title: ATI: Any Trajectory Instruction for Controllable Video Generation
summary: We propose a unified framework for motion control in video generation that
  seamlessly integrates camera movement, object-level translation, and
  fine-grained local motion using trajectory-based inputs. In contrast to prior
  methods that address these motion types through separate modules or
  task-specific designs, our approach offers a cohesive solution by projecting
  user-defined trajectories into the latent space of pre-trained image-to-video
  generation models via a lightweight motion injector. Users can specify
  keypoints and their motion paths to control localized deformations, entire
  object motion, virtual camera dynamics, or combinations of these. The injected
  trajectory signals guide the generative process to produce temporally
  consistent and semantically aligned motion sequences. Our framework
  demonstrates superior performance across multiple video motion control tasks,
  including stylized motion effects (e.g., motion brushes), dynamic viewpoint
  changes, and precise local motion manipulation. Experiments show that our
  method provides significantly better controllability and visual quality
  compared to prior approaches and commercial solutions, while remaining broadly
  compatible with various state-of-the-art video generation backbones. Project
  page: https://anytraj.github.io/.
paper_authors._id: 6839f54680cc67601374b80c
paper_authors.name: Angtian Wang
paper_authors._id: 6839f54680cc67601374b80d
paper_authors.name: Haibin Huang
paper_authors._id: 6839f54680cc67601374b80e
paper_authors.name: Jacob Zhiyuan Fang
paper_authors._id: 6839f54680cc67601374b80f
paper_authors.name: Yiding Yang
paper_authors._id: 6839f54680cc67601374b810
paper_authors.name: Chongyang Ma
paper_title: ATI: Any Trajectory Instruction for Controllable Video Generation
paper_summary: We propose a unified framework for motion control in video generation that
  seamlessly integrates camera movement, object-level translation, and
  fine-grained local motion using trajectory-based inputs. In contrast to prior
  methods that address these motion types through separate modules or
  task-specific designs, our approach offers a cohesive solution by projecting
  user-defined trajectories into the latent space of pre-trained image-to-video
  generation models via a lightweight motion injector. Users can specify
  keypoints and their motion paths to control localized deformations, entire
  object motion, virtual camera dynamics, or combinations of these. The injected
  trajectory signals guide the generative process to produce temporally
  consistent and semantically aligned motion sequences. Our framework
  demonstrates superior performance across multiple video motion control tasks,
  including stylized motion effects (e.g., motion brushes), dynamic viewpoint
  changes, and precise local motion manipulation. Experiments show that our
  method provides significantly better controllability and visual quality
  compared to prior approaches and commercial solutions, while remaining broadly
  compatible with various state-of-the-art video generation backbones. Project
  page: https://anytraj.github.io/.
paper_ai_summary: A unified framework for video motion control integrates camera movement, object translation, and local motion via trajectory-based inputs, improving controllability and visual quality.
paper_ai_keywords: trajectory-based inputs latent space lightweight motion injector keypoints motion paths generative process temporally consistent semantically aligned motion sequences video motion control stylized motion effects dynamic viewpoint changes precise local motion manipulation