In this paper, we study video synthesis with emphasis on simplifying the
generation conditions. Most existing video synthesis models or datasets are
designed to address complex motions of a single object, lacking the ability of
comprehensively understanding the spatio-temporal relationships among multiple
objects. Besides, current methods are usually conditioned on intricate
annotations (e.g. video segmentations) to generate new videos, being
fundamentally less practical. These motivate us to generate multi-object videos
conditioning exclusively on object layouts from a single frame. To solve above
challenges and inspired by recent research on image generation from layouts, we
have proposed a novel video generative framework capable of synthesizing global
scenes with local objects, via implicit neural representations and layout
motion self-inference. Our framework is a non-trivial adaptation from image
generation methods, and is new to this field. In addition, our model has been
evaluated on two widely-used video recognition benchmarks, demonstrating
effectiveness compared to the baseline model.

该论文研究了视频生成，并着重简化生成条件。作者使用单个帧对象布局作为唯一条件，通过隐式神经表示和布局动态自我推理，提出了一种新的视频生成框架，能够合成全局场景和局部对象，实验结果表明，该模型比基准模型更加有效。