  In this work, we are dedicated to a new task, i.e., hand-object interaction
image generation, which aims to conditionally generate the hand-object image
under the given hand, object and their interaction status. This task is
challenging and research-worthy in many potential application scenarios, such
as AR/VR games and online shopping, etc. To address this problem, we propose a
novel HOGAN framework, which utilizes the expressive model-aware hand-object
representation and leverages its inherent topology to build the unified surface
space. In this space, we explicitly consider the complex self- and mutual
occlusion during interaction. During final image synthesis, we consider
different characteristics of hand and object and generate the target image in a
split-and-combine manner. For evaluation, we build a comprehensive protocol to
access both the fidelity and structure preservation of the generated image.
Extensive experiments on two large-scale datasets, i.e., HO3Dv3 and DexYCB,
demonstrate the effectiveness and superiority of our framework both
quantitatively and qualitatively. The project page is available at
https://play-with-hoi-generation.github.io/.
