Playable Environments: Video Manipulation in Space and Time
This work addresses the challenge of interactive video synthesis for creative applications, representing a novel method for a known bottleneck in video manipulation.
The paper tackles the problem of interactive video generation and manipulation from a single image by introducing Playable Environments, a framework that allows users to move objects in 3D and control the camera to produce videos based on learned actions, achieving creative applications like playable 3D video generation and stylization.
We present Playable Environments - a new representation for interactive video generation and manipulation in space and time. With a single image at inference time, our novel framework allows the user to move objects in 3D while generating a video by providing a sequence of desired actions. The actions are learnt in an unsupervised manner. The camera can be controlled to get the desired viewpoint. Our method builds an environment state for each frame, which can be manipulated by our proposed action module and decoded back to the image space with volumetric rendering. To support diverse appearances of objects, we extend neural radiance fields with style-based modulation. Our method trains on a collection of various monocular videos requiring only the estimated camera parameters and 2D object locations. To set a challenging benchmark, we introduce two large scale video datasets with significant camera movements. As evidenced by our experiments, playable environments enable several creative applications not attainable by prior video synthesis works, including playable 3D video generation, stylization and manipulation. Further details, code and examples are available at https://willi-menapace.github.io/playable-environments-website