论文检索

Lihua Jing, Rui Wang, Jinwen Zhong, Runbo Li, Zixuan Zhu

Adversarial patch attacks pose a significant threat to visual systems. While current patch purification-based defense methods enhance core metrics of visual perception models, they overlook the critical issue of false positive patches, severely compromising image usability. This paper reveals the inadequacy of existing evaluations for adversarial patch defenses, and pioneers a multidimensional adversarial patch localization evaluation framework, which comprehensively quantifies false positives, recall capability, and overall localization accuracy, providing a novel perspective for comparative analysis within the field. Furthermore, building upon the observation that false positives stem from a lack of semantic understanding, we propose a Semantic-Aware Training-free Explainable Defense method (SATED). SATED achieves zero-shot patch localization, false detection correction, and decision explanation by constructing a patch reasoning chain, while simultaneously performing integrated text-guided patch inpainting. Extensive experiments across digital and physical scenarios, detection and segmentation tasks, and diverse adversarial patches, demonstrate that our method significantly reduces false positives and doubles the overall patch localization accuracy, boosting both the generalizability and explainability of the defense.

Yuqi Jiang, Yupeng Hu, Jinyuan Deng, Xiaotian Qiu, Yucheng Cui, Xuyang He, Ruidong Li, Qi Sun, Cheng Zhuo

Vision Language Models (VLMs) have shown strong performance in multimodal understanding, offering promise for the circuit-to-netlist translation task. However, the diverse component symbols and complex connections in circuit images challenge VLMs in understanding physical layouts and reasoning for electrical connection logic. To address these, we propose Circuit-Think, the first multimodal reasoning framework for the automated circuit-to-netlist translation task, which employs a Trajectory-Guided Reinforcement Learning (TGRL) paradigm for structured logical reasoning on circuit images. Circuit-Think initializes reasoning capabilities through supervised fine-tuning (SFT) on image-netlist pairs, then optimizes reasoning trajectories and netlist generation decisions using TGRL. Firstly, TGRL introduces a step-by-step reasoning paradigm, which guides the model with stepwise reward functions to simulate the human cognitive trajectory of ``identifying ports, recognizing devices, and inferring connections''. Secondly, we customize a multi-level reward that maps reasoning and answers into graph structures and node sets, jointly optimizing logical consistency and netlist accuracy via graph similarity and set matching. Thirdly, TGRL contains a reflective learning mechanism for low-scoring samples, which corrects the reasoning trajectory through reference answers as hints, avoiding local optima caused by sparse reward signals or erroneous reasoning paths. Moreover, we construct a circuit image-netlist reasoning dataset with 3,100 samples, offering step-by-step annotations for converting circuit images to netlists. Extensive experiments demonstrate that Circuit-Think achieves SOTA netlist accuracy and significantly improves the accuracy of downstream tasks.

Yiming Jiang, Wenfeng Song, Shuai Li, Aimin Hao

3D multi-human reconstruction from single images holds significant potential for advancing AR/VR applications. While remarkable progress has been made in single-human reconstruction, existing methods face challenges when reconstructing multiple humans. These challenges include: (1) severe inter-occlusion that disrupts individual body structures, and (2) the absence of physically plausible relative positioning among subjects. We present DECON, a novel DEcouple-and-reCONstruct framework that systematically addresses these limitations through two technical innovations: (1) a decouple-and-reconstruct framework with multi-view synthesis. It separates individuals and reconstructs detailed 3D bodies from a single image. (2) a Perspective-Aware Position Optimization (PAPO) approach. It ensures realistic positioning by fixing overlaps and gaps between subjects. Extensive experiments demonstrate our method's capability to reconstruct fully separated, anatomically complete 3D humans with clothed-geometric details and plausible interactions. Quantitative evaluations show a 54% reduction in Chamfer Distance and 35% in Point-to-Surface Distance compared to state-of-the-art methods.

Rui Jiang, Chongmian Wang, Xinghe Fu, Yehao Lu, Teng Li, Xi Li

Video transitions are critical for ensuring temporal coherence in edited media, yet existing methods often rely on handcrafted effects or relative-scale trajectories that fail to capture the physical structure of real-world scenes. In this work, we introduce a scale-aware video transition framework that explicitly incorporates depth-aware 3D reasoning into a diffusion-based generation pipeline. Built upon a powerful I2V foundation, our method leverages single-image depth prediction to align camera motion with metric-scale geometry, enabling physically consistent transitions. To reduce reliance on precise camera inputs, we propose a bidirectional conditional control module and a progressive training strategy with conditional dropout, enhancing generalization to loosely specified or missing camera trajectories. Extensive experiments demonstrate that our approach achieves state-of-the-art performance, delivering realistic, geometrically coherent transitions across diverse scenes and applications with minimal input guidance.

Keishi Ishihara, Kento Sasaki, Tsubasa Takahashi, Daiki Shiono, Yu Yamaguchi

Vision-Language Models (VLMs) have been applied to autonomous driving to support decision-making in complex real-world scenarios. However, their training on static, web-sourced image-text pairs fundamentally limits the precise spatiotemporal reasoning required to understand and predict dynamic traffic scenes. We address this critical gap with STRIDE-QA, a large-scale visual question answering (VQA) dataset for physically grounded reasoning from an ego-centric perspective. Constructed from 100 hours of multi-sensor driving data in Tokyo, capturing diverse and challenging conditions, STRIDE-QA is the largest VQA dataset for spatiotemporal reasoning in urban driving, offering 16 M QA pairs over 270 K frames. Grounded by dense, automatically generated annotations including 3D bounding boxes, segmentation masks, and multi-object tracks, the dataset uniquely supports both object-centric and ego-centric reasoning through three novel QA tasks that require spatial localization and temporal prediction. Our benchmarks demonstrate that existing VLMs struggle significantly, with near-zero scores on prediction consistency. In contrast, VLMs fine-tuned on STRIDE-QA exhibit dramatic performance gains, achieving 55% success in spatial localization and 28% consistency in future motion prediction, compared to near-zero scores from general-purpose VLMs. Therefore, STRIDE-QA establishes a comprehensive foundation for developing more reliable VLMs for safety-critical autonomous systems.

Junming Hou, Ran Ran, Sixing Chen, Zihao Chen, Xiaofeng Cong, Junling Li, Liang-Jian Deng

Pansharpening is a powerful technique for generating high-resolution multispectral (HRMS) images by fusing currently available image pairs of low-resolution multispectral (LRMS) and texture-rich panchromatic (PAN) data, effectively addressing the physical constraints of satellite sensors. While recent generative diffusion models have demonstrated impressive performance gains in this domain, their prohibitive computational demands and training costs hinder practicality in resource-constrained remote sensing satellite systems. In this work, we propose NODiff, a novel diffusion framework that replaces the conventional attention-based denoising backbone with a neural operator, seamlessly integrating operator learning and generative modeling into an efficient yet effective solution for pansharpening. In practice, we implement our approach through a two-stage learning paradigm: First, we pretrain the proposed Neural Operator-based diffusion model to learn the high-resolution texture priors essential for pansharpening. Afterward, we freeze the pretrained parameters, and design a lightweight conditional detail guidance adapter to enable efficient fine-tuning for generating desired HRMS images. Meanwhile, a time-aware low-rank adaptation is introduced to dynamically refine high-frequency details potentially affected by spectral mode truncation. Extensive experiments on multiple benchmark datasets demonstrate that NODiff achieves competitive pansharpening performance while significantly reducing training and inference costs. Beyond pansharpening, our method provides new insights into building resource-efficient generative models.

Haoqin Hong, Ding Fan, Fubin Dou, Zhi-Li Zhou, Haoran Sun, Congcong Zhu, Jingrun Chen

Recently, 3D Gaussian Splatting (3DGS), an explicit scene representation technique, has shown significant promise for dynamic novel-view synthesis from monocular video input. However, purely data-driven 3DGS often struggles to capture the diverse physics-driven motion patterns in dynamic scenes. To fill this gap, we propose Physics‑Informed Deformable Gaussian Splatting (PIDG), which treats each Gaussian particle as a Lagrangian material point with time-varying constitutive parameters and is supervised by 2D optical flow via motion projection. Specifically, we adopt static-dynamic decoupled 4D decomposed hash encoding to reconstruct geometry and motion efficiently. Subsequently, we impose the Cauchy momentum residual as a physics constraint, enabling independent prediction of each particle’s velocity and constitutive stress via a time-evolving material field. Finally, we further supervise data fitting by matching Lagrangian particle flow to camera-compensated optical flow, which accelerates convergence and improves generalization. Experiments on a custom physics-driven dataset as well as on standard synthetic and real-world datasets demonstrate significant gains in physical consistency and monocular dynamic reconstruction quality.

Shaojie Guo, Yingying Fang, Junkang Zhang, Yan Wang

Deep unrolling models (DUMs) have shown great poten-tial in sparse-view CT reconstruction by combining itera-tive optimization and deep learning. However, most DUMsinsufficiently account for physical degradation from sparse-view imaging, leading to slow convergence and persistentartifacts. To address this, we propose PAUM, a Physics-Aware Accelerated Unrolling Model explicitly incorporatingCT imaging physics into the iterative reconstruction. PAUMfirst introduces a Dual-Domain Physics-Aware Extrapolation(DDPE) module. By modeling dual-domain degradations, itperforms row-wise extrapolation in the sinogram domain toimprove missing view recovery, and pixel-wise extrapolationin the image domain to address spatially variant degradationfrom incomplete backprojection. This physics-aware extrap-olation aligns optimization dynamics with underlying physi-cal imaging degradation, significantly enhances structural up-dates, thereby accelerating convergence. Subsequently, wedevelop a lightweight Block-Attention Deformable Regu-larization Network (BDRN), leveraging deformable convo-lutions and block-wise attention to model spatially variantand structured artifact physical characteristics. This enablesspatially adaptive regularization on extrapolated results, ef-fectively improving reconstruction quality. Extensive exper-iments demonstrate PAUM achieves over 1dB improvementcompared to SOTA methods, while reducing iteration countby 50%.

Qianren Guo, Yuehang Wang, Yongji Zhang, Qi Chu, Sen Liu, Yu Jiang

Effectively capturing co-occurrence signals, such as hand shapes, facial expressions, and body postures, is critical for semantic understanding in sign language recognition (SLR) and translation (SLT). Although skeleton data offer greater efficiency and robustness than RGB inputs, existing methods typically rely on pairwise graph structures, limiting their ability to model complex high-order interactions across body regions. To address this limitation, we propose HyperSign, a hierarchical hypergraph neural network that systematically captures high-order co-occurrence patterns among diverse body parts. The Co-occurrence Graph Perception Module jointly learns relational structures via three complementary pathways: (1) traditional graph convolutions for modeling physical joint connections, (2) dynamic geometric hypergraphs constructed via k-nearest neighbors to encode local spatial patterns, and (3) soft hypergraphs generated by learnable prototypes to reveal latent semantic associations. To further enhance structural modeling and semantic consistency, a Meta-Part Hypergraph Fusion Module abstracts feature streams from the hands, face, and body into unified hypergraph nodes, while leveraging empirically derived co-occurrence priors to model high-order cross-part dependencies. Moreover, an uncertainty-aware collaborative distillation mechanism guides the model to focus on critical body regions. Extensive experiments on standard SLR and SLT benchmarks (e.g., PHOENIX-2014, PHOENIX-2014T, and CSL-Daily) demonstrate that HyperSign not only outperforms existing skeleton-based approaches in both speed and accuracy but also achieves competitive or superior results compared to several state-of-the-art RGB-based methods across multiple evaluation metrics.

Qi Guo, Xiaojun Jia, Shanmin Pang, Simeng Qin, Lin Wang, Ju Jia, Yang Liu, Qing Guo

Multimodal Large Language Models (MLLMs) are becoming integral to autonomous driving (AD) systems due to their strong vision-language reasoning capabilities. However, MLLMs are vulnerable to adversarial attacks—particularly adversarial patch attacks—which can pose serious threats in real-world scenarios. Existing patch-based attack methods are primarily designed for object detection models. Due to the more complex architectures and strong reasoning capabilities of MLLMs, these approaches perform poorly when transferred to MLLM-based systems. To address these limitations, we propose PhysPatch, a physically realizable and transferable adversarial patch framework tailored for MLLM-based AD systems. PhysPatch jointly optimizes patch location, shape, and content to enhance attack effectiveness and real-world applicability. It introduces a semantic-based mask initialization strategy for realistic placement, an SVD-based local alignment loss with patch-guided crop-resize to improve transferability, and a potential field-based mask refinement method. Extensive experiments across open-source, commercial, and reasoning-capable MLLMs demonstrate that PhysPatch significantly outperforms state-of-the-art (SOTA) methods in steering MLLM-based AD systems toward target-aligned perception and planning outputs. Moreover, PhysPatch consistently places adversarial patches in physically feasible regions of AD scenes, ensuring strong real-world applicability and deployability.

Bingyang Guo, Qiang Zuo, Ruiyun Yu

The effective segmentation of 3D data is crucial for a wide range of industrial applications, especially for detecting subtle defects in the field of integrated circuits (IC). Ceramic package substrates (CPS), as an important electronic material, are essential in IC packaging owing to their superior physical and chemical properties. However, the complex structure and minor defects of CPS, along with the absence of a publically available dataset, significantly hinder the development of CPS surface defect detection. In this study, we construct a high-quality point cloud dataset for 3D segmentation of surface defects in CPS, i.e., CPS3D-Seg, which has the best point resolution and precision compared to existing 3D industrial datasets. CPS3D-Seg consists of 1300 point cloud samples under 20 product categories, and each sample provides accurate point-level annotations. Meanwhile, we conduct a comprehensive benchmark based on SOTA point cloud segmentation algorithms to validate the effectiveness of CPS3D-Seg. Additionally, we propose a novel 3D segmentation method based on causal inference (CINet), which quantifies potential confounders in point clouds through Structural Refine (SR) and Quality Assessment (QA) Modules. Extensive experiments demonstrate that CINet significantly outperforms existing algorithms in both mIoU and accuracy.

Yutao Feng, Yintong Shang, Xiang Feng, Lei Lan, Shandian Zhe, Tianjia Shao, Hongzhi Wu, Kun Zhou, Chenfanfu Jiang, Yin Yang

We present ElastoGen, a knowledge-driven AI model that generates physically accurate 4D elastodynamics. Unlike deep models that learn from video- or image-based observations, ElastoGen leverages the principles of physics and learns from established mathematical and optimization procedures. The core idea of ElastoGen is converting the differential equation, corresponding to the nonlinear force equilibrium, into a series of iterative local convolution-like operations, which naturally fit deep architectures. We carefully build our network module following this overarching design philosophy. ElastoGen is much more lightweight in terms of both training requirements and network scale than deep generative models. Because of its alignment with actual physical procedures, ElastoGen efficiently generates accurate dynamics for a wide range of hyperelastic materials and can be easily integrated with upstream and downstream deep modules to enable end-to-end 4D generation.

Wei Feng, Chi Huang, Qi Zhang, Qian Zhang, Nan Li

The latest advancements in scene relighting have been predominantly driven by inverse rendering with 3D Gaussian Splatting (3DGS). However, existing methods remain overly reliant on precise camera parameters under static illumination conditions, which is prohibitively expensive and even impractical in real-world scenarios. In this paper, we propose a novel learning from Unposed views under Varied illuminations Relightable 3D Gaussian Splatting (dubbed UV-RGS), to address this challenge by jointly optimizing camera poses, 3DGS representations, surface materials, and environment illuminations (i.e., unknown and varied lighting conditions in training) using only unposed views under varied lightings. Firstly, UV-RGS presents a viewpoint dividing strategy to group inputs into constituent units, enabling each unit can perform similar poses and illuminations. Next, for each unit, to get the constituent model, UV-RGS establishes an incrementally pose learning module to estimate coarse camera parameters, which also enjoy a proxy-view refinement to alleviate the sparse view learning. Additionally, for all constituent unit models, we introduce a holistic model learning strategy that integrates progressive unit aggregation component and the 3DGS coupled with camera poses joint optimization, which realizes the scene high-fidelity perception by the physical-based rendering. Extensive experiments on both real-world and synthetic challenging datasets demonstrate the effectiveness of UV-RGS, achieving the state-of-the-art performance for scene inverse rendering by learning 3DGS from only unposed views under varied illuminations.

Songcheng Du, Yang Zou, Jiaxin Li, Mingxuan Liu, Ying Li, Changjing Shang, Qiang Shen

Pansharpening under thin cloudy conditions is a practically significant yet rarely addressed task, challenged by simultaneous spatial resolution degradation and cloud-induced spectral distortions. Existing methods often address cloud removal and pansharpening sequentially, leading to cumulative errors and suboptimal performance due to the lack of joint degradation modeling. To address these challenges, we propose a Unified Pansharpening Model with Thin Cloud Removal (Pan-TCR), an end-to-end framework that integrates physical priors. Motivated by theoretical analysis in the frequency domain, we design a frequency-decoupled restoration (FDR) block that disentangles the restoration of multispectral image (MSI) features into amplitude and phase components, each guided by complementary degradation-robust prompts: the near-infrared (NIR) band amplitude for cloud-resilient restoration, and the panchromatic (PAN) phase for high-resolution structural enhancement. To ensure coherence between the two components, we further introduce an interactive inter-frequency consistency (IFC) module, enabling cross-modal refinement that enforces consistency and robustness across frequency cues. Furthermore, we introduce the first real-world thin-cloud contaminated pansharpening dataset (PanTCR-GF2), comprising paired clean and cloudy PAN-MSI images, to enable robust benchmarking under realistic conditions. Extensive experiments on real-world and synthetic datasets demonstrate the superiority and robustness of Pan-TCR, establishing a new benchmark for pansharpening under realistic atmospheric degradations.

Zeyu Deng, Lihui Wang, Xi Tao, Qijian Chen, Ying Cao, XuLinHu, Yingfeng Ou

The inherently low signal-to-noise ratio (SNR) in diffusion-weighted (DW) imaging fundamentally impedes precise tissue microstructure characterization, rendering effective noise suppression a persistent challenge. Existing denoising methods frequently suffer from over-smoothing or distortion of microstructure information when handling spatially correlated or severe noise. To address these limitations, we propose UP2-MAE fusion model, a self-supervised DWI denoising method based on Uncertainty-Propelled Physics and Masked Auto-Encoder (MAE) fusion. This framework integrates two complementary branches: one leverages MAE to suppress noise through local context modeling, while the other constructs uncorrelated noisy pairs using diffusion tensor imaging (DTI) physics and denoises them via a Noise2Noise approach, which can preserve texture details by exploiting directional relationships across diffusion encoding directions. To fully integrate the strengths of both branches, an uncertainty-propelled fusion strategy based on maximum likelihood estimation is proposed to derive the final denoised output. In addition, to further promote the performance, uncertainty-guided reconstruction and consistency loss are presented. Evaluations against state-of-the-art denoising methods on both simulated and acquired DW datasets confirm the efficacy of our approach.

Thinh Dao, Khoa D Doan, Kok-Seng Wong

Deep Neural Networks (DNNs) are shown to be vulnerable to backdoor poisoning attacks, with most research focusing on digital triggers that consist of artificial patterns added to test-time inputs to induce targeted misclassification. Physical triggers, which are natural objects embedded in real-world scenes, offer a promising alternative for attackers as they can activate backdoors in real-time without digital manipulation. However, existing physical backdoor attacks are dirty-label, meaning that attackers must change the labels of poisoned inputs to the target label. The inconsistency between image content and label exposes the attack to human inspection, reducing its stealthiness in real-world settings. To address this limitation, we introduce Clean-Label Physical Backdoor Attack (CLPBA), a new paradigm of physical backdoor attack that does not require label manipulation and trigger injection at the training stage. Instead, the attacker injects imperceptible perturbations into a small number of target class samples to backdoor a model. By framing the attack as a Dataset Distillation problem, we develop three CLPBA variants, namely Parameter Matching, Gradient Matching, and Feature Matching, that craft effective poisons under both linear probing and full-finetuning training settings. In hard scenarios that require backdoor generalizability in the physical world, CLPBA is shown to even surpass Dirty-label attack baselines. We demonstrate the effectiveness of CLPBA via extensive experiments on two collected physical backdoor datasets for facial recognition and animal classification.

Honghao Dai, Yuanfeng Zhou, Guangshun Wei, Zhihao Li, Wenping Wang

Orthodontic treatment needs regular tooth alignment checks, but current methods depend on clinic visits, limiting remote care. With the emergence of 3D Gaussian Splatting (3DGS), realistic novel views can be synthesized, making it possible for clinicians to remotely monitor orthodontic conditions. However, using only five intraoral images with unknown camera poses and dynamic lighting presents major challenges in dental applications. To address these challenges, we propose DentalGS, an enhanced 3DGS framework capable of synthesizing novel intraoral views from five post-orthodontic intraoral images and pre-orthodontic intraoral scan (IOS) data as prior, without camera poses. Our method initializes a Gaussian point cloud labeled with ISO-FDI tooth classes based on the patient’s pre-orthodontic IOS data, then estimates camera poses through iterative optimization. We introduce a Progressive Pair Generation Strategy as a data augmentation method that generates damage–repair image pairs to train a RepairNet, aiming to restore degraded geometry and appearance caused by the limited number of intraoral images. Additionally, we introduce a Lighting-Aware 3DGS inspired by physical reflectance properties to mitigate the effects of dynamic lighting conditions. Experimental results show that our method produces high-quality novel views while preserving geometric structure even under extreme viewpoints, offering an efficient and reliable solution for 3D tooth visualization in remote orthodontic monitoring.

Zhuo Chen, Zhongqun Zhang, Yihua Cheng, Aleš Leonardis, Hyung Jin Chang

Contact-based grasp generation plays a crucial role in various applications. Recent methods typically focus on the geometric structure of objects, producing grasps with diverse hand poses and plausible contact points. However, these approaches often overlook the physical attributes of the grasp, specifically the contact force, leading to reduced stability of the grasp. In this paper, we focus on stable grasp generation using explicit contact force predictions. First, we define a force-aware contact representation by transforming the normal force value into discrete levels and encoding it using a one-hot vector. Next, we introduce force-aware stability constraints. We define the stability problem as an acceleration minimization task and explicitly relate stability with contact geometry by formulating the underlying physical constraints. Finally, we present a pose optimizer that systematically integrates our contact representation and stability constraints to enable stable grasp generation. We show that these constraints can help identify key contact points for stability which provide effective initialization and guidance for optimization towards a stable grasp. Experiments are carried out on two public benchmarks, showing that our method brings about 20% improvement in stability metrics and adapts well to novel objects.

Kanghao Chen, Zixin Zhang, Hangyu Li, Lin Wang, Zeyu Wang

Event cameras are bio-inspired sensors that capture visual information through asynchronous brightness changes, offering distinct advantages including high temporal resolution and wide dynamic range. While prior research has investigated event-based 3D reconstruction for extreme scenarios, existing methods face inherent limitations and fail to fully exploit the unique characteristics of event data. In this paper, we present EvDiff3D, a novel two-stage 3D reconstruction framework that integrates event-based geometric constraints with an event-aware diffusion prior for appearance refinement. Our key insight lies in bridging the gap between physically grounded event-based reconstruction and data-driven appearance repair through a unified cyclical pipeline. In the first stage, we reconstruct a coarse 3D scene under supervision from event loss and event-based monocular depth constraints to preserve structural fidelity. The second stage fine-tunes an event-aware diffusion model based on a pretrained video diffusion model as a repair prior to enhance the appearance in under-constrained regions. Based on the diffusion model, our pipeline operates within a reconstruction-generation cycle that progressively refines both geometry and appearance using only event data. Extensive experiments on synthetic and real-world datasets demonstrate that EvDiff3D significantly outperforms existing methods in perceptual quality and structural consistency.

Frédéric Berdoz, Luca A Lanzendörfer, Nick Tuninga, Roger Wattenhofer

Prompt-driven scene synthesis allows users to generate complete 3D environments from textual descriptions. Current text-to-scene methods often struggle with complex geometries and object transformations, and tend to show weak adherence to complex instructions. We address these limitations by introducing Reason-3D, a text-to-scene model powered by large reasoning models (LRMs). Reason-3D integrates object retrieval using captions covering physical, functional, and contextual attributes. Reason-3D then places the selected objects based on implicit and explicit layout constraints, and refines their positions with collision-aware spatial reasoning. Evaluated on instructions ranging from simple to complex indoor configurations, Reason-3D significantly outperforms previous methods in human-rated visual fidelity, adherence to constraints, and asset retrieval quality. Beyond its contribution to the field of text-to-scene generation, our work showcases the advanced spatial reasoning abilities of modern LRMs. Additionally, we release the codebase to further the research in object retrieval and placement with LRMs.