ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion (2403.18818v1)

Abstract: Diffusion models have revolutionized image editing but often generate images that violate physical laws, particularly the effects of objects on the scene, e.g., occlusions, shadows, and reflections. By analyzing the limitations of self-supervised approaches, we propose a practical solution centered on a \q{counterfactual} dataset. Our method involves capturing a scene before and after removing a single object, while minimizing other changes. By fine-tuning a diffusion model on this dataset, we are able to not only remove objects but also their effects on the scene. However, we find that applying this approach for photorealistic object insertion requires an impractically large dataset. To tackle this challenge, we propose bootstrap supervision; leveraging our object removal model trained on a small counterfactual dataset, we synthetically expand this dataset considerably. Our approach significantly outperforms prior methods in photorealistic object removal and insertion, particularly at modeling the effects of objects on the scene.

• The paper introduces a counterfactual dataset creation method by pairing scenes before and after object removal to capture causal effects on the environment.
• The paper applies bootstrap supervision to enlarge the dataset for object insertion, ensuring physical consistency in lighting, shadows, and reflections.
• The paper demonstrates superior performance through quantitative evaluations and user studies, setting new benchmarks for photorealistic image editing.

ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion

Introduction

Photorealistic image editing tasks, particularly object removal and insertion, require sophisticated modeling of not only the object in question but also its effects on the surrounding environment such as shadows, reflections, and occlusions. Traditional diffusion models and self-supervised learning approaches have shown limited efficacy in addressing these challenges, often resulting in physically implausible edits. This paper introduces a novel approach, ObjectDrop, which leverages counterfactual reasoning and bootstrap supervision to enhance the realism of object removal and insertion in images.

Related Work

The paper positions its contributions in context with prior works, categorized into several domains:

1. Image Inpainting: Acknowledging the advancements brought by deep learning and diffusion models to the field of inpainting, the paper critiques the limitations of existing methods in generating physically consistent edits, especially when object-related physical laws are involved.
2. Shadow Removal: While dedicated shadow removal techniques have progressed, they primarily focus on task-specific solutions and fall short in fully addressing the comprehensive needs of object-centric editing where occlusions and reflections also play a crucial role.
3. General Image Editing Models: The emergence of text-based image editing models has broadened the editing capabilities, yet the need for a method that excels specifically at object manipulation with photorealistic accuracy becomes evident.
4. Object Insertion: Existing methods for object insertion, though benefiting from diffusion models and generative adversarial networks, are critiqued for their shortcomings in sustaining object identity and ensuring seamless integration with scene-specific physical attributes like lighting and shading.

Task Definition

The paper meticulously defines the task of photorealistic editing as encompassing not just the visual replacement or addition of objects in images, but also the contextual adaptation of the scene to reflect the logical physical interactions, viz., shadows and reflections, engendered by the object's presence or absence. This rigorous task definition underscores the complexity of realizing both object removal and insertion with high fidelity and physical coherency.

Self-Supervised Limitations

A thorough analysis underscores significant limitations inherent to self-supervised approaches for this task. These include challenges related to the disentanglement of scene and object properties from mere observational data, thus often leading to edits that fail to convincingly mimic real-world physics.

ObjectDrop Methodology

ObjectDrop introduces a counterfactual data generation strategy, where scenes are physically altered to provide direct comparison points for both the presence and absence of objects. This revolutionary concept facilitates the training of diffusion models on high-quality datasets reflective of true physical alterations, circumventing the pitfalls of relying solely on data-driven inference for understanding object-scene interactions.

1. Counterfactual Dataset Creation: For object removal, scenes are captured before and after the physical removal of objects, forming a paired dataset that precisely delineates the causal impact of the object on its environment.
2. Bootstrap Supervision for Object Insertion: Acknowledging the challenge in collecting extensive counterfactual data for object insertion, ObjectDrop employs a novel bootstrap supervision tactic. Here, an object removal model initially trained on a smaller counterfactual dataset is utilized to synthetically generate a larger dataset depicting scenes with artificially inserted objects, albeit without their natural interactions with the environment. This bootstrapped dataset then serves to refine the model's ability to predict these interactions accurately.

Experimental Validation

Comprehensive experiments validate ObjectDrop's superior performance in rendering photorealistic edits across various scenarios, outperforming state-of-the-art methods in both object removal and insertion. Quantitative evaluations, alongside a user paper, manifest a clear preference for ObjectDrop over competing approaches, attesting to its effectiveness in generating visually and physically coherent scene modifications.

Implications and Future Directions

ObjectDrop's success not only advances the state of the art in image editing but also opens new horizons for further research in photorealistic editing, counterfactual reasoning in AI, and beyond. The methodology introduces a paradigm shift towards leveraging physical alterations and bootstrap supervision for training deep learning models, promising significant implications for computational photography, virtual reality, and related fields.

Conclusion

In conclusion, ObjectDrop heralds a novel direction in photorealistic image editing by effectively harnessing counterfactual datasets and bootstrap supervision. Its remarkable capability to perform object removal and insertion with high fidelity, respecting the physical laws governing shadows, reflections, and occlusions, sets a new benchmark in the domain. The methodology and findings presented in this work not only address the identified gaps within previous research paradigms but also pave the way for future explorations into more nuanced and contextually aware image editing technologies.