Affordance-Aware Object Insertion via Mask-Aware Dual Diffusion (2412.14462v2)

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.

• The paper introduces a Mask-Aware Dual Diffusion (MADD) model that enhances object-scene composition by leveraging affordance principles.
• It utilizes the SAM-FB dataset with over 3 million samples and 3,000+ object categories to support diverse and precise insertion prompts.
• Empirical results show improved FID and CLIP scores, indicating realistic integration and broad applicability in automated content creation.

Affordance-Aware Object Insertion via Mask-Aware Dual Diffusion

This paper presents a novel approach to image composition through the introduction of the affordance-aware object insertion task, which aims to integrate any object into any scene pursuant to affordance principles. The proposed method extends the concept of affordance—traditionally used in human-centered tasks—to general object-scene composition, addressing the integration of foreground objects into background images while ensuring semantic consistency and physical plausibility.

Objectives and Key Innovations

The primary objective of this work is to develop a robust framework that can seamlessly insert objects into backgrounds without contravening physical laws and semantic expectations. The authors identify three core challenges in achieving this objective: correctly recognizing affordance relationships between objects and their environments, supporting generalization across varied foreground objects, and engaging with diverse types of insertion prompts—from precise masks to ambiguous points or even null prompts.

To address these challenges, the authors have introduced:

1. SAM-FB Dataset: A large-scale dataset specifically designed for affordance learning, containing over 3 million samples with more than 3,000 object categories. This dataset is essential for training models capable of accommodating the diverse range of objects and integration scenarios found in real-world applications.
2. Mask-Aware Dual Diffusion (MADD) Model: A dual-stream architecture in which RGB images and insertion masks are denoised concurrently. The MADD model diverges from traditional single-stream diffusion models by explicitly incorporating the insertion mask in the diffusion process, aiding the learning of affordance concepts and improving integration coherence.
3. Unified Prompt Representation: A mechanism for interpreting a variety of positional prompts uniformly, thereby enhancing the model's flexibility and utility.

Results and Implications

Empirical evaluations demonstrate that the introduced MADD model consistently outperforms baseline methods on both the SAM-FB test set and in-the-wild images, achieving superior quality integration as evidenced by metrics like FID and CLIP scores. The capacity of the method to maintain semantic consistency while generating realistic compositions under various conditions underscores its efficacy.

The implications of this work extend to numerous fields, including automated dataset synthesis, novel content creation, and advanced photo editing. From a theoretical standpoint, extending the concept of affordance beyond the human-object interaction paradigm enriches the dialogue on AI's capacity to understand and simulate complex environmental relationships.

Speculative Future Directions

Given the promising results achieved through the Mask-Aware Dual Diffusion model, future research could explore further applications of this architecture, potentially extending beyond static image synthesis to include dynamic content such as video generation. Additionally, refining the model to handle more complex affordance conditions, such as multiple object interactions, shrouded object settings, or extreme weather conditions, would bolster its applicability.

Moreover, the SAM-FB dataset provides a foundation for further exploration into large-scale, diverse object integration tasks. As the dataset can be expanded with additional images and categories, it serves as a significant resource for continued investigation into comprehensive affordance-aware insertion methodologies.

In conclusion, this paper presents a structured approach to advancing image composition via affordance principles and dual diffusion processes, and its findings are likely to influence subsequent work in image synthesis and computer vision.