Specifying nuanced and compelling camera motion remains a significant hurdle for non-expert creators using generative tools, creating an "expressive gap" where generic text prompts fail to capture cinematic vision. This barrier limits individual creativity and restricts the accessibility of cinematic production for small-scale industries and educational content creators. To address this, we present a zero-shot diffusion-based framework for personalized camera motion control, enabling the transfer of cinematic movements from a single reference video onto a user-provided static image without requiring 3D data, predefined trajectories, or complex graphical interfaces. Our technical contribution involves an inference-time optimization strategy using dual Low-Rank Adaptation (LoRA) networks, with an orthogonality regularizer that encourages separation between spatial appearance and temporal motion updates, alongside a homography-based refinement strategy that provides weak geometric guidance. We evaluate our approach using a new metric, CameraScore, and two distinct user studies. A 72-participant perceptual study demonstrates that our method significantly outperforms existing baselines in motion accuracy (90.45% preference) and scene preservation (70.31% preference). Furthermore, a 12-participant task-based interaction study confirms that our workflow significantly improves usability and creative control (p < 0.001) compared to standard text- or preset-based prompts. We hope this work lays a foundation for future advancements in camera motion transfer across diverse scenes.