ATI: Any Trajectory Instruction for Controllable Video Generation (2505.22944v3)

Abstract: 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/.

Analysis of "ATI: Any Trajectory Instruction for Controllable Video Generation"

The paper "ATI: Any Trajectory Instruction for Controllable Video Generation" proposes a novel framework designed to address the complexities associated with motion control in video generation. Traditionally, systems dealing with video generation have been limited by the need for separate modules to manage different types of motion—whether it be camera movement, object translation, or localized motion. Such fragmentation leads to inconsistent outcomes and fragmented workflows, posing significant challenges to user-driven video synthesis. ATI proposes a unified framework that uses trajectory-based inputs to simplify these operations and provide superior control over video generation processes.

Key Contributions

The paper outlines several primary contributions that differentiate the ATI framework:

1. Unified Trajectory-Based Framework: By representing diverse motion effects as trajectories, the framework facilitates a cohesive approach to video generation. Users can define motion paths using keypoints, which are projected into the latent space of pre-trained image-to-video generation models. This uniform method helps in achieving temporally consistent and semantically aligned motion sequences.
2. Motion Injector Module: ATI introduces a lightweight motion injector that processes trajectory information to guide the generative processes. This module effectively integrates user-defined trajectory controls into the latent space of existing video generation architectures without necessitating retraining of the models, thereby ensuring adaptability across various systems.
3. Enhanced Performance and Compatibility: Through rigorous evaluation, ATI demonstrates improved controllability and visual quality compared to existing methods. It excels in synchronizing camera and object motion, outperforming both academic approaches and commercial solutions. It also highlights compatibility with multiple state-of-the-art video generation backbones.
4. Tailored Regularization Techniques: The paper introduces the Tail Dropout Regularizer, which addresses issues related to premature trajectory termination in the synthesized videos. This technique encourages the model to differentiate between actual occlusions and natural trajectory conclusions, preventing unwanted visual disruptions.

Experimental Results

The experiments conducted demonstrate significant improvement in video generation quality when utilizing the ATI framework. Quantitative metrics such as [email protected] and [email protected] reveal high accuracy rates in tracking user-specified trajectories, with the framework significantly reducing error distances compared to other approaches. The qualitative results further illustrate ATI’s capabilities in coherent motion synthesis, including seamless integration of complex camera dynamics alongside object motion.

Implications and Future Work

The research has both practical and theoretical implications. Practically, ATI simplifies workflows and provides a user-friendly interface for generating video content, which can be pivotal for applications in filmmaking, virtual reality, and beyond. Theoretically, the unified approach underscores potential advancements in trajectory-based modeling, offering a basis for exploring more sophisticated motion interactions in generated sequences.

Looking forward, future developments might explore enhanced physical realism in trajectory following and improved user interfaces for trajectory specification, enhancing accessibility and precision. As AI continues to evolve, integrating such frameworks within broader creative tools could revolutionize content production, offering refined control and creativity to both algorithm designers and end-users.

In summary, the ATI framework offers a substantial leap towards unifying and simplifying motion control in video generation, backed by robust experimental validation and promising potential for future exploration in AI-driven content creation.