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Title: Compositional GAN: Learning Conditional Image Composition

Paper content:
Generative Adversarial Networks (GANs) can produce images of surprising complexity and realism, but are generally structured to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene.
Capturing such complex interactions between different objects in the world, including their relative scaling, spatial layout, occlusion, or viewpoint transformation is a challenging problem.
In this work, we propose to model object composition in a GAN framework as a self-consistent composition-decomposition network.
Our model is conditioned on the object images from their marginal distributions and can generate a realistic image from their joint distribution.
We evaluate our model through qualitative experiments and user evaluations in scenarios when either paired or unpaired examples for the individual object images and the joint scenes are given during training.
Our results reveal that the learned model captures potential interactions between the two object domains given as input to output new instances of composed scene at test time in a reasonable fashion.
Generative Adversarial Networks (GANs) have emerged as a powerful method for generating images conditioned on a given input.
The input cue could be in the form of an image BID1 BID20 , a text phrase BID32 BID23 a; BID10 or a class label layout BID18 BID19 BID0 .
The goal in most of these GAN instantiations is to learn a mapping that translates a given sample from source distribution to generate a sample from the output distribution.
This primarily involves transforming either a single object of interest (apples to oranges, horses to zebras, label to image etc.), or changing the style and texture of the input image (day to night etc.).
However, these direct input-centric transformations do not directly capture the fact that a natural image is a 2D projection of a composition of multiple objects interacting in a 3D visual world.
In this work, we explore the role of compositionality in learning a function that maps images of different objects sampled from their marginal distributions (e.g., chair and table) into a combined sample (table-chair) that captures their joint distribution.Modeling compositionality in natural images is a challenging problem due to the complex interaction possible among different objects with respect to relative scaling, spatial layout, occlusion or viewpoint transformation.
Recent work using spatial transformer networks BID9 within a GAN framework BID14 decomposes this problem by operating in a geometric warp parameter space to find a geometric modification for a foreground object.
However, this approach is only limited to a fixed background and does not consider more complex interactions in the real world.
Another recent work on scene generation conditioned on text and a scene graph and explicitly provides reasoning about objects and their relations BID10 .We
develop a novel approach to model object compositionality in images. We
consider the task of composing two input object images into a joint image that captures their joint interaction in natural images. For
instance, given an image of a chair and a table, our formulation should be able to generate an image containing the same chair-table pair interacting naturally. For
a model to be able to capture the composition correctly, it needs to have the knowledge of occlusion ordering, i.e., a table comes in front of chair, and spatial layout, i.e., a chair slides inside table. To
the best of our knowledge, we are among the first to solve this problem in the image conditional space without any prior explicit information about the objects' layout.Our key insight is to reformulate the problem of composition of two objects into first composing the given object images to generate the joint combined image which models the object interaction, and then decomposing the joint image back to obtain individual ones. This
reformulation enforces a selfconsistency constraint ) through a composition-decomposition network. However
, in some scenarios, one does not have access to the paired examples of same object instances with their combined compositional image, for instance, to generate the joint image from the image of a given table and a chair, we might not have any example of that particular chair besides that particular table while we might have images of other chairs and other tables together. We add
an inpainting network to our composition-decomposition layers to handle the unpaired case as well.Through qualitative and quantitative experiments, we evaluate our proposed Compositional-GAN approach in two training scenarios: (a) paired
: when we have access to paired examples of individual object images with their corresponding composed image, (b) unpaired
: when we have a dataset from the joint distribution without being paired with any of the images from the marginal distributions.
In this paper, we proposed a novel Compositional GAN model addressing the problem of object composition in conditional image generation.
Our model captures the relative linear and viewpoint transformations needed to be applied on each input object (in addition to their spatial layout and occlusions) to generate a realistic joint image.
To the best of our knowledge, we are among the first to solve the compositionality problem without having any explicit prior information about object's layout.
We evaluated our compositional GAN through multiple qualitative experiments and user evaluations for two cases of paired versus unpaired training data.
In the future, we plan to extend this work toward generating images composed of multiple (more than two) and/or non-rigid objects.

assistant: text: We develop a novel approach to model object compositionality in images in a GAN framework.