Taekyung Ki

I am a 2nd-year Ph.D. student at KAIST MLAI advised by Prof. Sung Ju Hwang. I am currently completing a research internship at Apple, working on multimodal diffusion models. Prior to this, I completed a three-year alternative military service in South Korea as a research scientist working on video generation. I received my M.S. and B.S. in Mathematics.

I am interested in the following research topics:

  • Generative Models
  • Audio-Visual Learning
  • Video-based World Models
  • World Action Models

I am open to research collaborations globally! Feel free to reach out to me if you are interested in any related topics.

Email  /  CV  /  Scholar  /  X  /  GitHub  /  HF  /  LinkedIn

profile photo

I am open to research collaborations globally! Feel free to reach out to me if you are interested in any related topics.

Email  /  CV  /  Scholar  /  X  /  GitHub  /  HF  /  LinkedIn

Highlights

  • [Jun. 2026 - Nov. 2026] Phd research intern at APPLE
  • [Aug. 2026] One paper accepted to EMNLP Findings 2026
  • [Apr. 2026] One paper accepted to ICML 2026
  • [Feb. 2026] One paper accepted to CVPR 2026
  • [Jan. 2026] One paper accepted to ICLR 2026
  • [Sep. 2025] Starting Ph.D. program at KAIST
  • [Jun. 2025] One paper accepted to ICCV 2025
  • [Mar. 2025] Joined KAIST MLAI as a researcher
  • [Jul. 2024] One paper accepted to ECCV 2024
  • [Jul. 2023] One paper accepted to ICCV 2023
  • [Mar. 2022 - Mar. 2025] Mandatory military service for South Korea



Experiences

Apple
AI/ML Ph.D. Research Intern
Research on multimodal diffusion models
Video Computer Vision (VCV) Team @ Apple
June 15, 2026 - November 30, 2026
Sunnyvale, CA, US

Publications

Highlighted, *: Equal contribution, †: Corresponding author

Demo
HINT-SD: Targeted Hindsight Self-Distillation for Long-Horizon Agents
Woongyeng Yeo*, Yumin Choi*, Taekyung Ki, Sung Ju Hwang†
Empirical Methods in Natural Language Processing (EMNLP Findings), 2026
arXiv / Code / Hugging Face

Targeted self-distillation framework that selectively distills failure-relevant actions from full trajectories to train long-horizon agent effectively.

Demo
Self-Refining Video Sampling
Sangwon Jang*, Taekyung Ki*, Jaehyeong Jo*, Saining Xie, Jaehong Yoon†, Sung Ju Hwang†
International Conference on Machine Learning (ICML), 2026
Project Page / arXiv / Paper / Code / Hugging Face

Improving physical plausibility in video generation without any additional training or external verifiers.

Demo
Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation
Taekyung Ki*, Sangwon Jang*, Jaehyeong Jo, Jaehong Yoon, Sung Ju Hwang†
Conference on Computer Vision and Pattern Recognition (CVPR), 2026
Project Page / arXiv / Paper / Video / Code / Hugging Face

User-interactive head generation model based on motion latent diffusion forcing and a direct preference optimization method for interactive motion.

Demo

Frame-level controllable training-free guidance method for large video diffusion models on a single GPU.

Demo
FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait
Taekyung Ki, Dongchan Min, Gyeongsu Chae
International Conference on Computer Vision (ICCV), 2025
Audio-Visual Generation & Learning Workshop at ICCV (AVGen@ICCV), 2025
Project Page / arXiv / Paper / Code / Hugging Face

Motion latent flow matching for real-time audio-driven talking portrait generation and editing using the learned orthonormal motion basis.

Demo
Learning to Generate Conditional Tri-plane for 3D-aware Expression Controllable Portrait Animation
Taekyung Ki, Dongchan Min, Gyeongsu Chae
European Conference on Computer Vision (ECCV), 2024
Project Page / arXiv / Paper / Supp

Contrastive pre-training framework for appearance-free facial expression hidden in 3DMM and 3D-aware expression-controllable portrait animation model.

Demo
StyleLipSync: Style-based Personalized Lip-sync Video Generation
Taekyung Ki*, Dongchan Min*
International Conference on Computer Vision (ICCV), 2023
Project Page / arXiv / Paper / Code / Supp

StyleLipSync generates generalized and person-agnostic audio-lip synchronized videos by leveraging the strong facial prior of style-based generator.

Demo
Deep Scattering Network with Max-pooling
Taekyung Ki, Youngmi Hur†
IEEE Data Compression Conference (DCC), 2021
Paper / Code

Proved that pooling operators allow scattering networks to extract translation-invariant features.


Talks

Demo
Motion Latent Flow Matching for Real-time Audio-driven Talking Portrait
Pika Labs
Host: Chenlin Meng (CTO)
August 2025


Awards and Honors

  • [Oct. 2021] 1st Prize Winner, NLP-based Math-Word Problem Track in 2021 AI Grand Challenge (AGC), funded by South Korea Ministry of Science and ICT.
  • [Feb. 2019] Graduated with top honors, ranked 1st in Department of Mathematics.


Academic Services

  • Conference Reviewer: CVPR 2025-2026, NeurIPS 2026, ICCV 2025, ECCV 2026, SIGGRAPH ASIA 2026, ACL ARR 2026, BMVC 2026, AAAI 2027

Lastly updated in November 9, 2026. This page is based on Jon Barron's website template.