ZeroComp: Zero-shot Object Compositing from Image Intrinsics via
  Diffusion
Authors: name: Zitian Zhang, name: Frédéric Fortier-Chouinard, name: Mathieu Garon, name: Anand Bhattad, name: Jean-François Lalonde

We present ZeroComp, an effective zero-shot 3D object compositing approach
that does not require paired composite-scene images during training. Our method
leverages ControlNet to condition from intrinsic images and combines it with a
Stable Diffusion model to utilize its scene priors, together operating as an
effective rendering engine. During training, ZeroComp uses intrinsic images
based on geometry, albedo, and masked shading, all without the need for paired
images of scenes with and without composite objects. Once trained, it
seamlessly integrates virtual 3D objects into scenes, adjusting shading to
create realistic composites. We developed a high-quality evaluation dataset and
demonstrate that ZeroComp outperforms methods using explicit lighting
estimations and generative techniques in quantitative and human perception
benchmarks. Additionally, ZeroComp extends to real and outdoor image
compositing, even when trained solely on synthetic indoor data, showcasing its
effectiveness in image compositing.