Neural Crossbreed: Neural Based Image Metamorphosis
This addresses image morphing for computer graphics and vision applications, offering a novel method that handles objects with different poses or views, but it is incremental as it builds on pre-trained generative models.
The paper tackles the problem of image morphing by proposing Neural Crossbreed, a feed-forward neural network that learns semantic changes in a latent space to generate meaningful intermediate images without requiring explicit correspondences, resulting in high-quality morphed images that overcome limitations of conventional approaches.
We propose Neural Crossbreed, a feed-forward neural network that can learn a semantic change of input images in a latent space to create the morphing effect. Because the network learns a semantic change, a sequence of meaningful intermediate images can be generated without requiring the user to specify explicit correspondences. In addition, the semantic change learning makes it possible to perform the morphing between the images that contain objects with significantly different poses or camera views. Furthermore, just as in conventional morphing techniques, our morphing network can handle shape and appearance transitions separately by disentangling the content and the style transfer for rich usability. We prepare a training dataset for morphing using a pre-trained BigGAN, which generates an intermediate image by interpolating two latent vectors at an intended morphing value. This is the first attempt to address image morphing using a pre-trained generative model in order to learn semantic transformation. The experiments show that Neural Crossbreed produces high quality morphed images, overcoming various limitations associated with conventional approaches. In addition, Neural Crossbreed can be further extended for diverse applications such as multi-image morphing, appearance transfer, and video frame interpolation.