Towards Accurate Generative Models of Video: A New Metric & Challenges

Understand

Recent advances in deep generative models have lead to remarkable progress in synthesizing high quality images.

• Following their successful application in image processing and representation learning, an important next step is to consider videos.
• Learning generative models of video is a much harder task, requiring a model to capture the temporal dynamics of a scene, in addition to the visual presentation of objects.
• While recent attempts at formulating generative models of video have had some success, current progress is hampered by (1) the lack of qualitative metrics that consider visual quality, temporal coherence, and diversity of samples, and (2) the wide gap between purely synthetic video data sets and challenging real-world data sets in terms of complexity.