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Jingwen Chen

10 accepted papers

2026

Beyond Wide-Angle Images: Structure-to-Detail Video Portrait Correction via Unsupervised Spatiotemporal Adaptation

AAAI 2026technical

Wide-angle cameras, despite their popularity for content creation, suffer from distortion-induced facial stretching—especially at the edge of the lens—which degrades visual appeal. To address this issue, we propose a structure-to-detail portrait correction model named ImagePC. It integrates the long

Cited by 0SourcePDFScholar
2026

DREAMVAR: TAMING REINFORCED VISUAL AUTOREGRESSIVE MODEL FOR HIGH-FIDELITY SUBJECT-DRIVEN IMAGE GENERATION

ICASSP 2026poster

Recent advances in subject-driven image generation using diffusion models have attracted considerable attention for their remarkable capabilities in producing high-quality images. Nevertheless, the potential of Visual Autoregressive (VAR) models, despite their unified architecture and efficient infe…

Cited by 0SourcePDFScholar
2025

Incorporating Visual Correspondence into Diffusion Model for Virtual Try-On

ICLR 2025poster

Diffusion models have shown preliminary success in virtual try-on (VTON) task. The typical dual-branch architecture comprises two UNets for implicit garment deformation and synthesized image generation respectively, and has emerged as the recipe for VTON task. Nevertheless, the problem remains chall…

2025

VTON-VLLM: Aligning Virtual Try-On Models with Human Preferences

NeurIPS 2025poster

Diffusion models have yielded remarkable success in virtual try-on (VTON) task, yet they often fall short of fully meeting user expectations regarding visual quality and detail preservation. To alleviate this issue, we curate a dataset of synthesized VTON images annotated with human judgments across…

Cited by 0SourcecodeScholar
2024

Improving Text-guided Object Inpainting with Semantic Pre-inpainting

ECCV 2024poster

"Recent years have witnessed the success of large text-to-image diffusion models and their remarkable potential to generate high-quality images. The further pursuit of enhancing the editability of images has sparked significant interest in the downstream task of inpainting a novel object described b…

2024

Improving Virtual Try-On with Garment-focused Diffusion Models

ECCV 2024poster

"Diffusion models have led to the revolutionizing of generative modeling in numerous image synthesis tasks. Nevertheless, it is not trivial to directly apply diffusion models for synthesizing an image of a target person wearing a given in-shop garment, i.e., image-based virtual try-on (VTON) task. T…

2024

Unleashing Text-to-Image Diffusion Prior for Zero-Shot Image Captioning

ECCV 2024poster

"Recently, zero-shot image captioning has gained increasing attention, where only text data is available for training. The remarkable progress in text-to-image diffusion model presents the potential to resolve this task by employing synthetic image-caption pairs generated by this pre-trained prior.…

Cited by 0SourcePDFScholar
2021

Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network

AAAI 2021technical

Despite having impressive vision-language (VL) pretraining with BERT-based encoder for VL understanding, the pretraining of a universal encoder-decoder for both VL understanding and generation remains challenging. The difficulty originates from the inherently different peculiarities of the two disci…

2019

Social Relation Recognition From Videos via Multi-Scale Spatial-Temporal Reasoning

CVPR 2019poster

Discovering social relations, e.g., kinship, friendship, etc., from visual contents can make machines better interpret the behaviors and emotions of human beings. Existing studies mainly focus on recognizing social relations from still images while neglecting another important media--video. On one h…

Cited by 94PDFScholar
2018

Image Blind Denoising With Generative Adversarial Network Based Noise Modeling

CVPR 2018poster

In this paper, we consider a typical image blind denoising problem, which is to remove unknown noise from noisy images. As we all know, discriminative learning based methods, such as DnCNN, can achieve state-of-the-art denoising results, but they are not applicable to this problem due to the lack of…

Cited by 728SourcePDFScholar