Layered Neural Rendering for Retiming People in Video
This addresses video editing challenges for creators and professionals by allowing precise temporal manipulation of people in real-world scenes, though it is incremental as it builds on layered representations.
The paper tackles the problem of retiming people in natural videos, enabling temporal alignment, speed changes, or removal of individuals, and achieves realistic, high-quality renderings for complex actions like dancing and group running.
We present a method for retiming people in an ordinary, natural video -- manipulating and editing the time in which different motions of individuals in the video occur. We can temporally align different motions, change the speed of certain actions (speeding up/slowing down, or entirely "freezing" people), or "erase" selected people from the video altogether. We achieve these effects computationally via a dedicated learning-based layered video representation, where each frame in the video is decomposed into separate RGBA layers, representing the appearance of different people in the video. A key property of our model is that it not only disentangles the direct motions of each person in the input video, but also correlates each person automatically with the scene changes they generate -- e.g., shadows, reflections, and motion of loose clothing. The layers can be individually retimed and recombined into a new video, allowing us to achieve realistic, high-quality renderings of retiming effects for real-world videos depicting complex actions and involving multiple individuals, including dancing, trampoline jumping, or group running.