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Can robots mold soft plastic materials by shaping depth images? The shortanswer is no: current day robots can't. In this article, we address the problemof shaping plastic material with an anthropomorphic arm/hand robot, whichobserves the material with a fixed depth camera. Robots capable of moldingcould assist humans in many tasks, such as cooking, scooping or gardening. Yet,the problem is complex, due to its high-dimensionality at both perception andcontrol levels. To address it, we design three alternative data-based methodsfor predicting the effect of robot actions on the material. Then, the robot canplan the sequence of actions and their positions, to mold the material into adesired shape. To make the prediction problem tractable, we rely on twooriginal ideas. First, we prove that under reasonable assumptions, the shapingproblem can be mapped from point cloud to depth image space, with many benefits(simpler processing, no need for registration, lower computation time andmemory requirements). Second, we design a novel, simple metric for quicklymeasuring the distance between two depth images. The metric is based on theinherent point cloud representation of depth images, which enables direct andconsistent comparison of image pairs through a non-uniform scaling approach,and therefore opens promising perspectives for designing \textit{depth image --based} robot controllers. We assess our approach in a series of unprecedentedexperiments, where a robotic arm/hand molds flour from initial to final shapes,either with its own dataset, or by transfer learning from a human dataset. Weconclude the article by discussing the limitations of our framework and thoseof current day hardware, which make human-like robot molding a challenging openresearch problem.