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Yi-Chuan Huang 黃怡川

Ph.D. Student · Department of Computer Science
National Yang Ming Chiao Tung University (NYCU), Taiwan

I am a third-year Ph.D. student in Computer Science at National Yang Ming Chiao Tung University (NYCU), advised by Prof. Yu-Lun Liu at the Computational Photography Lab. My research focused on 3D Computer Vision and Generative Models.

Outside of work and research, I enjoy cooking🧑‍🍳, caring for animals🐱, and working out🏃.

Research Interests

Computer Vision 3D Reconstruction Neural Radiance Fields (NeRF) 3D Gaussian Splatting Multi-view Diffusion Model Novel View Synthesis Generative AI Object Generation & Editing

News

  • Nov. 2025
    Paper accepted to WACV 2025: “Splannequin”. 🎉
  • Jun. 2025
    Served as a reviewer for Pacific Graphics 2025 (PG 2025).
  • Mar. 2025
    Paper accepted to CVPR 2025: “AuraFusion360”. 🎉
  • Sep. 2024
    Awarded Outstanding Teaching Assistant for the course Signals and Systems. 📶
  • Jun. 2024
    Passed Ph.D. Qualification and officially became a Ph.D. Candidate . 🧑‍🎓
  • Sep. 2023
    Joined the Computational Photography Lab at NYCU as a Ph.D. student. 📖

Publications

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Splannequin: Freezing Monocular Mannequin-Challenge Footage with Dual-Detection Splatting

Hao-Jen Chien, Yi-Chuan Huang, Chung-Ho Wu, Wei-Lun Chao, Yu-Lun Liu
WACV 2025

Splannequin freezes dynamic Gaussian splats into crisp 3D scenes from monocular videos by anchoring artifacts to more reliable temporal states.

AuraFusion360: Augmented Unseen Region Alignment for Reference-based 360° Unbounded Scene Inpainting

Chung-Ho Wu*, Yang-Jung Chen*, Ying-Huan Chen, Jie‑Ying Lee, Bo-Hsu Ke, Chun-Wei Tuan Mu, Yi-Chuan Huang, Chin-Yang Lin, Min-Hung Chen, Yen-Yu Lin, Yu-Lun Liu (*Equal Contribution) ·
CVPR 2025

Introduces depth-aware unseen mask generation, Adaptive Guided Depth Diffusion (zero-shot), and SDEdit-based detail enhancement for multi-view coherence.

Projects

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Knowledge Distillation for Parameter-Efficient Large Language Models

Distilled knowledge from LLaMA-3.2-3B-Instruct into a smaller LLaMA-3.2-1B-Instruct model, using WikiText-2 and combined KL/MSE loss, achieving a perplexity of 11.72 on the student model.

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Layered Vectorization of Natural Images for Editable SVG Graphics

Converted natural images into layered SVGs for intuitive and editable graphics. Achieved structure-preserving vectorization for AI-assisted design and editing.

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