The most incredible thing here is that this demonstrates a level of 3D understanding that I didn't believe existed in 2D image models yet. All of the 3D information in the output was inferred from the training set, which is exclusively uncurated and unsorted 2D still images. No 3D models, no camera parameters, no depth maps. No information about picture content other than a text label (scraped from the web and often incorrect!).
From a pile of random undifferentiated images the model has learned the detailed 3D structure and plausible poses and variants of thousands (millions?) of everyday objects. And all we needed to get that 3D information out of the model was the right sampling procedure.
From a pile of random undifferentiated images the model has learned the detailed 3D structure and plausible poses and variants of thousands (millions?) of everyday objects. And all we needed to get that 3D information out of the model was the right sampling procedure.