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Addressing the limitations of text as a source of accurate layoutrepresentation in text-conditional diffusion models, many works incorporateadditional signals to condition certain attributes within a generated image.Although successful, previous works do not account for the specificlocalization of said attributes extended into the three dimensional plane. Inthis context, we present a conditional diffusion model that integrates controlover three-dimensional object placement with disentangled representations ofglobal stylistic semantics from multiple exemplar images. Specifically, wefirst introduce \textit{depth disentanglement training} to leverage therelative depth of objects as an estimator, allowing the model to identify theabsolute positions of unseen objects through the use of synthetic imagetriplets. We also introduce \textit{soft guidance}, a method for imposingglobal semantics onto targeted regions without the use of any additionallocalization cues. Our integrated framework, \textsc{Compose and Conquer(CnC)}, unifies these techniques to localize multiple conditions in adisentangled manner. We demonstrate that our approach allows perception ofobjects at varying depths while offering a versatile framework for composinglocalized objects with different global semantics. Code:https://github.com/tomtom1103/compose-and-conquer/

Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis