What is libcom
libcom is an image composition toolbox launched by the BCMI Lab at Shanghai Jiao Tong University. It aims to solve inconsistency issues between foreground and background, such as mismatches in appearance, geometry, and semantics, to generate realistic composite images. The toolbox includes a variety of image composition-related tasks, such as image blending, image harmonization, shadow generation, object placement, generative composition, and quality assessment. libcom provides a series of efficient algorithm implementations, and the algorithms will be continuously updated. The goal is to solve problems related to image composition based on a simple import command import libcom so that users can easily perform complex image composition operations.
Main features of libcom
- Image blending: when generating composite images, image blending techniques are used to smooth the transition between foreground and background, reducing visible seams and unnatural boundaries.
- Image harmonization(Image Harmonization): adjust the lighting and color of the foreground to match the lighting and color of the background, improving the realism of the composite image.
- Shadow generation(Shadow Generation): add appropriate shadows to foreground objects, enhancing the interaction and three-dimensionality between the foreground and background.
- Object placement(Object Placement): determine the reasonable position and size of foreground objects in the background, taking into account occlusion, depth, and the semantic consistency of the scene.
- Generative composition(Generative Composition): directly generate realistic composite images from foreground and background images based on generative models, taking into account the geometric and semantic information of the foreground.
Technical principles of libcom
- Traditional image processing: smooth image boundaries based on techniques such as Poisson editing and Laplacian pyramids.
- Deep learning: combine with convolutional neural networks (CNN) to learn image features and perform pixel-level color and lighting adjustments.
- Generative adversarial networks (GANs): in generative composition, use GANs to generate foregrounds that are harmonious with the background and have correct lighting and shadows.
- Diffusion models: generate high-quality composite images based on diffusion models given noise and a small amount of foreground information.
- Datasets and pretrained models: train models on multiple public datasets to ensure that the algorithms work effectively in diverse scenarios.
- Feature extraction and matching: based on feature extraction techniques such as BargainNet, evaluate the harmony between the foreground and background in composite images.
libcom project address
- Project website：https://pypi.org/project/libcom/
- GitHub repository：https://github.com/bcmi/libcom
- arXiv technical paper：https://arxiv.org/pdf/2106.14490
- Online demo：https://bcmi.sjtu.edu.cn/home/niuli/demo_image_composition/
Application scenarios of libcom
- Augmented reality (AR): in augmented reality applications, image composition technology seamlessly integrates virtual objects into real-world scenes, providing users with a more immersive experience.
- Artistic creation: artists and designers use image composition technology to combine different visual elements together to create entirely new artworks or design concept images.
- E-commerce: online retailers display the effects of products in different environments, for example, placing furniture models in real photos of homes to help customers conduct virtual try-ons.
- Film and video production: In film post-production, image compositing technology creates complex scenes or places actors in backgrounds that are impossible to shoot in reality.
- Game development: Game designers use image compositing technology to create game environments or integrate game characters into real-world scenes, increasing the fun and realism of the game.