CamMimic: Zero-Shot Image To Camera Motion Personalized Video Generation Using Diffusion Models

• URL: http://arxiv.org/abs/2504.09472v1
• Date: Sun, 13 Apr 2025 08:04:11 GMT
• Title: CamMimic: Zero-Shot Image To Camera Motion Personalized Video Generation Using Diffusion Models
• Authors: Pooja Guhan, Divya Kothandaraman, Tsung-Wei Huang, Guan-Ming Su, Dinesh Manocha,
• Abstract summary: CamMimic is designed to seamlessly transfer the camera motion observed in a given reference video onto any scene of the user's choice.<n>In the absence of an established metric for assessing camera motion transfer between unrelated scenes, we propose CameraScore.
• Score: 47.65379612084075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce CamMimic, an innovative algorithm tailored for dynamic video editing needs. It is designed to seamlessly transfer the camera motion observed in a given reference video onto any scene of the user's choice in a zero-shot manner without requiring any additional data. Our algorithm achieves this using a two-phase strategy by leveraging a text-to-video diffusion model. In the first phase, we develop a multi-concept learning method using a combination of LoRA layers and an orthogonality loss to capture and understand the underlying spatial-temporal characteristics of the reference video as well as the spatial features of the user's desired scene. The second phase proposes a unique homography-based refinement strategy to enhance the temporal and spatial alignment of the generated video. We demonstrate the efficacy of our method through experiments conducted on a dataset containing combinations of diverse scenes and reference videos containing a variety of camera motions. In the absence of an established metric for assessing camera motion transfer between unrelated scenes, we propose CameraScore, a novel metric that utilizes homography representations to measure camera motion similarity between the reference and generated videos. Extensive quantitative and qualitative evaluations demonstrate that our approach generates high-quality, motion-enhanced videos. Additionally, a user study reveals that 70.31% of participants preferred our method for scene preservation, while 90.45% favored it for motion transfer. We hope this work lays the foundation for future advancements in camera motion transfer across different scenes.

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