Depth self-supervision for single image novel view synthesis
Giovanni Minelli Matteo Poggi Samuele Salti
In this paper, we tackle the problem of generating a novel image from an
arbitrary viewpoint given a single frame as input. While existing methods
operating in this setup aim at predicting the target view depth map to guide
the synthesis, without explicit supervision over such a task, we jointly
optimize our framework for both novel view synthesis and depth estimation to
unleash the synergy between the two at its best. Specifically, a shared depth
decoder is trained in a self-supervised manner to predict depth maps that are
consistent across the source and target views. Our results demonstrate the
effectiveness of our approach in addressing the challenges of both tasks
allowing for higher-quality generated images, as well as more accurate depth
for the target viewpoint.