  Video Anomaly Detection (VAD) systems can autonomously monitor and identify
disturbances, reducing the need for manual labor and associated costs. However,
current VAD systems are often limited by their superficial semantic
understanding of scenes and minimal user interaction. Additionally, the
prevalent data scarcity in existing datasets restricts their applicability in
open-world scenarios. In this paper, we introduce Hawk, a novel framework that
leverages interactive large Visual Language Models (VLM) to interpret video
anomalies precisely. Recognizing the difference in motion information between
abnormal and normal videos, Hawk explicitly integrates motion modality to
enhance anomaly identification. To reinforce motion attention, we construct an
auxiliary consistency loss within the motion and video space, guiding the video
branch to focus on the motion modality. Moreover, to improve the interpretation
of motion-to-language, we establish a clear supervisory relationship between
motion and its linguistic representation. Furthermore, we have annotated over
8,000 anomaly videos with language descriptions, enabling effective training
across diverse open-world scenarios, and also created 8,000 question-answering
pairs for users' open-world questions. The final results demonstrate that Hawk
achieves SOTA performance, surpassing existing baselines in both video
description generation and question-answering. Our codes/dataset/demo will be
released at https://github.com/jqtangust/hawk.
