AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion
  Transformers
Authors: name: Sherwin Bahmani, name: Ivan Skorokhodov, name: Guocheng Qian, name: Aliaksandr Siarohin, name: Willi Menapace, name: Andrea Tagliasacchi, name: David B. Lindell, name: Sergey Tulyakov

Numerous works have recently integrated 3D camera control into foundational
text-to-video models, but the resulting camera control is often imprecise, and
video generation quality suffers. In this work, we analyze camera motion from a
first principles perspective, uncovering insights that enable precise 3D camera
manipulation without compromising synthesis quality. First, we determine that
motion induced by camera movements in videos is low-frequency in nature. This
motivates us to adjust train and test pose conditioning schedules, accelerating
training convergence while improving visual and motion quality. Then, by
probing the representations of an unconditional video diffusion transformer, we
observe that they implicitly perform camera pose estimation under the hood, and
only a sub-portion of their layers contain the camera information. This
suggested us to limit the injection of camera conditioning to a subset of the
architecture to prevent interference with other video features, leading to 4x
reduction of training parameters, improved training speed and 10% higher visual
quality. Finally, we complement the typical dataset for camera control learning
with a curated dataset of 20K diverse dynamic videos with stationary cameras.
This helps the model disambiguate the difference between camera and scene
motion, and improves the dynamics of generated pose-conditioned videos. We
compound these findings to design the Advanced 3D Camera Control (AC3D)
architecture, the new state-of-the-art model for generative video modeling with
camera control.