Preserving Salient Object Boundaries in Text-Guided Background Generation using Diffusion Models

Temel Kavramlar

Diffusion-based inpainting models often expand the boundaries of salient objects when used for background generation, changing the object's identity. This paper introduces a model based on ControlNet to adapt inpainting diffusion models to the task of salient object outpainting, preserving the object's boundaries.

Özet

This paper focuses on the problem of salient object outpainting, which involves generating a natural and coherent background for a salient object, while optionally conditioning on a text prompt. The authors observe that popular diffusion-based inpainting models, when used for background generation, often expand the boundaries of the salient object, changing its identity.
To address this issue, the authors propose a model architecture based on ControlNet, which adapts diffusion-based inpainting models (specifically Stable Inpainting 2.0) to the task of salient object outpainting. The key idea is to use the mask of the salient object as an additional input condition to the ControlNet, which helps maintain the object's boundaries during the background generation process.
The authors conduct extensive experiments on multiple datasets, comparing their proposed approach to several state-of-the-art inpainting and outpainting methods. They introduce a new metric to quantify the degree of object expansion, which does not require any human labeling. The results show that their proposed approach reduces object expansion by 3.6x on average compared to the Stable Inpainting 2.0 baseline, while also outperforming the baselines on standard visual metrics such as FID and LPIPS.
The authors also perform ablation studies to analyze the impact of text prompts and the effectiveness of using inpainting models as the base architecture. They find that including a diverse dataset like COCO, even with synthetic salient object masks, can significantly improve the background diversity and reduce object expansion.

İstatistikler

The salient object datasets used for training contain a total of 56k images.
The COCO dataset with 118k images was also included in the training set.
Salient object masks for COCO were generated using the InSPyReNet model.

Alıntılar

"Diffusion models have produced appealing results on different tasks, e.g., unconditional image generation [14, 16, 39, 42], text-to-image generation [33–36], video generation [15], image inpainting [1, 2, 26, 29], image translation [27, 46, 58], and image editing [6, 10, 18]."
"To ensure that salient objects are not masked out, they subtract the portion of some masks that correspond to salient objects in the training image. However, these masks can be from any object, resulting in small masks relative to the image size."
"We call this phenomenon object expansion. As shown in Figure 2, even popular commercial tools for background generation are prone to this limitation."

Önemli Bilgiler Şuradan Elde Edildi

Salient Object-Aware Background Generation using Text-Guided Diffusion Models

by Amir Erfan E... : arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10157.pdf

Daha Derin Sorular

How can the proposed approach be extended to handle non-salient objects in the image?

To extend the proposed approach to handle non-salient objects in the image, the model architecture can be modified to incorporate instance or panoptic segmentation masks for these objects. By training the model on datasets that provide accurate segmentation masks for various objects in the scene, the model can learn to generate backgrounds while preserving the boundaries of both salient and non-salient objects. Additionally, the control mechanism can be adjusted to differentiate between salient and non-salient objects, allowing for tailored background generation based on the object's importance in the scene.

What other control mechanisms, besides ControlNet, could be explored to further improve the preservation of salient object boundaries during background generation?

Besides ControlNet, other control mechanisms that could be explored to enhance the preservation of salient object boundaries during background generation include:

Attention Mechanisms: Implementing attention mechanisms that focus on the salient object regions during the generation process can help maintain object boundaries and ensure that the background complements the object.
Spatial Transformers: Using spatial transformers to manipulate the feature maps based on the salient object's location can guide the model in generating backgrounds that align with the object's context.
Conditional GANs: Incorporating conditional generative adversarial networks can enable the model to generate backgrounds conditioned on the salient object, ensuring that the object remains the focus of the scene.

How can the diversity of generated backgrounds be improved without relying on external datasets like COCO, which may have noisy salient object annotations?

To enhance the diversity of generated backgrounds without relying on external datasets like COCO, which may have noisy salient object annotations, the following strategies can be employed:

Data Augmentation: Implementing data augmentation techniques such as rotation, scaling, and flipping can introduce variability in the training data, leading to a more diverse set of generated backgrounds.
Adversarial Training: Incorporating adversarial training methods can encourage the model to generate more diverse backgrounds by penalizing repetitive or unrealistic outputs.
Multi-Modal Training: Training the model on a combination of image and text modalities can introduce additional context and variability, resulting in a broader range of generated backgrounds that align with different textual prompts.
Curriculum Learning: Gradually increasing the complexity of the training data and tasks can help the model learn to generate diverse backgrounds while preserving salient object boundaries effectively.