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Junsoo Lee

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AAAI Conference 2024 Conference Paper

DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models

  • Namhyuk Ahn
  • Junsoo Lee
  • Chunggi Lee
  • Kunhee Kim
  • Daesik Kim
  • Seung-Hun Nam
  • Kibeom Hong

Recent progresses in large-scale text-to-image models have yielded remarkable accomplishments, finding various applications in art domain. However, expressing unique characteristics of an artwork (e.g. brushwork, colortone, or composition) with text prompts alone may encounter limitations due to the inherent constraints of verbal description. To this end, we introduce DreamStyle, a novel framework designed for artistic image synthesis, proficient in both text-to-image synthesis and style transfer. DreamStyle optimizes a multi-stage textual embedding with a context-aware text prompt, resulting in prominent image quality. In addition, with content and style guidance, DreamStyle exhibits flexibility to accommodate a range of style references. Experimental results demonstrate its superior performance across multiple scenarios, suggesting its promising potential in artistic product creation. Project page: https://nmhkahn.github.io/dreamstyler/

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AAAI Conference 2021 Conference Paper

Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation

  • Sunghyun Park
  • Kangyeol Kim
  • Junsoo Lee
  • Jaegul Choo
  • Joonseok Lee
  • Sookyung Kim
  • Edward Choi

Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e. g. , increasing the frame rate of the more dynamic portion of the video as well as handling missing video frames). To resolve the restricted nature of existing video generation models’ ability to handle arbitrary timesteps, we propose continuous-time video generation by combining neural ODE (Vid-ODE) with pixellevel video processing techniques. Using ODE-ConvGRU as an encoder, a convolutional version of the recently proposed neural ODE, which enables us to learn continuous-time dynamics, Vid-ODE can learn the spatio-temporal dynamics of input videos of flexible frame rates. The decoder integrates the learned dynamics function to synthesize video frames at any given timesteps, where the pixel-level composition technique is used to maintain the sharpness of individual frames. With extensive experiments on four real-world video datasets, we verify that the proposed Vid-ODE outperforms state-ofthe-art approaches under various video generation settings, both within the trained time range (interpolation) and beyond the range (extrapolation). To the best of our knowledge, Vid- ODE is the first work successfully performing continuous-time video generation using real-world videos.

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