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

• URL: http://arxiv.org/abs/2412.01343v1
• Date: Mon, 02 Dec 2024 10:07:59 GMT
• Title: MoTrans: Customized Motion Transfer with Text-driven Video Diffusion Models
• Authors: Xiaomin Li, Xu Jia, Qinghe Wang, Haiwen Diao, Mengmeng Ge, Pengxiang Li, You He, Huchuan Lu,
• Abstract summary: MoTrans is a customized motion transfer method enabling video generation of similar motion in new context. multimodal representations from recaptioned prompt and video frames promote the modeling of appearance. Our method effectively learns specific motion pattern from singular or multiple reference videos.
• Score: 59.10171699717122
• License:
• 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.

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