In this paper, we propose a unified variational framework for tomographic reconstruction of 3-D dynamic objects. We use a geometric scene model, where the scene is assumed to be composed of discrete objects captured by their continuous surface boundaries. Object dynamics are modeled as consisting of separate intensity dynamics and object boundary dynamics. The shape dynamics are incorporated into our variational framework by defining a new distance measure between surfaces based on their signed distance functions, which is an extension of our previous definition of distance between curves. These models are then combined in a unified variational framework which incorporates the observation data, shape and intensity dynamics, and prior information on object spatial smoothness. The object surface and intensity sequences are estimated jointly as the minimizer of the resulting energy function. A coordinate descent algorithm based on surface evolution is developed to solve this nonlinear optimization problem. Efficient level set methods are used to implement the algorithm. This approach evolves the surfaces from their initial position to the final solution and handles topological uncertainties automatically.