ATI: Any Trajectory Instruction for Controllable Video Generation
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/.
6839f54680cc67601374b80c
Angtian Wang
6839f54680cc67601374b80d
Haibin Huang
6839f54680cc67601374b80e
Jacob Zhiyuan Fang
6839f54680cc67601374b80f
Yiding Yang
6839f54680cc67601374b810
Chongyang Ma
ATI: Any Trajectory Instruction for Controllable Video Generation
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/.
A unified framework for video motion control integrates camera movement, object translation, and local motion via trajectory-based inputs, improving controllability and visual quality.
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