Learning real-world robotic manipulation is challenging, particularly when limited demonstrations are available. Existing methods for few-shot manipulation often rely on simulation-augmented data or pre-built modules like grasping and pose estimation, which struggle with sim-to-real gaps and lack versatility. While large-scale imitation pre-training shows promise, adapting these general-purpose policies to specific tasks in data-scarce settings remains unexplored. To achieve this, we propose ControlManip, a novel framework that bridges pre-trained manipulation policies with object-centric representations via a ControlNet-style architecture for efficient fine-tuning. Specifically, to introduce object-centric conditions without overwriting prior knowledge, ControlManip zero-initializes a set of projection layers, allowing them to gradually adapt the pre-trained manipulation policies. In real-world experiments across 6 diverse tasks, including pouring cubes and folding clothes, our method achieves a 73.3\% success rate while requiring only 10-20 demonstrations --- a significant improvement over traditional approaches that require more than 100 demonstrations to achieve comparable success. Comprehensive studies show that ControlManip improves the few-shot fine-tuning success rate by 252\% over baselines and demonstrates robustness to object and background changes. By lowering the barriers to task development, ControlManip accelerates real-world robot adoption and lays the groundwork for unifying large-scale policy pre-training with object-centric representations.