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Hongbin Xu

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6 papers

1 author row

AAAI Conference 2026 Conference Paper

CyC3D: Fine-grained Controllable 3D Generation via Cycle Consistency Regularization

Hongbin Xu
Chaohui Yu
Feng Xiao
Jiazheng Xing
Hai Ci
Weitao Chen
Fan Wang
Ming Li

Despite the remarkable progress of 3D generation, achieving controllability, i.e., ensuring consistency between generated 3D content and input conditions like edge and depth, remains a significant challenge. Existing methods often struggle to maintain accurate alignment, leading to noticeable discrepancies. To address this issue, we propose CyC3D, a new framework that enhances controllable 3D generation by explicitly encouraging cyclic consistency between the second-order 3D content, generated based on extracted signals from the first-order generation, and its original input controls. Specifically, we employ an efficient feed-forward backbone that can generate a 3D object from an input condition and a text prompt. Given an initial viewpoint and a control signal, a novel view is rendered from the generated 3D content, from which the extracted condition is used to regenerate the 3D content. This re-generated output is then rendered back to the initial viewpoint, followed by another round of control signal extraction, forming a cyclic process with two consistency constraints. View consistency ensures coherence between the two generated 3D objects, measured by semantic similarity to accommodate generative diversity. Condition consistency aligns the final extracted signal with the original input control, preserving structural or geometric details throughout the process. Extensive experiments on popular benchmarks demonstrate that CyC3D significantly improves controllability, especially for fine-grained details, outperforming existing methods across various conditions (e.g., +14.17% PSNR for edge, +6.26% PSNR for sketch).

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AAAI Conference 2026 Conference Paper

OptMark: Robust Multi-bit Diffusion Watermarking via Inference Time Optimization

Jiazheng Xing
Hai Ci
Hongbin Xu
Hangjie Yuan
Yong Liu
Mike Zheng Shou

Watermarking diffusion-generated images is crucial for copyright protection and user tracking. However, current diffusion watermarking methods face significant limitations: zero-bit watermarking systems lack the capacity for large-scale user tracking, while multi-bit methods are highly sensitive to certain image transformations or generative attacks, resulting in a lack of comprehensive robustness. In this paper, we propose OptMark, an optimization-based approach that embeds a robust multi-bit watermark into the intermediate latents of the diffusion denoising process. OptMark strategically inserts a structural watermark early to resist generative attacks and a detail watermark late to withstand image transformations, with tailored regularization terms to preserve image quality and ensure imperceptibility. To address the challenge of memory consumption growing linearly with the number of denoising steps during optimization, OptMark incorporates adjoint gradient methods, reducing memory usage from O(N) to O(1). Experimental results demonstrate that OptMark achieves invisible multi-bit watermarking while ensuring robust resilience against valuemetric transformations, geometric transformations, editing, and regeneration attacks.

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EAAI Journal 2026 Journal Article

Semi-supervised tunnel lining water leakage image segmentation via two-stage collaborative mean-teacher

Peng Geng
Yimeng Wang
Xiaofen Wang
Jun Feng
Hongbin Xu
Yuting Zhang

Accurate water leakage image segmentation plays a crucial role in maintaining tunnel linings, as it can delineate leakage areas and provide clear guidance for subsequent repair work. Existing fully supervised methods for water leakage image segmentation in tunnel linings have achieved good results. Still, these methods require a large amount of labeled data, which limits the broader application of these models. To address this issue, this study proposes a semi-supervised tunnel lining water leakage image segmentation method, which is a two-stage collaborative mean-teacher framework (TCMT). Specifically, to enhance the accuracy of model training, the first stage of the framework employs a collaborative contrastive learning strategy, primarily using cross-supervised contrastive learning to capture intra-class compactness and separability at the feature level. To enhance semantic consistency at the data level, the second stage of the framework adopts a collaborative replication augmentation strategy, learning more comprehensive water leakage features from labeled data through bidirectional copy-paste. Employing the two-stage method with different strategies helps increase model uncertainty, thereby further preventing co-training from degenerating into self-training. In terms of the segmentation network, a dual U-shaped network combining the advantages of the Transformer and Convolutional Neural Network is proposed. Experimental results demonstrate that the proposed method achieves high segmentation performance even with a very small amount of labeled data. When using only 1% of the labeled data, the proposed method achieved an 82. 30% Intersection of Union (IoU) and an 85. 74% IoU when using 10% of the labeled data, outperforming the state-of-the-art semi-supervised methods. Our code is available at https: //github. com/sineagles/TCMT.

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AAAI Conference 2026 Conference Paper

StyleTailor: Towards Personalized Fashion Styling via Hierarchical Negative Feedback

Hongbo Ma
Fei Shen
Hongbin Xu
Xiaoce Wang
Gang Xu
Jinkai Zheng
Liangqiong Qu
Ming Li

The advancement of intelligent agents has revolutionized problem-solving across diverse domains, yet solutions for personalized fashion styling remain underexplored, which holds immense promise for promoting shopping experiences. In this work, we present StyleTailor, the first collaborative agent framework that seamlessly unifies personalized apparel design, shopping recommendation, virtual try-on, and systematic evaluation into a cohesive workflow. To this end, StyleTailor pioneers an iterative visual refinement paradigm driven by multi-level negative feedback, enabling adaptive and precise user alignment. Specifically, our framework features two core agents, i.e., Designer for personalized garment selection and Consultant for virtual try-on, whose outputs are progressively refined via hierarchical vision-language model feedback spanning individual items, complete outfits, and try-on efficacy. Counterexamples are aggregated into negative prompts, forming a closed-loop mechanism that enhances recommendation quality. To assess the performance, we introduce a comprehensive evaluation suite encompassing style consistency, visual quality, face similarity, and artistic appraisal. Extensive experiments demonstrate StyleTailor's superior performance in delivering personalized designs and recommendations, outperforming strong baselines without negative feedback and establishing a new benchmark for intelligent fashion systems.

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IJCAI Conference 2023 Conference Paper

CostFormer: Cost Transformer for Cost Aggregation in Multi-view Stereo

Weitao Chen
Hongbin Xu
Zhipeng Zhou
Yang Liu
Baigui Sun
Wenxiong Kang
Xuansong Xie

The core of Multi-view Stereo(MVS) is the matching process among reference and source pixels. Cost aggregation plays a significant role in this process, while previous methods focus on handling it via CNNs. This may inherit the natural limitation of CNNs that fail to discriminate repetitive or incorrect matches due to limited local receptive fields. To handle the issue, we aim to involve Transformer into cost aggregation. However, another problem may occur due to the quadratically growing computational complexity caused by Transformer, resulting in memory overflow and inference latency. In this paper, we overcome these limits with an efficient Transformer-based cost aggregation network, namely CostFormer. The Residual Depth-Aware Cost Transformer(RDACT) is proposed to aggregate long-range features on cost volume via self-attention mechanisms along the depth and spatial dimensions. Furthermore, Residual Regression Transformer(RRT) is proposed to enhance spatial attention. The proposed method is a universal plug-in to improve learning-based MVS methods.

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AAAI Conference 2021 Conference Paper

Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation

Hongbin Xu
Zhipeng Zhou
Yu Qiao
Wenxiong Kang
Qiuxia Wu

Recent studies have witnessed that self-supervised methods based on view synthesis obtain clear progress on multiview stereo (MVS). However, existing methods rely on the assumption that the corresponding points among different views share the same color, which may not always be true in practice. This may lead to unreliable self-supervised signal and harm the final reconstruction performance. To address the issue, we propose a framework integrated with more reliable supervision guided by semantic co-segmentation and dataaugmentation. Specially, we excavate mutual semantic from multi-view images to guide the semantic consistency. And we devise effective data-augmentation mechanism which ensures the transformation robustness by treating the prediction of regular samples as pseudo ground truth to regularize the prediction of augmented samples. Experimental results on DTU dataset show that our proposed methods achieve the state-of-the-art performance among unsupervised methods, and even compete on par with supervised methods. Furthermore, extensive experiments on Tanks&Temples dataset demonstrate the effective generalization ability of the proposed method. The code is released at: https: //github. com/ ToughStoneX/Self-Supervised-MVS.

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