Yeji Song

Research Scientist

ldynx.github.io/

https://github.com/ldynx

ldynx@snu.ac.kr

I am a Ph.D student at Seoul National University, under the supervision of Prof. Nojun Kwak. My primary focus is on video & image generation, aiming to push the boundaries of their applications in real-world scenarios. Specifically, developing generative models that provide more diverse experiences to users is my central goal. My research interests also include a broader computer vision area, with experience spanning diffusion, video rendering, segmentation, and 3D object detection.

Education

Seoul National University
Ph. D in Computer Science, advised by Prof. Nojun Kwak.
Supported by LG Electronics Scholarship 2023-2025
Mar 2020 - Current
Seoul National University
B.S. in Statistics and Psychology (double-major).
Mar 2013 - Feb 2020

Employment

NAVER Webtoon
Applied AI Intern on Webtoon AI team. Worked on Semantic Segmentation for Webtoon Platform.
Jan 2023 - Jul 2023
Korea

Publications

Image & Video Generation

Harmonizing Visual and Textual Embeddings for Zero-Shot Text-to-Image Customization
Y. Song, J. Kim * , W. Park * , W. Shin, W. Rhee, N. Kwak
There is a conflict among contextual embeddings in zero-shot T2I customization when varying the subject's pose. We resolve it by orthogonalization and attention swap.
AAAI, 2025
SAVE: Protagonist Diversification with Structure Agnostic Video Editing
Y. Song, W. Shin, J. Lee, J. Kim, N. Kwak
We adopt a personalization framework for video editing tasks, isolating the motion as a concept from a source video and subsequently modifying the protagonist as a novel context.
ECCV, 2024
Towards Efficient Neural Scene Graphs by Learning Consistency Fields
Y. Song, C. Kong, S. Lee, N. Kwak * , J. Lee *
In video scene rendering, we reformulate neural radiance fields to additionally consider consistency fields, enabling more efficient and controllable scene manipulation.
BMVC, 2022

Safe Generative Models

Targeted Data Protection for Diffusion Model by Matching Training Trajectory
H. Lee, M. Koo, Y. Song, N. Kwak
We aim to prevent unauthorized T2I customization by guiding the weight trajectory of protected data to ensure it outputs only the precise targeted image.
AAAI Workshop, 2025
Exploring Causal Mechanisms for Machine Text Detection Methods
K. Yoo, W. Ahn, Y. Song, N. Kwak
With the increasing importance of discriminating machine-text from human text, we show the existence of backdoor path that confounds the relationships between text and its detection score.
NAACL Workshop, 2024

Model Efficiency

Finding Efficient Pruned Network via Refined Gradients for Pruned Weights
J. Kim, J. Yoo * , Y. Song * , K. Yoo, N. Kwak
We advance dynamic pruning by employing refined gradients to update the pruned weights, enhancing both training stability and the model performance.
ACM MM, 2023

3D Object Detection

Md3d: Mixture-density-based 3d object detection in point clouds
J. Choi, Y. Song, Y. Kim, J. Yoo, N. Kwak
We utilize the Gaussian Mixture Model (GMM) in the 3D object detection task to predict the distribution of 3D bounding boxes, eliminating the need for laborious, hand-crafted anchor design.
IEEE Access, 2022
Part-aware data augmentation for 3d object detection in point cloud
J. Choi, Y. Song, N. Kwak
We delve into data augmentation in 3D object detection, leveraging sophisticated and rich structural information present in 3D labels.
IROS, 2021

Projects

Video Generation

Korean traditional art style transfer
Ministry of Culture, Sports and Tourism (MCST)
• Aimed to generate images that faithfully follow specific traditional Korean art styles.
• Viewing styles within the customization framework, we adopted and optimized unique style tokens.
Apr 2024 - Dec 2024
Video outpainting for Media Art
Easywith Co., Ltd.
• Tackled the physical limitations in the Media Art Exhibition derived from exhibition space which has a different size and ratio from artwork.
• Adopting the diffusion-based video method, we outpainted the artworks to match the size and ratio of the exhibition space.
Nov 2023 - Feb 2024

Segmentation & Detection

Semantic segmentation for Webtoon images
Naver Webtoon Corp.
• Rearranging balloons and typesets is necessary during webtoon translation, and proper segmentation for these elements becomes essential.
• Took charge of the entire development process of the webtoon segmentation model, from enhancing data augmentation schemes to building a demo page.
Jan 2023 - Mar 2023
Development of object detector for Fashion AI Chatbot
Ministry of Science, and Information and Communication Technologies (ICT)
• Participated in developing an AI chatbot that facilitates multimodal conversations about fashion, engaging in both text and images.
• Contributed to implementing an object detector capable of analyzing the user-provided images, capturing clothing items, and extracting critical features.
Jan 2022 - Dec 2022
Unmanned outdoor security robot
Samsung Electronics Co., Ltd.
• Participated in a project aimed at creating an autonomous outdoor security robot capable of independent navigation and object detection.
Jun 2021 - Dec 2021

Reviewer

• CVPR 2025
• ACM MM 2023 - 2025

Teaching

• TA, Introduction to Diffusion-based Generative Models for The AI Lab (Feb 2024)
• TA, Linear Algebra for Hyundai Heavy Industries. (Mar 2022 - Jun 2024)
• TA, Mathematics for Intelligent Systems (Mar 2023 - Jun 2023)