ObjectMate: A Recurrence Prior for Object Insertion and Subject-Driven Generation (2412.08645v1)

Abstract: This paper introduces a tuning-free method for both object insertion and subject-driven generation. The task involves composing an object, given multiple views, into a scene specified by either an image or text. Existing methods struggle to fully meet the task's challenging objectives: (i) seamlessly composing the object into the scene with photorealistic pose and lighting, and (ii) preserving the object's identity. We hypothesize that achieving these goals requires large scale supervision, but manually collecting sufficient data is simply too expensive. The key observation in this paper is that many mass-produced objects recur across multiple images of large unlabeled datasets, in different scenes, poses, and lighting conditions. We use this observation to create massive supervision by retrieving sets of diverse views of the same object. This powerful paired dataset enables us to train a straightforward text-to-image diffusion architecture to map the object and scene descriptions to the composited image. We compare our method, ObjectMate, with state-of-the-art methods for object insertion and subject-driven generation, using a single or multiple references. Empirically, ObjectMate achieves superior identity preservation and more photorealistic composition. Differently from many other multi-reference methods, ObjectMate does not require slow test-time tuning.

• The paper presents ObjectMate, a tuning-free diffusion model approach that leverages an "object recurrence prior" by creating a large dataset through instance retrieval from unlabeled images to improve object insertion and generation.
• ObjectMate establishes new evaluation protocols and demonstrates superior performance over existing methods, particularly in object identity preservation and photorealistic integration, confirmed through numerical metrics and user studies.
• The framework offers a cost-effective method for large-scale dataset creation without manual labeling, providing a template for future research and holding implications for various computer vision tasks like object composition and 3D geometry.

A Comprehensive Overview of "ObjectMate: A Recurrence Prior for Object Insertion and Subject-Driven Generation"

This paper presents a novel approach for object insertion and subject-driven generation by introducing a method called ObjectMate. The primary challenge addressed is seamlessly integrating an object into a designated scene while maintaining photorealistic elements like pose, lighting, and identity preservation of the object. Existing frameworks often struggle with these competing objectives due to the lack of large-scale, high-quality paired datasets necessary for effective supervised learning.

Core Contributions and Methodologies

ObjectMate leverages the "object recurrence prior" concept, capitalizing on the prevalence of mass-produced objects across vast, unlabeled image datasets. By detecting and retrieving instances of these objects from large collections such as COCO, Open Images, and WebLI, with features specifically tailored to instance retrieval rather than semantic similarity, the authors construct a valuable dataset of diverse object views under various lighting conditions and poses.

The proposed method is notable for its ability to operate tuning-free, distinctly separating it from other methods that rely on fine-tuning during test-time, which subsequently reduces efficiency and can introduce inconsistency due to parameter sensitivity. ObjectMate employs a text-to-image diffusion architecture trained on the new dataset, which includes both background descriptions from image captions and innovative techniques such as counterfactual object removal to handle problematic features like shadows and reflections.

Evaluation Metrics and Results

For evaluation, the authors advance the current methods by creating new protocols and datasets equipped with ground-truth examples for testing object insertion performance. They propose an enhanced metric for assessing identity preservation, a critical factor often inadequately grasped by current approaches. ObjectMate demonstrates superior outcomes compared to existing state-of-the-art methods, particularly in identity preservation and photorealistic scene integration, both in numerical evaluations and user studies.

A comparative analysis involves the use of retrieval methods based on deep features that ensure the object identity is prioritized. Their comprehensive experimentation with various encoders demonstrates that features designed for instance retrieval significantly outperform traditional semantic features.

Implications and Future Directions

The implications of the ObjectMate framework extend into improving both theoretical understanding and practical capabilities in computer vision tasks like object composition and scene generation. With its methodology of massive supervised dataset creation without manual labeling, ObjectMate provides a template for future research into cost-effective dataset assembly and model training.

Furthermore, the paper hints at areas for development, such as enhancing support for more than three reference views and incorporating human subject retrieval by exploring advanced facial recognition features. The potential for using the created datasets beyond object composition, including possible applications in 3D geometry and object editing, suggests new investigative pathways.

In conclusion, while ObjectMate significantly improves upon the fidelity and efficiency of object insertion and generation tasks, its reliance on high-scale unsupervised dataset extraction and IR feature technology marks a substantial step forward, opening further opportunities for scale and refinement in future AI development.