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Junfeng Wu

Huazhong University of Science and Technology

4 papers hereh-index 8704 citations13 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV4
affiliations
  • Huazhong University of Science and Technology
HomepageORCID 0000-0003-3453-7140
same name
  • Junfeng Wu — 7 papers, h 2
  • Junfeng Wu — 5 papers, h 1
  • Junfeng Wu — 4 papers, h 1
  • Junfeng Wu — 3 papers, h 2
  • Junfeng Wu — 1 paper, h 1
  • Junfeng Wu — 1 paper, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

works on
autoencoders 1diffusion models 1representation learning 1video foundation models 1video generation 1

From the 1 of 4 linked papers with an AI index.

collaborators

4 papers

cs.CV2026

VideoRAE: Taming Video Foundation Models for Generative Modeling via Representation Autoencoders

Zhihao Xie, Junfeng Wu, Xinting Hu +2

VideoRAE is a representation autoencoder that leverages frozen video foundation model features to create compact, generation‑friendly video latents, supporting both continuous diff…

cs.CV2025

UniTok: A Unified Tokenizer for Visual Generation and Understanding

Chuofan Ma, Yi Jiang, Junfeng Wu +5

Visual generative and understanding models typically rely on distinct tokenizers to process images, presenting a key challenge for unifying them within a single framework. Recent s…

cs.CV2025

TokBench: Evaluating Your Visual Tokenizer before Visual Generation

Junfeng Wu, Dongliang Luo, Weizhi Zhao +6

In this work, we reveal the limitations of visual tokenizers and VAEs in preserving fine-grained features, and propose a benchmark to evaluate reconstruction performance for two ch…

cs.CV2025

Liquid: Language Models are Scalable and Unified Multi-modal Generators

Junfeng Wu, Yi Jiang, Chuofan Ma +5

We present Liquid, an auto-regressive generation paradigm that seamlessly integrates visual comprehension and generation by tokenizing images into discrete codes and learning these…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.