title: Jurassic World Remake: Bringing Ancient Fossils Back to Life via
    Zero-Shot Long Image-to-Image Translation
summary: With a strong understanding of the target domain from natural language, we
  produce promising results in translating across large domain gaps and bringing
  skeletons back to life. In this work, we use text-guided latent diffusion
  models for zero-shot image-to-image translation (I2I) across large domain gaps
  (longI2I), where large amounts of new visual features and new geometry need to
  be generated to enter the target domain. Being able to perform translations
  across large domain gaps has a wide variety of real-world applications in
  criminology, astrology, environmental conservation, and paleontology. In this
  work, we introduce a new task Skull2Animal for translating between skulls and
  living animals. On this task, we find that unguided Generative Adversarial
  Networks (GANs) are not capable of translating across large domain gaps.
  Instead of these traditional I2I methods, we explore the use of guided
  diffusion and image editing models and provide a new benchmark model,
  Revive-2I, capable of performing zero-shot I2I via text-prompting latent
  diffusion models. We find that guidance is necessary for longI2I because, to
  bridge the large domain gap, prior knowledge about the target domain is needed.
  In addition, we find that prompting provides the best and most scalable
  information about the target domain as classifier-guided diffusion models
  require retraining for specific use cases and lack stronger constraints on the
  target domain because of the wide variety of images they are trained on.
paper_authors._id: 64db23c200b80a024c5ba9e2
paper_authors.name: Alexander Martin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-08-16T13:22:33.801Z
paper_authors._id: 644ffb154f731658826945c8
paper_authors.avatarUrl: /avatars/46fa3a15c2b974692bee5b6ed6468be0.svg
paper_authors.fullname: Alex Martin
paper_authors.type: user
paper_authors.user: alexmartin1722
paper_authors._id: 64db23c200b80a024c5ba9e3
paper_authors.name: Haitian Zheng
paper_authors._id: 64db23c200b80a024c5ba9e4
paper_authors.name: Jie An
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-17T10:28:35.867Z
paper_authors._id: 6267fb1387d7f9040e031d39
paper_authors.avatarUrl: /avatars/c7170aa3d28d2cb851e075b98ca5f5b1.svg
paper_authors.fullname: Jie An
paper_authors.type: user
paper_authors.user: pkuanjie
paper_authors._id: 64db23c200b80a024c5ba9e5
paper_authors.name: Jiebo Luo
paper_title: Jurassic World Remake: Bringing Ancient Fossils Back to Life via
    Zero-Shot Long Image-to-Image Translation
paper_summary: With a strong understanding of the target domain from natural language, we
  produce promising results in translating across large domain gaps and bringing
  skeletons back to life. In this work, we use text-guided latent diffusion
  models for zero-shot image-to-image translation (I2I) across large domain gaps
  (longI2I), where large amounts of new visual features and new geometry need to
  be generated to enter the target domain. Being able to perform translations
  across large domain gaps has a wide variety of real-world applications in
  criminology, astrology, environmental conservation, and paleontology. In this
  work, we introduce a new task Skull2Animal for translating between skulls and
  living animals. On this task, we find that unguided Generative Adversarial
  Networks (GANs) are not capable of translating across large domain gaps.
  Instead of these traditional I2I methods, we explore the use of guided
  diffusion and image editing models and provide a new benchmark model,
  Revive-2I, capable of performing zero-shot I2I via text-prompting latent
  diffusion models. We find that guidance is necessary for longI2I because, to
  bridge the large domain gap, prior knowledge about the target domain is needed.
  In addition, we find that prompting provides the best and most scalable
  information about the target domain as classifier-guided diffusion models
  require retraining for specific use cases and lack stronger constraints on the
  target domain because of the wide variety of images they are trained on.
paper_ai_keywords: latent diffusion models zero-shot image-to-image translation (I2I) generative adversarial networks (GANs) guided diffusion image editing models classifier-guided diffusion models Skull2Animal longI2I Revive-2I domain gap prior knowledge scalable information target domain constraints