Yeongtak Oh

I am a third year Ph.D student at DSAIL Lab in Seoul National University, Seoul, South Korea. I am working on computer vision and machine learning.

I received a BS in mechanical engineering from Seoul National University, Seoul, Korea, in 2018. I received an MS in the same department at SNU, in 2020. I worked as a Military Science and Technology Researcher in 2021 at the Korea Military Academy AI R&D Center.

My current research topics include generative models, continual learning, and vision-language models.

Email  /  CV  /  Google Scholar  /  Github

ControlDreamer enables high-quality 3D generation with creative geometry and styles via multi-view ControlNet.

We propose Decorruptor to enhance the robustness of the diffusion model and accelerate the diffusion-based image-level updates.

We have enhanced a range of CLIP-guided image morphing baselines through the implementation of our proposed inter- and intra-modality regularization losses, effectively addressing the SP dilemma.

We present a deep learning-based motion-adaptive fault detection method for industrial robots using torque ripples.

We propose the Style-friendly SNR sampler, which aggressively shifts the signal-to-noise ratio (SNR) distribution toward higher noise levels during fine-tuning to focus on noise levels where stylistic features emerge.

This page is borrowed from Jon Barron's webpage.