We present an efficient technique for testulating high-quality capma-clark subdivision surfaces, including features such as semi-sharp creases and hierarchical edits. Here's a car character's subdivision surface, and it's associated base mesh. Regular regions, such as these, are bee splines and can be handled efficiently by the GPU. Regions around extraordinary points, or near semi-sharp creases, are not and require more intelligent care. These extraordinary points and semi-sharp creases are examples of what we call features. Looking at the region near an extraordinary point, we see that none of the patches are bee spline patches. However, after one level of subdivision, the outer patches are bee splines, and the extraordinary region is reduced. Performing another level of subdivision creates more bee spline patches and reduces the extraordinary region further. And we can continue adaptively subdividing around these features until the region is small enough to be handled analytically. Finally, we stitch up the T-junctions to produce a patch structure that can be efficiently handled by the GPU. Our full algorithm proceeds as follows. First, as a pre-computation step, we perform adaptive subdivision on the CPU to produce patch control indices and subdivision tables that describe the adaptive subdivision process. This step is only dependent on the mesh topology and tags such as creases or hierarchal edits, and the indices and tables are loaded onto the GPU only once. Then, at runtime, we send the animated base control point to the GPU, which uses the pre-computed tables to efficiently compute the subdivided patches. Finally, the patches are sent to the test later, which produces the output triangles. Here, we see a real-time capture of our technique. As we increase the test factor, we adaptively subdivide around extraordinary points and simply increase the patch test relation in the regular regions. Our scheme can support level of detail by increasing or decreasing the adaptive subdivision or changing the patch test relation, or even both at the same time. Here, we see our technique applied to a car body. Coloring based on adaptive subdivision depth shows how we efficiently subdivide only around features such as extraordinary points and the semi-sharp creases in the cars hood. Since we are testulating from scratch at every frame, we can easily animate the base control points and produce an animated test relation. Here is another example of a full production quality animated character, test-related in real-time using our technique. In this example, we see the efficiency of using hierarchical edits. For this shot, a character leaves footprints in a relatively low resolution sand field. By using hierarchical edits, we can efficiently subdivide only where necessary and still get highly detailed footprints. The next example of a full production of a full production quality animated character is the first one to be used in the series. The first one to be used in the series is the first one to be used in the series. The first one to be used in the series is the first one to be used in the series. The first one to be used in the series is the first one to be used in the series. The first one to be used in the series is the first one to be used in the series.