Title: VividCam: Learning Unconventional Camera Motions from Virtual Synthetic Videos
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: controllable video generation, camera control, diffusion models

Abstract: Although recent text-to-video generative models are getting more capable of following external camera controls, imposed by either text descriptions or camera trajectories, they still struggle to generalize to unconventional camera motions, which is crucial in creating truly original and artistic videos. The challenge lies in the difficulty of finding sufficient training videos with the intended uncommon camera motions. To address this challenge, we propose VividCam, a training paradigm that enables diffusion models to learn complex camera motions from synthetic videos, releasing the reliance on collecting realistic training videos. VividCam incorporates multiple disentanglement strategies that isolates camera motion learning from synthetic appearance artifacts, ensuring more robust motion representation and mitigating domain shift. We demonstrate that our design synthesizes a wide range of precisely controlled and complex camera motions using surprisingly simple synthetic data. Notably, this synthetic data often consists of basic geometries within a low-poly 3D scene and can be efficiently rendered by engines like Unity. Our video results can be found in https://anonymoususers196.github.io/VividCamDemo/ .

TL;DR: VividCam learns unconventional camera motions from surprisingly simple synthetic data, producing videos with motions that are often challenging for existing methods.