Generation Module Results

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Overall Process

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1. Object Asset Generation (gen_assets.py)

객체의 마스크, 에지, 중심 편향 등을 활용해 Score Map을 생성하고 오브젝트별 에셋 생성

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2. Facial Modification (mod_facial.py)

생성된 객체에 다양한 표정 및 감정을 반영

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3. Human Character Generation (gen_human.py)

객체와 연결된 캐릭터를 사람 형태로 변환

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4. Face Generation (gen_face_v2.py)

생성된 얼굴을 여러 감정 상태로 다양하게 표현

References

gen_face

@misc{zhang2023adding, 
  title={Adding Conditional Control to Text-to-Image Diffusion Models}, 
  author={Lvmin Zhang and Maneesh Agrawala}, 
  year={2023}, 
  eprint={2302.05543}, 
  archivePrefix={arXiv}, 
  primaryClass={cs.CV} 
}

@misc{rombach2021highresolution,
  title={High-Resolution Image Synthesis with Latent Diffusion Models}, 
  author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
  year={2021},
  eprint={2112.10752},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

gen_object, mod_facial, gen_human

@misc{podell2023sdxlimprovinglatentdiffusion,
  title={SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis}, 
  author={Dustin Podell and Zion English and Kyle Lacey and Andreas Blattmann and Tim Dockhorn and Jonas Müller and Joe Penna and Robin Rombach},
  year={2023},
  eprint={2307.01952},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2307.01952}, 
}

@misc{ye2023ipadaptertextcompatibleimage,
  title={IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models}, 
  author={Hu Ye and Jun Zhang and Sibo Liu and Xiao Han and Wei Yang},
  year={2023},
  eprint={2308.06721},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2308.06721}, 
}