  Image composition is a complex task which requires a lot of information about
the scene for an accurate and realistic composition, such as perspective,
lighting, shadows, occlusions, and object interactions. Previous methods have
predominantly used 2D information for image composition, neglecting the
potentials of 3D spatial information. In this work, we propose DepGAN, a
Generative Adversarial Network that utilizes depth maps and alpha channels to
rectify inaccurate occlusions and enhance transparency effects in image
composition. Central to our network is a novel loss function called Depth Aware
Loss which quantifies the pixel wise depth difference to accurately delineate
occlusion boundaries while compositing objects at different depth levels.
Furthermore, we enhance our network's learning process by utilizing opacity
data, enabling it to effectively manage compositions involving transparent and
semi-transparent objects. We tested our model against state-of-the-art image
composition GANs on benchmark (both real and synthetic) datasets. The results
reveal that DepGAN significantly outperforms existing methods in terms of
accuracy of object placement semantics, transparency and occlusion handling,
both visually and quantitatively. Our code is available at
https://amrtsg.github.io/DepGAN/.
