MoTrans: Customized Motion Transfer with Text-driven Video Diffusion Models

Xiaomin Li, Xu Jia, Qinghe Wang, Haiwen Diao, mengmeng Ge, Pengxiang Li, You He, Huchuan Lu

Abstract: Existing pretrained text-to-video (T2V) models have demonstrated impressive abilities in generating realistic videos with basic motion or camera movement. However, these models exhibit significant limitations when generating intricate, human-centric motions. Current efforts primarily focus on fine-tuning models on a small set of videos containing a specific motion. They often fail to effectively decouple motion and the appearance in the limited reference videos, thereby weakening the modeling capability of motion patterns. To this end, we propose MoTrans, a customized motion transfer method enabling video generation of similar motion in new context. Specifically, we introduce a multimodal large language model (MLLM)-based recaptioner to expand the initial prompt to focus more on appearance and an appearance injection module to adapt appearance prior from video frames to the motion modeling process. These complementary multimodal representations from recaptioned prompt and video frames promote the modeling of appearance and facilitate the decoupling of appearance and motion. In addition, we devise a motion-specific embedding for further enhancing the modeling of the specific motion. Experimental results demonstrate that our method effectively learns specific motion pattern from singular or multiple reference videos, performing favorably against existing methods in customized video generation.

Primary Subject Area: [Generation] Generative Multimedia

Secondary Subject Area: [Generation] Generative Multimedia

Relevance To Conference: Our work involves interactions across multiple modalities: text, images, and video. We believe our work is highly relevant to one of the themes in ACM Multimedia 2024, namely specifically Generative Multimedia. This paper primarily focuses on customized text-to-video tasks, where the trained model can customize specific motions for a user-specified subject. The overall framework can be regarded as personalized systems that allow for a better user experience.

Submission Number: 164