Trajectory attention for fine-grained video motion control (doi:10.21979/N9/II0EM4)

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Trajectory attention for fine-grained video motion control

doi:10.21979/N9/II0EM4

DR-NTU (Data)

2025-02-04

1

Xiao, Zeqi; Ouyang, Wenqi; Zhou, Yifan; Yang, Shuai; Yang, Lei; Si, Jianlou; Pan, Xingang, 2025, "Trajectory attention for fine-grained video motion control", https://doi.org/10.21979/N9/II0EM4, DR-NTU (Data), V1

Trajectory attention for fine-grained video motion control

doi:10.21979/N9/II0EM4

Xiao, Zeqi (Nanyang Technological University)

Ouyang, Wenqi (Nanyang Technological University)

Zhou, Yifan (Nanyang Technological University)

Yang, Shuai (Nanyang Technological University)

Yang, Lei (Sensetime Ltd.)

Si, Jianlou (Sensetime Ltd.)

Pan, Xingang (Nanyang Technological University)

NIL

DR-NTU (Data)

Xiao, Zeqi

Xiao, Zeqi

2025-02-01

https://doi.org/10.21979/N9/II0EM4

Computer and Information Science, Computer and Information Science, Video diffusion model, motion control

Recent advancements in video generation have been greatly driven by video diffusion models, with camera motion control emerging as a crucial challenge in creating view-customized visual content. This paper introduces trajectory attention, a novel approach that performs attention along available pixel trajectories for fine-grained camera motion control. Unlike existing methods that often yield imprecise outputs or neglect temporal correlations, our approach possesses a stronger inductive bias that seamlessly injects trajectory information into the video generation process. Importantly, our approach models trajectory attention as an auxiliary branch alongside traditional temporal attention. This design enables the original temporal attention and the trajectory attention to work in synergy, ensuring both precise motion control and new content generation capability, which is critical when the trajectory is only partially available. Experiments on camera motion control for images and videos demonstrate significant improvements in precision and long-range consistency while maintaining high-quality generation. Furthermore, we show that our approach can be extended to other video motion control tasks, such as first-frame-guided video editing, where it excels in maintaining content consistency over large spatial and temporal ranges.

Code

S-Lab License 1.0 <br/> Copyright 2025 S-Lab <br/><br/> Redistribution and use for non-commercial purpose in source and binary forms, with or without modification, are permitted provided that the following conditions are met: <br/> 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. <br/> 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. <br/> 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. <br/><br/> THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. <br/><br/> In the event that redistribution and/or use for commercial purpose in source or binary forms, with or without modification is required, please contact the contributor(s) of the work.

Code: <a href="https://xizaoqu.github.io/trajattn/">Link</a>

2411.19324.2024

Xiao, Z., Ouyang, W., Zhou, Y., Yang, S., Yang, L., Si, J., & Pan, X. (2024). Trajectory Attention for Fine-grained Video Motion Control. arXiv preprint arXiv:2411.19324.