In this paper, we propose a geometric active contour approach driven by implicit flows for segmenting deformative objects. The geometric active contours capture dynamical shapes by yielding initial level-set to image features. However, the interesting objects are often associated with salient motions, which has been ignored by naive level-set methods and thus intrinsically limit the harmonizing range. According to hydrodynamics, the implicit flows involving advections and diffusions are formulated to directly guide the level-set updating. To generate the advective vector field, a regression algorithm with smoothing and sparse is embedded in terms of finite motion vectors among sequence. Also the implicit flows are simultaneously synthesized with the non-uniform diffusions restrained by spatial gradients. The proposed improved geometric active contour model based on advection vector field and diffusion flows is a unified and efficient framework, also the topological preservation is inherited from the original geometric active contours. Experimental results of applying this method to real scenes show that the method has fast convergence speed and can accurately segment deformable objects with global motion.