Image Generation from Layout (1811.11389v3)

Abstract: Despite significant recent progress on generative models, controlled generation of images depicting multiple and complex object layouts is still a difficult problem. Among the core challenges are the diversity of appearance a given object may possess and, as a result, exponential set of images consistent with a specified layout. To address these challenges, we propose a novel approach for layout-based image generation; we call it Layout2Im. Given the coarse spatial layout (bounding boxes + object categories), our model can generate a set of realistic images which have the correct objects in the desired locations. The representation of each object is disentangled into a specified/certain part (category) and an unspecified/uncertain part (appearance). The category is encoded using a word embedding and the appearance is distilled into a low-dimensional vector sampled from a normal distribution. Individual object representations are composed together using convolutional LSTM, to obtain an encoding of the complete layout, and then decoded to an image. Several loss terms are introduced to encourage accurate and diverse generation. The proposed Layout2Im model significantly outperforms the previous state of the art, boosting the best reported inception score by 24.66% and 28.57% on the very challenging COCO-Stuff and Visual Genome datasets, respectively. Extensive experiments also demonstrate our method's ability to generate complex and diverse images with multiple objects.

• The paper introduces Layout2Im, a novel model that separates object category and appearance to synthesize realistic images from specified layouts.
• The methodology leverages a convolutional LSTM to merge individual object features, efficiently handling overlapping objects in complex scenes.
• The model demonstrates significant improvements in inception scores on datasets like COCO-Stuff and Visual Genome, surpassing methods such as sg2im and pix2pix.

Image Generation from Layout

The paper "Image Generation from Layout" presents a sophisticated approach to the controlled generation of images based on predefined spatial layouts. The authors introduce a novel layout-based image generation model named Layout2Im, designed to address the inherent complexities in generating realistic images that encapsulate multiple and varied objects in specified spatial arrangements.

Overview of Layout2Im

Layout2Im leverages a disentangled object representation that separates each object's category from its appearance. The category is represented using word embeddings, while the appearance is captured through a low-dimensional vector drawn from a normal distribution. These individual object representations are then combined using a convolutional LSTM, providing an integrated encoding of the entire layout, which is subsequently decoded into a realistic image.

Methodology

1. Object Representation: Each object in the specified layout is represented with a tuple of bounding boxes and categories. The category is encoded via word embeddings, while appearances are characterized by low-dimensional vectors sampled from a normal distribution, allowing diverse image realizations from a single layout.
2. Latent Code Sampling: The model incorporates a variational inference framework to sample latent appearance codes for each object. This accounts for the uncertainty and variability in object appearances across different instances.
3. Image Generation: Layout2Im employs convolutional LSTMs to merge the individual object feature maps into a unified hidden feature map for the full image, which is then decoded to produce the final output image. This enables the model to handle overlapping objects and ensure an accurate composition.
4. Loss Functions: The training process uses multiple loss components, including adversarial loss, perceptual loss, and KL-divergence, to encourage both realism and diversity in generated images. Discriminators ensure that individual objects are convincing and positioned correctly according to the layout.

Experimental Results

Layout2Im significantly outperforms state-of-the-art methods such as sg2im and pix2pix on challenging datasets like COCO-Stuff and Visual Genome, evidenced by a substantial improvement in inception scores—24.66% and 28.57%, respectively. The model not only generates plausible and accurate object layouts but also ensures that these objects are recognizable and spatially coherent.

The results showcase the model's robustness in generating complex scenes with numerous objects, where each object's interactions and spatial integrity are maintained. Moreover, experiments highlight Layout2Im's capability for producing diverse image outcomes from identical layouts by sampling different appearance vectors.

Implications and Future Directions

The proposed method's implications are manifold. Practically, it enhances automated image generation capabilities, potentially serving artistic and commercial applications where specific scene compositions are vital. Theoretically, the disentangled representation and effective incorporation of spatial layouts in generative models provide insights into advancing conditional image synthesis.

Future research may explore high-resolution image generation or incorporate additional object attributes for greater control. Investigating methods to require less labeled data or leveraging unsupervised techniques could further broaden the applicability of layout-based image generation methods.

In conclusion, the Layout2Im model represents a pivotal advancement in controlled image synthesis, showing promising potential in accurately rendering complex scenes from specified layouts. Its methodological innovations and empirical successes pave the way for further exploration and refinement in this domain.