Generating realistic human-object interactions (HOI) remains challenging due to the complex temporal coordination required between human actions and object responses. Current diffusion-based methods excel at spatial modeling but fail to capture the critical temporal dynamics that govern physically plausible interactions, leading to artifacts such as premature object movement and poor action-response synchronization. To tackle this issue, we introduce V-HOI, a novel velocity-aware framework that explicitly models temporal dynamics through enhanced motion representations and adaptive constraints. A fundamental principle underlying realistic interactions is the coordinated motion dynamics where contact regions and objects exhibit coupled velocity behaviors that vary systematically across interaction phases. Our framework employs a ControlNet-inspired three-branch architecture featuring a dedicated motion branch and dual interaction branches, trained via a two-stage strategy that preserves motion generation capabilities while learning interaction dynamics. Extensive experiments on the FullBodyManipulation dataset demonstrate substantial improvements over existing methods across both motion quality and interaction metrics, with particularly notable advances in contact modeling accuracy. Ablation studies validate the necessity of each component and confirm that our velocity-aware approach successfully generates temporally coordinated and physically plausible interaction sequences.