<p>Classic active contour models (ACMs) are becoming a great promising solution to the contour-based object extraction with the progress of deep learning recently. Inspired by the wave vibration theory in physics, we propose a Generalized Contour Vibration Model (G-CVM) by inheriting the force and motion principle of contour wave for automatically estimating building contours. The contour estimation problems, conventionally solved by snake and level-set based ACMs, are unified to formulate as second-order partial differential equation to model the contour evolution. In parallel with the current ACM methods, we propose two types of evolution paradigms: curve-CVM and surface-CVM, from the perspective of the vibration spaces of contour waves. To tailor personalization contours for specific targets, we parameterize the constant coefficient wave differential equation through a convolutional network, and hereby integrate them into a unified learnable model for contour extraction. Through adopting finite difference optimization, we can progressively perform the contour evolution from an initial state through a recursive computation on the contour vibration model. Both the building contour evolution and the model optimization are modulated to form a close-looping end-to-end network. Besides, we make a discussion of ours <i>vs</i> the conventional ACMs, all which can be interpreted uniformly from the view of differential equation in different evolution domains. Comprehensive evaluations on several building datasets demonstrate the effectiveness and superiority of our proposed G-CVM when compared with other state-of-the-art building extraction networks and deep active contour solutions.</p>