  Recent approaches have achieved great success in image generation from
structured inputs, e.g., semantic segmentation, scene graph or layout. Although
these methods allow specification of objects and their locations at
image-level, they lack the fidelity and semantic control to specify visual
appearance of these objects at an instance-level. To address this limitation,
we propose a new image generation method that enables instance-level attribute
control. Specifically, the input to our attribute-guided generative model is a
tuple that contains: (1) object bounding boxes, (2) object categories and (3)
an (optional) set of attributes for each object. The output is a generated
image where the requested objects are in the desired locations and have
prescribed attributes. Several losses work collaboratively to encourage
accurate, consistent and diverse image generation. Experiments on Visual Genome
dataset demonstrate our model's capacity to control object-level attributes in
generated images, and validate plausibility of disentangled object-attribute
representation in the image generation from layout task. Also, the generated
images from our model have higher resolution, object classification accuracy
and consistency, as compared to the previous state-of-the-art.
