Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image
  Synthesis
Jonghyun Lee Hansam Cho Youngjoon Yoo Seoung Bum Kim Yonghyun Jeong
Addressing the limitations of text as a source of accurate layout
representation in text-conditional diffusion models, many works incorporate
additional signals to condition certain attributes within a generated image.
Although successful, previous works do not account for the specific
localization of said attributes extended into the three dimensional plane. In
this context, we present a conditional diffusion model that integrates control
over three-dimensional object placement with disentangled representations of
global stylistic semantics from multiple exemplar images. Specifically, we
first introduce \textit{depth disentanglement training} to leverage the
relative depth of objects as an estimator, allowing the model to identify the
absolute positions of unseen objects through the use of synthetic image
triplets. We also introduce \textit{soft guidance}, a method for imposing
global semantics onto targeted regions without the use of any additional
localization cues. Our integrated framework, \textsc{Compose and Conquer
(CnC)}, unifies these techniques to localize multiple conditions in a
disentangled manner. We demonstrate that our approach allows perception of
objects at varying depths while offering a versatile framework for composing
localized objects with different global semantics. Code:
https://github.com/tomtom1103/compose-and-conquer/