IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representation

Core Concepts

Decoupling generative object compositing into identity preservation and background alignment stages significantly improves realism and fidelity.

Abstract

IMPRINT introduces a novel two-stage framework for generative object compositing. The first stage focuses on context-agnostic identity preservation, while the second stage harmonizes the object with the background. Extensive experiments show IMPRINT outperforms existing methods in identity preservation and composition quality. The model incorporates shape-guidance for user control over compositing.

Stats

IMPRINT significantly outperforms existing methods in identity preservation and composition quality. The first stage is trained on 1,409,545 pairs and validated on 11,175 pairs from MVImgNet. The second stage is fine-tuned on a mixture of image datasets and video datasets, including a training set of 217,451 pairs.

Quotes

Key Insights Distilled From

IMPRINT

by Yizhi Song,Z... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10701.pdf

Deeper Inquiries

How can IMPRINT's approach to generative object compositing be applied to other computer vision tasks

IMPRINT's approach to generative object compositing, particularly its two-stage framework that focuses on identity preservation and background harmonization, can be applied to various other computer vision tasks. For instance: Image Editing: The concept of separating the task into different stages could be beneficial for tasks like image editing or retouching where maintaining certain aspects of an image while adjusting others is crucial. Object Recognition: By training models to first focus on capturing detailed features of objects in a context-agnostic manner before integrating them into a scene, it could improve object recognition accuracy by ensuring better feature extraction. Scene Understanding: Applying this approach to scene understanding tasks could help in generating more realistic scenes by preserving the identity of objects while harmonizing them with the background.

What potential limitations could arise from decoupling the compositing process into two stages

Decoupling the compositing process into two stages may introduce some limitations: Loss of Contextual Information: Splitting the process might lead to a loss of contextual information as each stage focuses on specific aspects independently without considering holistic relationships between elements. Increased Complexity: Managing multiple stages adds complexity to the model architecture and training pipeline, potentially making it harder to optimize and fine-tune effectively. Synchronization Challenges: Ensuring seamless integration between the two stages without introducing artifacts or inconsistencies can be challenging.

How might advancements in multi-view datasets impact the performance of models like IMPRINT

Advancements in multi-view datasets can have significant impacts on models like IMPRINT: Improved Identity Preservation: Access to diverse views from multi-view datasets allows models like IMPRINT to capture finer details and variations in object appearance, leading to enhanced identity preservation during compositing. Enhanced Generalization Multi-view datasets provide a broader range of perspectives for training, enabling models like IMPRINT to generalize better across different viewpoints and poses when composing objects into backgrounds. Better Geometric Adaptation With richer data from multi-view sources, models can learn more robust geometric transformations for aligning objects with backgrounds accurately. This results in improved realism and coherence in composited images.