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Junkun Chen

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3 papers

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NeurIPS Conference 2025 Conference Paper

Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image

Junkun Chen
Aayush Bansal
Minh Vo
Yu-Xiong Wang

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/.

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AAAI Conference 2018 Conference Paper

Meta Multi-Task Learning for Sequence Modeling

Junkun Chen
Xipeng Qiu
Pengfei Liu
Xuanjing Huang

Semantic composition functions have been playing a pivotal role in neural representation learning of text sequences. In spite of their success, most existing models suffer from the underﬁtting problem: they use the same shared compositional function on all the positions in the sequence, thereby lacking expressive power due to incapacity to capture the richness of compositionality. Besides, the composition functions of different tasks are independent and learned from scratch. In this paper, we propose a new sharing scheme of composition function across multiple tasks. Speciﬁcally, we use a shared meta-network to capture the meta-knowledge of semantic composition and generate the parameters of the taskspeciﬁc semantic composition models. We conduct extensive experiments on two types of tasks, text classiﬁcation and sequence tagging, which demonstrate the beneﬁts of our approach. Besides, we show that the shared meta-knowledge learned by our proposed model can be regarded as off-theshelf knowledge and easily transferred to new tasks.

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IJCAI Conference 2018 Conference Paper

Same Representation, Different Attentions: Shareable Sentence Representation Learning from Multiple Tasks

Renjie Zheng
Junkun Chen
Xipeng Qiu

Distributed representation plays an important role in deep learning based natural language processing. However, the representation of a sentence often varies in different tasks, which is usually learned from scratch and suffers from the limited amounts of training data. In this paper, we claim that a good sentence representation should be invariant and can benefit the various subsequent tasks. To achieve this purpose, we propose a new scheme of information sharing for multi-task learning. More specifically, all tasks share the same sentence representation and each task can select the task-specific information from the shared sentence representation with attention mechanisms. The query vector of each task's attention could be either static parameters or generated dynamically. We conduct extensive experiments on 16 different text classification tasks, which demonstrate the benefits of our architecture. Source codes of this paper are available on Github.

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