A new learning strategy for object detection is presented. The proposed scheme forgoes the need to train a collection of detectors dedicated to homogeneous families of poses, and instead learns a single classifier that has the inherent ability to deform based on the signal of interest. Specifically, we train a detector with a standard AdaBoost procedure by using combinations of pose-indexed features and pose estimators instead of the usual image features. This allows the learning process to select and combine various estimates of the pose with features able to implicitly compensate for variations in pose. We demonstrate that a detector built in such a manner provides noticeable gains on two hand video sequences and analyze the performance of our detector as these data sets are synthetically enriched in pose while not increased in size.