纹理的变化具有鲁棒性，不需要对应的注释。
英文摘要：
In image morphing, a sequence of plausible frames are synthesized and composited together to form a smooth transformation between given instances. Intermediates must remain faithful to the input, stand on their own as members of the set, and maintain a well-paced visual transition from one to the next. In this paper, we propose a conditional GAN morphing framework operating on a pair of input images. The network is trained to synthesize frames corresponding to temporal samples along the transformation, and learns a proper shape prior that enhances the plausibility of intermediate frames. While individual frame plausibility is boosted by the adversarial setup, a special training protocol producing sequences of frames, combined with a perceptual similarity loss, promote smooth transformation over time. Explicit stating of correspondences is replaced with a grid-based freeform deformation spatial transformer that predicts the geometric warp between the inputs, instituting the smooth geometric effect by bringing the shapes into an initial alignment. We provide comparisons to classic as well as latent space morphing techniques, and demonstrate that, given a set of images for self-supervision, our network learns to generate visually pleasing morphing effects featuring believable in-betweens, with robustness to changes in shape and texture, requiring no correspondence annotation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
