I am currently a second-year Master’s student at the College of Computer Science and Artificial Intelligence, Fudan University.

My research interests lie in Multi-modal large models(MLLM) and Controllable image/video generation.

I am currently an Applied Research Intern at ByteDance, where I work on multimodal embedding models and post-training of multimodal large models.

News

• 2026.02: I joined ByteDance as an Applied Research Intern, working on MLLMs.
• 2025.10: I joined Meituan Intelligent Creation Team as a Research Intern.

Publications

Under Review

FUSE: Flow-aligned Universal Sketch Editor for DiT-based Generative Models

• Introduces a flow-aligned sketch-guided inpainting framework that improves background consistency and reduces boundary artifacts by aligning background latent trajectories during sampling.
• Uses Region-aware Sketch Control (RASC) to decouple sketch guidance for edited and background regions, improving sketch faithfulness and practical controllability across Flux and Z-Image backbones.

Under Review

SyncPainter: Caption-guided Video Inpainting with Multimodal Semantic Alignment

• Studies the logical distortion problem of current state-of-the-art video inpainting models under complex occlusion, and explores an automated restoration paradigm guided by MLLMs.
• Develops Mask-Aware Dual-Path Attention (MAAF), which decouples generation and reference streams for precise semantic-background alignment, together with latent-space temporal propagation for flow-free feature transfer and flicker reduction.

Educations

• 2024.09 - 2027.06, M.S. in Artificial Intelligence, Fudan University, Shanghai, China.
• 2020.10 - 2024.06, B.E. in Data Science and Big Data Technology, Zhejiang University of Technology, Hangzhou, China.

Internships

• ByteDance
  2026.02 - Present, Applied Research Intern, focusing on multimodal embedding models and post-training of multimodal large models.
• Meituan
  2025.10 - 2026.01, Research Intern, Intelligent Creation Team, focusing on multimodal algorithms.