We create a system for semi-automatically converting unconstrained 2D images and videos into stereoscopic 3D. Current efforts are done automatically or manually by rotoscopers. The former prohibits user intervention, or error correction, while the latter is time consuming, requiring a large staff. Semi-automatic mixes the two, allowing for faster and accurate conversion, while decreasing time to release 3D content. User-defined strokes for the image, or over several keyframes, corresponding to a rough estimate of the scene depths are defined. After, the rest of the depths are found, creating depth maps to generate stereoscopic 3D content, and Depth Image Based Rendering is employed to generate the artificial views. Here, depth map estimation can be considered as a multi-label segmentation problem, where each class is a depth value. Optionally, for video, only the first frame can be labelled, and the strokes are propagated using a modified robust tracking algorithm. Our work combines the merits of two respected segmentation algorithms: Graph Cuts and Random Walks. The diffusion of depths from Random Walks, combined with the edge preserving properties from Graph Cuts is employed to create the best results possible. Results demonstrate good quality stereoscopic images and videos with minimal effort.