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Junkun Chen

dblp:215/4874 · DBLP profile ↗
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4ranked-venue papers
2as first author
4since 2021 · last 2026
0009-0008-6701-0058ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Systems, architecture and hardware · 2 · 1 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Generative modeling · 100%
Computer graphics and multimedia
1 paper
Visual content generation and editing · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Generative modeling
autoregressive model
0.912025
Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image · NeurIPS 2025
Machine learning › Generative modeling › video generation
autoregressive video generation
0.912025
Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image · NeurIPS 2025
Visual content generation and editing › video generation
long video generation
0.912025
Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image · NeurIPS 2025
Visual content generation and editing
video generation
0.912025
Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image · NeurIPS 2025

Methods — techniques the papers use, named apart from their topics

prefix video conditioning · 1.7autoregressive segment-by-segment generation · 1.7anchor video conditioning · 1.7
YearPublicationVenuePosition
2026 SEA: Hierarchically searching efficient adapters for pre-trained models
Shun Lu 0001, Fangyuan Mao, Junkun Chen, Jilin Mei, Yu Hu 0001
Neural Networks3
2025 Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image
abstract
This paper proposes Virtual Fitting Room (VFR), a novel video generative model that produces arbitrarily long virtual try-on videos. Our VFR models long video generation tasks as an auto-regressive, segment-by-segment generation process, eliminating the need for resource-intensive generation and lengthy video data, while providing the flexibility to generate videos of arbitrary length. The key challenges of this task are twofold: ensuring local smoothness between adjacent segments and maintaining global temporal consistency across different segments. To address these challenges, we propose our VFR framework, which ensures smoothness through a prefix video condition and enforces consistency with the anchor video — a 360°-view video that comprehensively captures the human's whole-body appearance. Our VFR generates minute-scale virtual try-on videos with both local smoothness and global temporal consistency under various motions, making it a pioneering work in long virtual try-on video generation. Project Page: https://immortalco.github.io/VirtualFittingRoom/.
Junkun Chen, Aayush Bansal, Minh Vo, Yu-Xiong Wang
NeurIPS1
2024 A 0.5-V 0.02% THD Bulk-Driven OTA for Continuous-Time Applications in 180 nm CMOS
abstract
This paper introduces a 0.5-V, two-stage, pseudo-differential bulk-driven operational transconductance amplifier with high gain and linearity for low-power continuous-time applications. The input stage’s common-mode feedback utilizes a linear resistor detector with a conductance reduction cross-coupled pair to mitigate the loading effect of the common-mode detector resistor at ultra-low power operations. Temperature dependence of the conductance reduction circuit is compensated. The impact of the conductance reduction circuit on linearity performance is explored. The output stage employs a class-AB topology and utilizes a phase lead compensator to stabilize the OTA. Implemented in 180 nm CMOS technology, this OTA achieves a DC gain and slew rate of 68 dB and 26.1 V/$\mu $s, respectively, under a capacitive load of 10 pF. The total power consumption is$34.2~\mu $W. With unit gain feedback configuration, the measured total harmonic distortion is only 0.02% at an output amplitude of 500 mVpp. Test results validate the proposed circuit, positioning it competitively compared to state-of-the-art designs.
Yangxin Xiang, Huajun Yao, Junkun Chen, Yongzhen Chen, Jiangfeng Wu
IEEE Trans. Circuits Syst. I Regul. Pap.4
2023 A 4.75-64 Gb/s PAM-4 Wireline Transmitter with 3-tap FFE in 28-nm CMOS
abstract
This paper presents a reconfigurable 4.75-to-32 GBuad transmitter (TX) that operates up to 64Gb/s with four-level pulse-amplitude modulation (PAM-4) and at 32Gb/s with non-return-to-zero (NRZ) modulation scheme, designed in the 28-nm CMOS technology. The TX incorporates a quarter-rate architecture with a tap coefficient flexible feed-forward equalizer (FFE) up to 3 FFE taps. The TX employs a tailless CML driver with common-mode feedback (CMFB) for output swing control which provides 0.8 V pp output swing, and a helical wiring T -coil for bandwidth expansion. The clock path of the TX includes the duty-cycle detection/correction (DCD/DCC) circuits with a resolution of sub-60fs and quadrature-error detection/correction (QED/QEC) circuits, and a LC phase locked loop (PLL) with a local injection-locked (IL) quadrature clock generation circuit. The TX operating at 64 Gb/s in PAM-4 modulation consumes 76.7 mW from 1-V supply with 0.8 V pp, achieving an 1.2 pJ/b energy efficiency. The TX front end occupies an active area of 0.063 mm2.
Junkun Chen, Youzhi Gu, Miaomiao Xu, Yongzhen Chen, Cuixia Wang, Jiangfeng Wu
ISCAS1