  Diffusion model has demonstrated remarkable capability in video generation,
which further sparks interest in introducing trajectory control into the
generation process. While existing works mainly focus on training-based methods
(e.g., conditional adapter), we argue that diffusion model itself allows decent
control over the generated content without requiring any training. In this
study, we introduce a tuning-free framework to achieve trajectory-controllable
video generation, by imposing guidance on both noise construction and attention
computation. Specifically, 1) we first show several instructive phenomenons and
analyze how initial noises influence the motion trajectory of generated
content. 2) Subsequently, we propose FreeTraj, a tuning-free approach that
enables trajectory control by modifying noise sampling and attention
mechanisms. 3) Furthermore, we extend FreeTraj to facilitate longer and larger
video generation with controllable trajectories. Equipped with these designs,
users have the flexibility to provide trajectories manually or opt for
trajectories automatically generated by the LLM trajectory planner. Extensive
experiments validate the efficacy of our approach in enhancing the trajectory
controllability of video diffusion models.
