title: Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image
    Synthesis
summary: 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 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 soft guidance, a method for imposing
  global semantics onto targeted regions without the use of any additional
  localization cues. Our integrated framework, 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/
paper_authors._id: 65a8bab30dba6b99a0fd765a
paper_authors.name: Jonghyun Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-02T08:00:28.189Z
paper_authors._id: 63834f5262badff4326ad517
paper_authors.avatarUrl: /avatars/a747bcf121abb55f6d403cb1b1fae537.svg
paper_authors.fullname: Jonghyun Lee
paper_authors.type: user
paper_authors.user: tomtom1103
paper_authors._id: 65a8bab30dba6b99a0fd765b
paper_authors.name: Hansam Cho
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-18T10:18:03.259Z
paper_authors._id: 6355c80a0d6e89270d495994
paper_authors.avatarUrl: /avatars/d25b0d5b47cb92cc6b498ef4e92f3f70.svg
paper_authors.fullname: Hansam Cho
paper_authors.type: user
paper_authors.user: Hansam
paper_authors._id: 65a8bab30dba6b99a0fd765c
paper_authors.name: Youngjoon Yoo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-18T10:19:05.738Z
paper_authors._id: 6582d1b85def15befa412a88
paper_authors.avatarUrl: /avatars/5104073d752a9ed1a57678dd70753790.svg
paper_authors.fullname: YoungJoon Yoo
paper_authors.type: user
paper_authors.user: yjyoo3312
paper_authors._id: 65a8bab30dba6b99a0fd765d
paper_authors.name: Seoung Bum Kim
paper_authors._id: 65a8bab30dba6b99a0fd765e
paper_authors.name: Yonghyun Jeong
paper_title: Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image
    Synthesis
paper_summary: 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 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 soft guidance, a method for imposing
  global semantics onto targeted regions without the use of any additional
  localization cues. Our integrated framework, 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/
paper_ai_keywords: diffusion models depth disentanglement training synthesis image triplets soft guidance disentangled representations global stylistic semantics localized objects