ReCapture: Generative Video Camera Controls for User-Provided Videos
  using Masked Video Fine-Tuning
Authors: name: David Junhao Zhang, name: Roni Paiss, name: Shiran Zada, name: Nikhil Karnad, name: David E. Jacobs, name: Yael Pritch, name: Inbar Mosseri, name: Mike Zheng Shou, name: Neal Wadhwa, name: Nataniel Ruiz

Recently, breakthroughs in video modeling have allowed for controllable
camera trajectories in generated videos. However, these methods cannot be
directly applied to user-provided videos that are not generated by a video
model. In this paper, we present ReCapture, a method for generating new videos
with novel camera trajectories from a single user-provided video. Our method
allows us to re-generate the reference video, with all its existing scene
motion, from vastly different angles and with cinematic camera motion. Notably,
using our method we can also plausibly hallucinate parts of the scene that were
not observable in the reference video. Our method works by (1) generating a
noisy anchor video with a new camera trajectory using multiview diffusion
models or depth-based point cloud rendering and then (2) regenerating the
anchor video into a clean and temporally consistent reangled video using our
proposed masked video fine-tuning technique.