ATI：面向可控视频生成的任意轨迹指令

ATI: Any Trajectory Instruction for Controllable Video Generation

May 28, 2025

作者: Angtian Wang, Haibin Huang, Jacob Zhiyuan Fang, Yiding Yang, Chongyang Ma

cs.AI

摘要

我们提出了一种统一的视频生成运动控制框架，该框架通过基于轨迹的输入，无缝整合了摄像机运动、物体级平移以及精细局部运动。与以往采用独立模块或任务特定设计来处理这些运动类型的方法不同，我们的方案通过轻量级运动注入器，将用户定义的轨迹投射至预训练图像到视频生成模型的潜在空间，从而提供了一种连贯的解决方案。用户可通过指定关键点及其运动路径，来控制局部形变、整体物体运动、虚拟摄像机动态或这些元素的组合。注入的轨迹信号引导生成过程，产生时间上一致且语义对齐的运动序列。我们的框架在多种视频运动控制任务中展现了卓越性能，包括风格化运动效果（如运动笔刷）、动态视角变化以及精确的局部运动操控。实验表明，相较于先前方法和商业解决方案，我们的方法在保持与多种先进视频生成骨干广泛兼容的同时，提供了显著更优的可控性和视觉质量。项目页面：https://anytraj.github.io/。

English

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/.