The creation of an image from another and from different types of data including text, scene
graph, and object layout, is one of the very challenging tasks in computer vision. In addition …

With the increasing interest in the content creation field in multiple sectors such as media,
education, and entertainment, there is an increased trend in the papers that use AI …

Conditional image-to-video (cI2V) generation aims to synthesize a new plausible video
starting from an image (eg, a person's face) and a condition (eg, an action class label like …

The performance of video prediction has been greatly boosted by advanced deep neural
networks. However, most of the current methods suffer from large model sizes and require …

Video-to-video synthesis (vid2vid) aims at converting an input semantic video, such as
videos of human poses or segmentation masks, to an output photorealistic video. While the …

Due to the remarkable progress of deep generative models, animating images has become
increasingly efficient, whereas associated results have become increasingly realistic …

Few-shot learning (FSL) aims to learn new categories with a few visual samples per class.
Few-shot class representations are often biased due to data scarcity. To mitigate this issue …

Video-to-video synthesis (vid2vid) aims for converting high-level semantic inputs to
photorealistic videos. While existing vid2vid methods can achieve short-term temporal …

This presentation introduces a self-supervised learning approach to the synthesis of new
videos clips from old ones, with several new key elements for improved spatial resolution …

The video generation task can be formulated as a prediction of future video frames given
some past frames. Recent generative models for videos face the problem of high …