论文标题
通过基于剩余流量的校正，从未对准来源的姿势引导图像产生
Pose Guided Image Generation from Misaligned Sources via Residual Flow Based Correction
论文作者
论文摘要
由于其广泛的潜在应用，从源图像中生成具有所需属性（例如新的视图/姿势）的新图像（例如，新的视图/姿势）。确保高质量生成的一种方法是使用具有互补信息的多个来源，例如同一对象的不同视图。但是，由于源图像通常由于相机设置之间的较大差异而错过了对准，因此过去对相机或/和对象引起的强烈假设，从而限制了此类技术的应用。因此，我们提出了一种新的通用方法，该方法在统一框架中建模了源之间的多种变化，例如视角，姿势，面部表情，以便可以在截然不同的数据集中使用。我们验证了各种数据的方法，包括人体，面部，城市场景和3D对象。定性和定量结果都表明，与艺术的状态相比，我们方法的性能更好。
Generating new images with desired properties (e.g. new view/poses) from source images has been enthusiastically pursued recently, due to its wide range of potential applications. One way to ensure high-quality generation is to use multiple sources with complementary information such as different views of the same object. However, as source images are often misaligned due to the large disparities among the camera settings, strong assumptions have been made in the past with respect to the camera(s) or/and the object in interest, limiting the application of such techniques. Therefore, we propose a new general approach which models multiple types of variations among sources, such as view angles, poses, facial expressions, in a unified framework, so that it can be employed on datasets of vastly different nature. We verify our approach on a variety of data including humans bodies, faces, city scenes and 3D objects. Both the qualitative and quantitative results demonstrate the better performance of our method than the state of the art.
