ControlCom: Controllable Image Composition using Diffusion Model (2308.10040v1)

Abstract: Image composition targets at synthesizing a realistic composite image from a pair of foreground and background images. Recently, generative composition methods are built on large pretrained diffusion models to generate composite images, considering their great potential in image generation. However, they suffer from lack of controllability on foreground attributes and poor preservation of foreground identity. To address these challenges, we propose a controllable image composition method that unifies four tasks in one diffusion model: image blending, image harmonization, view synthesis, and generative composition. Meanwhile, we design a self-supervised training framework coupled with a tailored pipeline of training data preparation. Moreover, we propose a local enhancement module to enhance the foreground details in the diffusion model, improving the foreground fidelity of composite images. The proposed method is evaluated on both public benchmark and real-world data, which demonstrates that our method can generate more faithful and controllable composite images than existing approaches. The code and model will be available at https://github.com/bcmi/ControlCom-Image-Composition.

• The paper introduces a unified diffusion model that integrates multiple compositing tasks using a 2D control vector for fine-tuning attributes.
• It employs a novel two-stage fusion strategy that first ensures semantic coherence through global embedding and then preserves intricate local details.
• The self-supervised training framework, leveraging synthetic data, significantly improves foreground fidelity and background preservation on benchmarks.

Insights on ControlCom: Controllable Image Composition Using Diffusion Model

The paper "ControlCom: Controllable Image Composition using Diffusion Model" advances the field of image compositing by integrating controllability directly into the generation process. Addressing the limitations of existing generative models, particularly their inability to maintain control over specific attributes of foreground objects, the authors propose a unified framework that consolidates multiple compositing tasks into one diffusion model.

A significant contribution of this work is its focus on enhancing controllability in image composition. The authors successfully integrate tasks like image blending, image harmonization, view synthesis, and generative composition into a cohesive diffusion model. Remarkably, the ControlCom approach introduces a 2-dimensional indicator vector capable of selectively altering foreground illumination and pose. This design allows for more nuanced image synthesis, ensuring that attributes such as illumination or pose can be modified or maintained as per user preference.

To accommodate these enhancements, a novel two-stage fusion strategy has been developed. This strategy cleverly separates the integration of global and local embeddings within the diffusion model, thereby improving the representation and fidelity of the composite images. The first stage focuses on global embedding integration to ensure semantic coherence, followed by the fusion of local embeddings to preserve the intricate details of the foreground image. This sequential amalgamation significantly boosts the quality of synthesized images, addressing the commonly observed challenge of detail loss seen in other approaches.

The proposed self-supervised training framework is another notable feature, designed to enable the model to simultaneously learn all four tasks. By creating a synthetic dataset through a meticulously detailed data preparation pipeline, the authors ensure that the model encounters a comprehensive set of examples during training. This preparation involves innovative data augmentation techniques that preserve real-world challenges in image compositing.

Experimentally, ControlCom demonstrates an improvement over previous methods in terms of both foreground fidelity and background preservation. According to the paper, on standard benchmarks such as COCOEE, the model achieves high values of FID and CLIP scores, indicating superior realism and better foreground representation. Notably, ControlCom's ability to selectively alter illumination and pose without degrading the background provides evidence of its practical applicability.

Future research inspired by this paper might explore more extensive datasets that include a larger variety of compositional complexities or extend the 2-dimensional control vector to incorporate additional attributes, such as texture or scale. Moreover, applying ControlCom in dynamic contexts like video could offer new vistas of research.

In conclusion, "ControlCom: Controllable Image Composition using Diffusion Model" establishes a compelling approach to achieving enhanced controllability in image composition through diffusion models. Combining rigorous experimentation with a comprehensive methodology, this work sets a promising trajectory for future developments in the AI-driven control of image synthesis.