Zerocomp: Zero-Shot Object Compositing from Image Intrinsics via Diffusion

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, Zerocompuses intrinsic images based on geometry, albedo, and masked shading, all without the need for paired images of scenes with and without compos-ite objects. Once trained, it seamlessly integrates virtual 3D objects into scenes, adjusting shading to create realistic composites. We develop a high-quality evaluation dataset and demonstrate that Zerocompoutperforms methods using explicit lighting estimations and generative techniques in quantitative and human perception benchmarks. Additionally, Zerocompextends to real and outdoor image compositing, even when trained solely on synthetic indoor data, showcasing its effectiveness in image compositing.