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Jingyu Yang

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13 papers

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AAAI Conference 2026 Conference Paper

InterCoser: Interactive 3D Character Creation with Disentangled Fine-Grained Features

Yi Wang
Jian Ma
Zhuo Su
Guidong Wang
Jingyu Yang
Yu-Kun Lai
Kun Li

This paper aims to interactively generate and edit disentangled 3D characters based on precise user instructions. Existing methods generate and edit 3D characters via rough and simple editing guidance and entangled representations, making it difficult to achieve precise and comprehensive control over fine-grained local editing and free clothing transfer for characters. To enable accurate and intuitive control over the generation and editing of high-quality 3D characters with freely interchangeable clothing, we propose a novel user-interactive approach for disentangled 3D character creation. Specifically, to achieve precise control over 3D character generation and editing, we introduce two user-friendly interaction approaches: a sketch-based layered character generation/editing method, which supports clothing transfer; and a 3D-proxy-based part-level editing method, enabling fine-grained disentangled editing. To enhance 3D character quality, we propose a 3D Gaussian reconstruction strategy guided by geometric priors, ensuring that 3D characters exhibit detailed local geometry and smooth global surfaces. Extensive experiments on both public datasets and in-the-wild data demonstrate that our approach not only generates high-quality disentangled 3D characters but also supports precise and fine-grained editing through user interaction.

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YNIMG Journal 2025 Journal Article

Functional divisions of the left anterior and posterior temporoparietal junction for phonological and semantic processing in Chinese character reading

Aqian Li
Chuansheng Chen
Yuan Feng
Rui Hu
Xiaoxue Feng
Jingyu Yang
Xingying Lin
Leilei Mei

Previous studies have shown that the left temporoparietal junction (TPJ) plays a critical role in word reading. Nevertheless, there is still controversy surrounding the phonological and semantic functions of the left TPJ. The parietal unified connectivity-biased computation (PUCC) model posits that the function of the left TPJ depends on both the neurocomputation of this local area and its long-range connectivity. To clarify the specific roles of different TPJ subregions in phonological and semantic processing of Chinese characters, the present study used connectivity-based clustering to identify seven subdivisions within the left TPJ, and conducted comprehensive analyses including functional and structural connectivity, univariate and multivariate analyses (i.e., representational similarity analysis, RSA) on multimodal imaging data (task-state fMRI, resting-state fMRI, and diffusion-weighted imaging [DWI]). Functional and structural connectivity analyses revealed that the left anterior TPJ had stronger connections with the phonological network, while the left posterior TPJ had stronger connections with the semantic network. RSA revealed that the left anterior and posterior TPJ represented phonological and semantic information of Chinese characters, respectively. More importantly, the phonological and semantic representations of the left TPJ were respectively correlated with its functional connectivity to the phonological and semantic networks. Altogether, our results provide a more elaborate perspective on the functional dissociation of the left anterior and posterior TPJ in phonological and semantic processing of Chinese characters, and support the PUCC model.

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TIST Journal 2025 Journal Article

JASRNet: Learning Joint Adaptive Sampling and Reconstruction for Depth Sensing

Chunyang Bi
Mingnuo Teng
Tianhao Xie
Kun Li
Jingyu Yang

Recent attempts to exploit irregular sampling strategies for depth sensing have shown prominent merits over the uniform rectangular sampling in terms of depth reconstruction quality, particularly at low sampling rates. However, the separate treatment of depth sampling and reconstruction did not enjoy potential merits of joint optimization. In this article, we propose a joint adaptive depth sampling and reconstruction network, named JASRNet, for the RGB-D sensing configuration, to simultaneously optimize both the sampling and reconstruction of the depth information in an end-to-end manner. The sampling sub-network infers the locations to sample according to the significance distribution generated from the associated RGB image without any prior information of the underlying depth maps. The depth reconstruction sub-network learns and then fuses global and local depth features with attention guidance, which helps to obtain more accurate depth reconstruction results at boundaries. A hybrid loss function is further proposed to promote sharp discontinuities of the reconstructed depth maps. The qualitative and quantitative results show that our method achieves better depth sensing quality than several state-of-the-art methods for various indoor and outdoor scenes.

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NeurIPS Conference 2025 Conference Paper

Learning Differential Pyramid Representation for Tone Mapping

Qirui Yang
Yinbo Li
Yihao Liu
Peng-tao Jiang
Fangpu Zhang
cheng qihua
Huanjing Yue
Jingyu Yang

Existing tone mapping methods operate on downsampled inputs and rely on handcrafted pyramids to recover high-frequency details. Existing tone mapping methods operate on downsampled inputs and rely on handcrafted pyramids to recover high-frequency details. These designs typically fail to preserve fine textures and structural fidelity in complex HDR scenes. Furthermore, most methods lack an effective mechanism to jointly model global tone consistency and local contrast enhancement, leading to globally flat or locally inconsistent outputs such as halo artifacts. We present the Differential Pyramid Representation Network (DPRNet), an end-to-end framework for high-fidelity tone mapping. At its core is a learnable differential pyramid that generalizes traditional Laplacian and Difference-of-Gaussian pyramids through content-aware differencing operations across scales. This allows DPRNet to adaptively capture high-frequency variations under diverse luminance and contrast conditions. To enforce perceptual consistency, DPRNet incorporates global tone perception and local tone tuning modules operating on downsampled inputs, enabling efficient yet expressive tone adaptation. Finally, an iterative detail enhancement module progressively restores the full-resolution output in a coarse-to-fine manner, reinforcing structure and sharpness. Experiments show that DPRNet achieves state-of-the-art results, improving PSNR by 2. 39 dB on the 4K HDR+ dataset and 3. 01 dB on the 4K HDRI Haven dataset, while producing perceptually coherent, detail-preserving results. Demo available at DPRNet.

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AAAI Conference 2025 Conference Paper

Zero-shot Video Restoration and Enhancement Using Pre-Trained Image Diffusion Model

Cong Cao
Huanjing Yue
Xin Liu
Jingyu Yang

Diffusion-based zero-shot image restoration and enhancement models have achieved great success in various tasks of image restoration and enhancement. However, directly applying them to video restoration and enhancement results in severe temporal flickering artifacts. In this paper, we propose the first framework for zero-shot video restoration and enhancement based on the pre-trained image diffusion model. By replacing the spatial self-attention layer with the proposed short-long-range (SLR) temporal attention layer, the pre-trained image diffusion model can take advantage of the temporal correlation between frames. We further propose temporal consistency guidance, spatial-temporal noise sharing, and an early stopping sampling strategy to improve temporally consistent sampling. Our method is a plug-and-play module that can be inserted into any diffusion-based image restoration or enhancement methods to further improve their performance. Experimental results demonstrate the superiority of our proposed method.

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IJCAI Conference 2024 Conference Paper

Efficient Screen Content Image Compression via Superpixel-based Content Aggregation and Dynamic Feature Fusion

Sheng Shen
Huanjing Yue
Jingyu Yang

This paper addresses the challenge of efficiently compressing screen content images (SCIs) – computer generated images with unique attributes such as large uniform regions, sharp edges, and limited color palettes, which pose difficulties for conventional compression algorithms. We propose a Superpixel-based Content Aggregation Block (SCAB) to aggregate local pixels into one super-pixel and aggregate non-local information via super-pixel transformer. Such aggregation enables the dynamic assimilation of non-local information while maintaining manageable complexity. Furthermore, we enhance our channel-wise context entropy model with a Dynamic Feature Fusion (DFF) mechanism. This mechanism integrates decoded slices and side information dynamically based on their global correlation, allowing the network to dynamically learn the optimal weights for global information usage. Extensive experiments on three SCI datasets (SCID, CCT, and SIQAD) show our method’s superior RD performance and inference time, making it the first network comparable with the advanced VVC-SCC standard.

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AAAI Conference 2024 Conference Paper

KeDuSR: Real-World Dual-Lens Super-Resolution via Kernel-Free Matching

Huanjing Yue
Zifan Cui
Kun Li
Jingyu Yang

Dual-lens super-resolution (SR) is a practical scenario for reference (Ref) based SR by utilizing the telephoto image (Ref) to assist the super-resolution of the low-resolution wide-angle image (LR input). Different from general RefSR, the Ref in dual-lens SR only covers the overlapped field of view (FoV) area. However, current dual-lens SR methods rarely utilize these specific characteristics and directly perform dense matching between the LR input and Ref. Due to the resolution gap between LR and Ref, the matching may miss the best-matched candidate and destroy the consistent structures in the overlapped FoV area. Different from them, we propose to first align the Ref with the center region (namely the overlapped FoV area) of the LR input by combining global warping and local warping to make the aligned Ref be sharp and consistent. Then, we formulate the aligned Ref and LR center as value-key pairs, and the corner region of the LR is formulated as queries. In this way, we propose a kernel-free matching strategy by matching between the LR-corner (query) and LR-center (key) regions, and the corresponding aligned Ref (value) can be warped to the corner region of the target. Our kernel-free matching strategy avoids the resolution gap between LR and Ref, which makes our network have better generalization ability. In addition, we construct a DuSR-Real dataset with (LR, Ref, HR) triples, where the LR and HR are well aligned. Experiments on three datasets demonstrate that our method outperforms the second-best method by a large margin. Our code and dataset are available at https://github.com/ZifanCui/KeDuSR.

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YNIMG Journal 2024 Journal Article

Processing demands modulate the activities and functional connectivity patterns of the posterior (VWFA-1) and anterior (VWFA-2) VWFA

Aqian Li
Chuansheng Chen
Xiaoyan Wu
Yuan Feng
Jingyu Yang
Xiaoxue Feng
Rui Hu
Leilei Mei

Previous studies have shown that the visual word form area (VWFA) has structural and intrinsic functional connectivity with both language and attention networks. Nevertheless, it is still unclear how the functional connectivity pattern of the VWFA is regulated by processing demands induced by experimental tasks, and whether processing demands differentially regulate the posterior (VWFA-1) and anterior (VWFA-2) subregions of the VWFA. To address these questions, the present study adopted two tasks varying in processing demands (i.e., verbal and non-verbal tasks), and used generalized psychophysiological interaction (gPPI) and dynamic causal modeling (DCM) analyses to explore the task-dependent functional connectivity patterns of the two subregions of the VWFA. Activation analysis revealed that the VWFA-2 showed higher activation for the verbal task than the non-verbal task, while there were no activation differences in the VWFA-1 after controlling for the stimulus driven effects. Functional and effective connectivity analyses revealed that, for both VWFA-1 and VWFA-2, the verbal task enhanced connections from VWFAs to the ventral language regions (e.g., the left orbital frontal cortex), while the non-verbal task enhanced connections from VWFAs to the dorsal visuospatial regions (e.g., the left intraparietal sulcus). Results of the present study indicate that processing demands induced by tasks modulate both the local activity and functional connectivity patterns of the VWFA, providing new insights for understanding its domain-general function.

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NeurIPS Conference 2024 Conference Paper

Virtual Scanning: Unsupervised Non-line-of-sight Imaging from Irregularly Undersampled Transients

Xingyu Cui
Huanjing Yue
Song Li
Xiangjun Yin
Yusen Hou
Yun Meng
Kai Zou
Xiaolong Hu

Non-line-of-sight (NLOS) imaging allows for seeing hidden scenes around corners through active sensing. Most previous algorithms for NLOS reconstruction require dense transients acquired through regular scans over a large relay surface, which limits their applicability in realistic scenarios with irregular relay surfaces. In this paper, we propose an unsupervised learning-based framework for NLOS imaging from irregularly undersampled transients~(IUT). Our method learns implicit priors from noisy irregularly undersampled transients without requiring paired data, which is difficult and expensive to acquire and align. To overcome the ambiguity of the measurement consistency constraint in inferring the albedo volume, we design a virtual scanning process that enables the network to learn within both range and null spaces for high-quality reconstruction. We devise a physics-guided SURE-based denoiser to enhance robustness to ubiquitous noise in low-photon imaging conditions. Extensive experiments on both simulated and real-world data validate the performance and generalization of our method. Compared with the state-of-the-art (SOTA) method, our method achieves higher fidelity, greater robustness, and remarkably faster inference times by orders of magnitude. The code and model are available at https: //github. com/XingyuCuii/Virtual-Scanning-NLOS.

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NeurIPS Conference 2023 Conference Paper

Recaptured Raw Screen Image and Video Demoiréing via Channel and Spatial Modulations

Yijia Cheng
Xin Liu
Jingyu Yang

Capturing screen contents by smartphone cameras has become a common way for information sharing. However, these images and videos are often degraded by moiré patterns, which are caused by frequency aliasing between the camera filter array and digital display grids. We observe that the moiré patterns in raw domain is simpler than those in sRGB domain, and the moiré patterns in raw color channels have different properties. Therefore, we propose an image and video demoiréing network tailored for raw inputs. We introduce a color-separated feature branch, and it is fused with the traditional feature-mixed branch via channel and spatial modulations. Specifically, the channel modulation utilizes modulated color-separated features to enhance the color-mixed features. The spatial modulation utilizes the feature with large receptive field to modulate the feature with small receptive field. In addition, we build the first well-aligned raw video demoiréing (RawVDemoiré) dataset and propose an efficient temporal alignment method by inserting alternating patterns. Experiments demonstrate that our method achieves state-of-the-art performance for both image and video demoiréing. Our dataset and code will be released after the acceptance of this work.

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NeurIPS Conference 2022 Conference Paper

FOF: Learning Fourier Occupancy Field for Monocular Real-time Human Reconstruction

Qiao Feng
Yebin Liu
Yu-Kun Lai
Jingyu Yang
Kun Li

The advent of deep learning has led to significant progress in monocular human reconstruction. However, existing representations, such as parametric models, voxel grids, meshes and implicit neural representations, have difficulties achieving high-quality results and real-time speed at the same time. In this paper, we propose Fourier Occupancy Field (FOF), a novel, powerful, efficient and flexible 3D geometry representation, for monocular real-time and accurate human reconstruction. A FOF represents a 3D object with a 2D field orthogonal to the view direction where at each 2D position the occupancy field of the object along the view direction is compactly represented with the first few terms of Fourier series, which retains the topology and neighborhood relation in the 2D domain. A FOF can be stored as a multi-channel image, which is compatible with 2D convolutional neural networks and can bridge the gap between 3D geometries and 2D images. A FOF is very flexible and extensible, \eg, parametric models can be easily integrated into a FOF as a prior to generate more robust results. Meshes and our FOF can be easily inter-converted. Based on FOF, we design the first 30+FPS high-fidelity real-time monocular human reconstruction framework. We demonstrate the potential of FOF on both public datasets and real captured data. The code is available for research purposes at http: //cic. tju. edu. cn/faculty/likun/projects/FOF.

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AAAI Conference 2022 Conference Paper

Reference-Based Speech Enhancement via Feature Alignment and Fusion Network

Huanjing Yue
Wenxin Duo
Xiulian Peng
Jingyu Yang

Speech enhancement aims at recovering a clean speech from a noisy input, which can be classified into single speech enhancement and personalized speech enhancement. Personalized speech enhancement usually utilizes the speaker identity extracted from the noisy speech itself (or a clean reference speech) as a global embedding to guide the enhancement process. Different from them, we observe that the speeches of the same speaker are correlated in terms of frame-level short-time Fourier Transform (STFT) spectrogram. Therefore, we propose reference-based speech enhancement via a feature alignment and fusion network (FAF-Net). Given a noisy speech and a clean reference speech spoken by the same speaker, we first propose a feature-level alignment strategy to warp the clean reference with the noisy speech in frame level. Then, we fuse the reference feature with the noisy feature via a similarity-based fusion strategy. Finally, the fused features are skipped connected to the decoder, which generates the enhanced results. Experimental results demonstrate that the performance of the proposed FAF-Net is close to the state-of-the-art speech enhancement methods on both DNS and Voice Bank+DEMAND datasets. Our code is available at https: //github. com/HieDean/FAF-Net.

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NeurIPS Conference 2021 Conference Paper

Implicit Transformer Network for Screen Content Image Continuous Super-Resolution

Jingyu Yang
Sheng Shen
Huanjing Yue
Kun Li

Nowadays, there is an explosive growth of screen contents due to the wide application of screen sharing, remote cooperation, and online education. To match the limited terminal bandwidth, high-resolution (HR) screen contents may be downsampled and compressed. At the receiver side, the super-resolution (SR)of low-resolution (LR) screen content images (SCIs) is highly demanded by the HR display or by the users to zoom in for detail observation. However, image SR methods mostly designed for natural images do not generalize well for SCIs due to the very different image characteristics as well as the requirement of SCI browsing at arbitrary scales. To this end, we propose a novel Implicit Transformer Super-Resolution Network (ITSRN) for SCISR. For high-quality continuous SR at arbitrary ratios, pixel values at query coordinates are inferred from image features at key coordinates by the proposed implicit transformer and an implicit position encoding scheme is proposed to aggregate similar neighboring pixel values to the query one. We construct benchmark SCI1K and SCI1K-compression datasets withLR and HR SCI pairs. Extensive experiments show that the proposed ITSRN significantly outperforms several competitive continuous and discrete SR methods for both compressed and uncompressed SCIs.

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