Adaptive Appearance Rendering
This work addresses pose-guided appearance rendering for image and video generation, which is an incremental improvement in computer vision.
The paper tackles the problem of generating images and videos of people with desired appearances and poses by disentangling pose and appearance representations, achieving superior results compared to state-of-the-art methods.
We propose an approach to generate images of people given a desired appearance and pose. Disentangled representations of pose and appearance are necessary to handle the compound variability in the resulting generated images. Hence, we develop an approach based on intermediate representations of poses and appearance: our pose-guided appearance rendering network firstly encodes the targets' poses using an encoder-decoder neural network. Then the targets' appearances are encoded by learning adaptive appearance filters using a fully convolutional network. Finally, these filters are placed in the encoder-decoder neural networks to complete the rendering. We demonstrate that our model can generate images and videos that are superior to state-of-the-art methods, and can handle pose guided appearance rendering in both image and video generation.