Affordance-Aware Object Insertion via Mask-Aware Dual Diffusion
Jixuan He, Wanhua Li, Ye Liu, Junsik Kim, Donglai Wei, Hanspeter Pfister
2024-12-20
Summary
This paper discusses a new method called Affordance-Aware Object Insertion via Mask-Aware Dual Diffusion, which helps in seamlessly adding objects into different scenes in images. It focuses on understanding how objects interact with their surroundings to make the inserted objects look natural.
What's the problem?
When editing images, it's often difficult to place objects into backgrounds in a way that makes sense. Current methods can struggle with this because they don't fully consider how objects should behave in relation to their environment, leading to unrealistic results.
What's the solution?
The authors propose a new task called affordance-aware object insertion, where they developed a large dataset called SAM-FB containing over 3 million examples of different objects and scenes. They also created a model called Mask-Aware Dual Diffusion (MADD) that uses a dual-stream approach to improve how objects are inserted into scenes by considering both the image and the insertion mask at the same time. This helps ensure that the inserted objects fit well with the background.
Why it matters?
This research is important because it enhances image editing techniques, making it easier to create realistic compositions where objects look like they naturally belong in their new environments. This has practical applications in fields like graphic design, advertising, and virtual reality, where realistic image manipulation is crucial.
Abstract
As a common image editing operation, image composition involves integrating foreground objects into background scenes. In this paper, we expand the application of the concept of Affordance from human-centered image composition tasks to a more general object-scene composition framework, addressing the complex interplay between foreground objects and background scenes. Following the principle of Affordance, we define the affordance-aware object insertion task, which aims to seamlessly insert any object into any scene with various position prompts. To address the limited data issue and incorporate this task, we constructed the SAM-FB dataset, which contains over 3 million examples across more than 3,000 object categories. Furthermore, we propose the Mask-Aware Dual Diffusion (MADD) model, which utilizes a dual-stream architecture to simultaneously denoise the RGB image and the insertion mask. By explicitly modeling the insertion mask in the diffusion process, MADD effectively facilitates the notion of affordance. Extensive experimental results show that our method outperforms the state-of-the-art methods and exhibits strong generalization performance on in-the-wild images. Please refer to our code on https://github.com/KaKituken/affordance-aware-any.