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Haiyang Bai, Jiaqi Zhu, Songru Jiang, Wei Huang, Tao Lu, Yuanqi Li, Jie Guo, Runze Fu, Yanwen Guo, Lijun Chen

We propose a 3D Gaussian splatting-based framework for outdoor relighting that leverages intrinsic image decomposition to precisely integrate sunlight, sky radiance, and indirect lighting from unconstrained photo collections. Unlike prior methods that compress the per-image global illumination into a single latent vector, our approach enables simultaneously diverse shading manipulation and the generation of dynamic shadow effects. This is achieved through three key innovations: (1) a residual-based sun visibility extraction method to accurately separate direct sunlight effects, (2) a region-based supervision framework with a structural consistency loss for physically interpretable and coherent illumination decomposition, and (3) a ray-tracing-based technique for realistic shadow simulation. Extensive experiments demonstrate that our framework synthesizes novel views with competitive fidelity against state-of-the-art relighting solutions and produces more natural and multifaceted illumination and shadow effects.

Fei Zhou, Peng Wang, Lei Zhang, Wei Wei, Chen Ding, Guosheng Lin, Yanning Zhang

Large-scale pre-trained foundation models have demonstrated remarkable generalization capabilities across diverse computer vision tasks through fine-tuning. However, existing fine-tuning approaches often encounter challenges in extreme cross-domain few-shot learning scenarios, primarily due to the significant domain shift between pre-training data and target tasks, as well as the scarcity of annotated target samples. To mitigate this issue, we propose a novel absorption adaptation learning framework which meticulously regularizes the fine-tuning procedure of foundation model using an expert model with the same architecture but trained from scratch on the targeted data in two aspects. On one hand, we first design a masked cross-model unidirectional reconstruction scheme, which forces the foundation model to recover the intermediate feature of the expert model in a randomly masked manner. On the other hand, a decision graph association loss is developed to encourage the consistency of token similarity matrix between these two models. By doing these, the task-relevant semantic knowledge in the expert model from both intermediate feature and the final decision levels are appropriately extracted and absorbed by the foundation model during its fine-tuning, thus mitigating the performance drop caused by domain gap and limited annotation. Sufficient experiments with further observations and analyses underpin our observation and argument. The code is available at https://github.com/NWPUZhoufei/FMA.

Zhengbo Zhang, Lin Geng Foo, Hossein Rahmani, Jun Liu, De Wen Soh

Single image defocus deblurring (SIDD) is a challenging task that aims to recover an all-in-focus image from a defocused one. In this paper, we make the observation that a defocused image can be viewed as a blend of illuminated blobs based on fundamental imaging principles, and the defocus blur in the defocused image is caused by large illuminated blobs intermingling with each other. Thus, from a novel perspective, we perform SIDD by adjusting the shape and opacity of the illuminated blobs that compose the defocused image. With this aim, we adopt a novel 2D Gaussian blob representation for illuminated blobs and a differentiable rasterization method to obtain the parameters of the 2D Gaussian blobs that compose the defocused image. Additionally, we propose a blob deblurrer to adjust the parameters of the 2D Gaussian blobs corresponding to the defocused image, thereby obtaining a sharp image. We also explore incorporating prior depth information via our depth-based regularization loss to regularize the size of Gaussian blobs, further improving the performance of our method. Extensive experiments on five widely-used datasets validate the effectiveness of our proposed method.

Junhao Dong, Piotr Koniusz, Liaoyuan Feng, Yifei Zhang, Hao Zhu, Weiming Liu, Xinghua Qu, Yew-Soon Ong

Vision-Language Models (VLMs) enjoy strong zero-shot performance but are vulnerable to adversarial attacks posing security risks. Adversarially robust fine-tuning enhances zero-shot robustness on new datasets while preserving the natural performance of pre-trained VLMs. However, prior methods use sample-wise adversarial fine-tuning, neglecting the underlying second-order statistics that represent entire groups of samples. This leads to a feature-level discrepancy between clean and adversarial samples of their augmented variants. Thus, we propose to represent groups of samples as subspaces to capture distributions and turn the traditional sample-wise adversarial fine-tuning into its distributional counterpart. For each image, we build distributions from (i) a clean sample with its augmentations and (ii) their adversarial counterparts. For text, we build distributions from (iii) a clean prompt and its synonymous prompts and (iv) their adversarial counterparts. We then perform alignment between image and text subspaces, and "adversarial" subspaces are also aligned toward "clean" subspaces. Thus, all samples underlying these distributions (think infinite number) also get aligned, leading to generalizable robustness. Evaluations on 15 datasets are provided.

Weihao Yu, Xiaoqing Guo, Xinyu Liu, Yifan Liu, Hao Zheng, Yawen Huang, Yixuan Yuan

Intraoperative 2D/3D registration, which aligns preoperative CT scans with intraoperative X-ray images, is critical for surgical navigation. However, existing methods require extensive preoperative training (several hours), making them unsuitable for emergency surgeries where minutes significantly impact patient outcomes. We present GaussianReg, a novel registration framework that achieves clinically acceptable accuracy within minutes of preprocessing. Unlike prior approaches that learn primarily from 2D projections, we explicitly utilize 3D information by representing CT volumes as sparse Gaussian primitives and propose an innovative ray-based registration approach. These primitives emit rays toward potential camera positions, creating a hypothesis space of viewpoints. The registration problem then reduces to identifying rays that best match the target X-ray through our cross-modality attention mechanism. We further introduce canonical ellipsoid ray parameterization for stable optimization, bipartite matching-based patch aggregation for computational efficiency, and network pruning to accelerate training. Extensive experiments demonstrate that GaussianReg achieves 10mm-level accuracy with only 10 minutes of training, compared to hours required by existing methods. Our approach thus offers a promising solution for emergency surgical scenarios where rapid adaptation to patient-specific anatomy is critical. Code will be publicly available at https://github.com/CUHK-AIM-Group/GaussianReg.

Jiayuan Zhu, Junde Wu, Cheng Ouyang, Konstantinos Kamnitsas, J. Alison Noble

Medical image segmentation data inherently contain uncertainty. This can stem from both imperfect image quality and variability in labeling preferences on ambiguous pixels, which depend on annotator expertise and the clinical context of the annotations. For instance, a boundary pixel might be labeled as tumor in diagnosis to avoid under-estimation of severity, but as normal tissue in radiotherapy to prevent damage to sensitive structures. As segmentation preferences vary across downstream applications, it is often desirable for an image segmentation model to offer user-adaptable predictions rather than a fixed output. While prior uncertainty-aware and interactive methods offer adaptability, they are inefficient at test time: uncertainty-aware models require users to choose from numerous similar outputs, while interactive models demand significant user input through click or box prompts to refine segmentation. To address these challenges, we propose SPA, a new Segmentation Preference Alignment framework that efficiently adapts to diverse test-time preferences with minimal human interaction. By presenting users with a select few, distinct segmentation candidates that best capture uncertainties, it reduces the user workload to reach the preferred segmentation. To accommodate user preference, we introduce a probabilistic mechanism that leverages user feedback to adapt a model's segmentation preference. The proposed framework is evaluated on several medical image segmentation tasks: color fundus images, lung lesion and kidney CT scans, MRI scans of brain and prostate. SPA shows 1) a significant reduction in user time and effort compared to existing interactive segmentation approaches, 2) strong adaptability based on human feedback, and 3) state-of-the-art image segmentation performance across different imaging modalities and semantic labels. Our code is publicly available here: https://github.com/SuperMedIntel/SPA.

Khaled Abud, Sergey Lavrushkin, Alexey Kirillov, Dmitriy Vatolin

Diffusion-based models have recently revolutionized image generation, achieving unprecedented levels of fidelity. However, consistent generation of high-quality images remains challenging partly due to the lack of conditioning mechanisms for perceptual quality. In this work, we propose methods to integrate image quality assessment (IQA) models into diffusion-based generators, enabling quality-aware image generation. We show that diffusion models can learn complex qualitative relationships from both IQA models' outputs and internal activations. First, we experiment with gradient-based guidance to optimize image quality directly and show this method has limited generalizability. To address this, we introduce IQA-Adapter, a novel framework that conditions generation on target quality levels by learning the implicit relationship between images and quality scores. When conditioned on high target quality, IQA-Adapter can shift the distribution of generated images towards a higher-quality subdomain, and, inversely, it can be used as a degradation model, generating progressively more distorted images when provided with a lower-quality signal. Under high-quality condition, IQA-Adapter achieves up to a 10% improvement across multiple objective metrics, as confirmed by a user preference study, while preserving generative diversity and content. Furthermore, we extend IQA-Adapter to a reference-based conditioning scenario, utilizing the rich activation space of IQA models to transfer highly specific, content-agnostic qualitative features between images.

J. Alejandro Amador H., Yi Zhou, Xin Sun, Zhixin Shu, Chengan He, Soren Pirk, Dominik L. Michels

We propose a novel Augmented Mass-Spring (AMS) model for real-time simulation of dense hair at the strand level. Our approach considers the traditional edge, bending, and torsional degrees of freedom in mass-spring systems, but incorporates an additional one-way biphasic coupling with a ghost rest-shape configuration. Through multiple evaluation experiments with varied dynamical settings, we show that AMS improves the stability of the simulation in comparison to mass-spring discretizations, preserves global features, and enables the simulation of non-Hookean effects. Using a heptadiagonal decomposition of the resulting matrix, our approach provides the efficiency advantages of mass-spring systems over more complex constitutive hair models, while enabling a more robust simulation of multiple strand configurations. Finally, our results demonstrate that our framework enables the generation, complex interactivity, and editing of simulation-ready dense hair assets in real time.

Tobias Fischer, Samuel Rota Bulò, Yung-Hsu Yang, Nikhil Keetha, Lorenzo Porzi, Norman Müller, Katja Schwarz, Jonathon Luiten, Marc Pollefeys, Peter Kontschieder

3D Gaussian splatting enables high-quality novel view synthesis (NVS) at real-time frame rates. However, its quality drops sharply as we depart from the training views. Thus, dense captures are needed to match the high-quality expectations of applications like Virtual Reality (VR). However, such dense captures are very laborious and expensive to obtain. Existing works have explored using 2D generative models to alleviate this requirement by distillation or generating additional training views. These models typically rely on a noise-to-data generative process conditioned only on a handful of reference input views, leading to hallucinations, inconsistent generation results, and subsequent reconstruction artifacts. Instead, we propose a multi-view, flow matching model that learns a flow to directly connect novel view renderings from possibly sparse reconstructions to renderings that we expect from dense reconstructions. This enables augmenting scene captures with consistent, generated views to improve reconstruction quality. Our model is trained on a novel dataset of 3.6M image pairs and can process up to 45 views at 540x960 resolution (91K tokens) on one H100 GPU in a single forward pass. Our pipeline consistently improves NVS in sparse- and dense-view scenarios, leading to higher-quality reconstructions than prior works across multiple, widely-used NVS benchmarks.

Qiaosi Yi, Shuai Li, Rongyuan Wu, Lingchen Sun, Yuhui Wu, Lei Zhang

Impressive results on real-world image super-resolution (Real-ISR) have been achieved by employing pre-trained stable diffusion (SD) models. However, one critical issue of such methods lies in their poor reconstruction of image fine structures, such as small characters and textures, due to the aggressive resolution reduction of the VAE (e.g., 8xdownsampling) in the SD model. One solution is to employ a VAE with a lower downsampling rate for diffusion; however, adapting its latent features with the pre-trained UNet while mitigating the increased computational cost poses new challenges. To address these issues, we propose a Transfer VAE Training (TVT) strategy to transfer the 8xdownsampled VAE into a 4xone while adapting to the pre-trained UNet. Specifically, we first train a 4xdecoder based on the output features of the original VAE encoder, then train a 4xencoder while keeping the newly trained decoder fixed. Such a TVT strategy aligns the new encoder-decoder pair with the original VAE latent space while enhancing image fine details. Additionally, we introduce a compact VAE and compute-efficient UNet by optimizing their network architectures, reducing the computational cost while capturing high-resolution fine-scale features. Experimental results demonstrate that our TVT method significantly improves fine-structure preservation, which is often compromised by other SD-based methods, while requiring fewer FLOPs than state-of-the-art one-step diffusion models. The official code can be found at https://github.com/Joyies/TVT.

Yuan Wang, Yuxin Chen, Zhongang Qi, Lijun Liu, Jile Jiao, Xuetao Feng, Yujia Liang, Ying Shan, Zhipeng Zhang

3D vision-language (3D-VL) reasoning, connecting natural language with 3D physical world, represents a milestone in advancing spatial intelligence. While transformer-based methods dominate 3D-VL research, their quadratic complexity and simplistic positional embedding mechanisms severely limits effective modeling of long-range 3D-VL dependencies and spatial relationships in 3D-VL tasks. State Space Models (SSM) have emerged as promising linear-complexity alternatives for sequential data processing, while inherent selection mechanism offers notable capability for spatial modeling. Despite its potential, straightforward adoption of Mamba to 3D-VL tasks encounters two obstacles: (1) how to perceive the position of 3D objects and understand complex spatial relationships, and (2) how to achieve thorough synergies of multi-modal features. In this paper, we propose Mamba-3VL, a pioneering 3D-VL framework to model complex intra- and inter-modality correlations and enhance spatial relation reasoning, while guaranteeing top-tier performance, high efficiency, and generalization potential for 3D-VL tasks. Specifically, Mamba Mixer explicitly models 3D-VL interaction via channel twisting and relation-prioritized spatial scanning policy. It maximally retain spatial relation of object-centric features. To further provide precise spatial encoding for mamba, we develop Instance-aware Dynamic Position Adapter (IDPA) to dynamically adjust instance-specific positional embeddings and enhance local spatial relation of 3D objects. Extensive results validate Mamba-3VL trumps other competitors on seven 3D-VL benchmarks and showcases versatile potentials for challenging Embodied AI tasks.

Yijing Lin, Mengqi Huang, Shuhan Zhuang, Zhendong Mao

Unifying diverse image generation tasks within a single framework remains a fundamental challenge in visual generation. While large language models (LLMs) achieve unification through task-agnostic data and generation, existing visual generation models fail to meet these principles. Current approaches either rely on per-task datasets and large-scale training or adapt pre-trained image models with task-specific modifications, limiting their generalizability. In this work, we explore video models as a foundation for unified image generation, leveraging their inherent ability to model temporal correlations. We introduce RealGeneral, a novel framework that reformulates image generation as a conditional frame prediction task, analogous to in-context learning in LLMs. To bridge the gap between video models and condition-image pairs, we propose (1) a Unified Conditional Embedding module for multi-modal alignment and (2) a Unified Stream DiT Block with decoupled adaptive LayerNorm and attention mask to mitigate cross-modal interference. RealGeneral demonstrates effectiveness in multiple important visual generation tasks, e.g., it achieves a 14.5% improvement in subject similarity for customized generation and a 10% enhancement in image quality for canny-to-image task. Project Page: https://lyne1.github.io/realgeneral_web/; GitHub Link: https://github.com/Lyne1/RealGeneral

Shunjie Yuan, Xinghua Li, Xuelin Cao, Haiyan Zhang, Mengyao Zhu, Robert H. Deng

Backdoor attacks have revealed the vulnerability of deep neural networks (DNNs), which motivates the development of secure deep learning systems. However, existing backdoor attacks often fail to bypass backdoor detection and human visual inspection, resulting in the exposure of the backdoor implanted in DNNs, which can subsequently be significantly mitigated through pruning or fine-tuning on benign data. To address this issue, in this paper, we propose a novel backdoor attack called SPD (Shallow Protecting Deep), which consists of a deep backdoor in the frequency domain and a shallow backdoor in the pixel domain, where the shallow backdoor acts as a firewall to protect the deep backdoor from being detected. Specifically, the deep backdoor in SPD samples from a specific Gaussian distribution, and encodes the sampled results into the intensity of the image's amplitude component in the frequency domain using an autoencoder, which serves as the backdoor trigger, thereby ensuring the invisibility of the backdoor attack. The shallow backdoor leverages traditional patch-based triggers, which covers all classes and attracts the defender's attention, thereby preserving the deep backdoor's resistance to existing backdoor detection techniques. Experimental results demonstrate that SPD not only can resist existing backdoor detection techniques, but also, due to the minimal disturbance caused by the backdoor trigger on benign samples, remains invisible, allowing the backdoor samples to pass through the human visual inspection. The source code of SPD is available at GitHub https://github.com/YuanShunJie1/SPD.

Xiwen Chen, Peijie Qiu, Wenhui Zhu, Hao Wang, Huayu Li, Xuanzhao Dong, Xiaotong Sun, Xiaobing Yu, Yalin Wang, Abolfazl Razi 等

While multiple instance learning (MIL) has shown to be a promising approach for histopathological whole slide image (WSI) analysis, its reliance on permutation invariance significantly limits its capacity to effectively uncover semantic correlations between instances within WSIs. Based on our empirical and theoretical investigations, we argue that approaches that are not permutation-invariant but better capture spatial correlations between instances can offer more effective solutions. In light of these findings, we propose a novel alternative to existing MIL for WSI analysis by learning to restore the order of instances from their randomly shuffled arrangement. We term this task as cracking an instance jigsaw puzzle problem, where semantic correlations between instances are uncovered. To tackle the instance jigsaw puzzles, we propose a novel Siamese network solution, which is theoretically justified by optimal transport theory. We validate the proposed method on WSI classification and survival prediction tasks, where the proposed method outperforms the recent state-of-the-art MIL competitors.

Yufei Wang, Lanqing Guo, Zhihao Li, Jiaxing Huang, Pichao Wang, Bihan Wen, Jian Wang

Text-guided image editing is an essential task, enabling users to modify images through natural language descriptions. Recent advances in diffusion models and rectified flows have significantly improved editing quality, primarily relying on inversion techniques to extract structured noise from input images. However, inaccuracies in inversion can propagate errors, leading to unintended modifications and compromising fidelity. Moreover, even with perfect inversion, the entanglement between textual prompts and image features often results in global changes when only local edits are intended. To address these challenges, we propose a novel text-guided image editing framework based on VAR (Visual AutoRegressive modeling), which eliminates the need for explicit inversion while ensuring precise and controlled modifications. Our method introduces a caching mechanism that stores token indices and probability distributions from the original image, capturing the relationship between the source prompt and the image. Using this cache, we design an adaptive fine-grained masking strategy that dynamically identifies and constrains modifications to relevant regions, preventing unintended changes. A token re-assembling approach further refines the editing process, enhancing diversity, fidelity, and control. Our framework operates in a training-free manner and achieves high-fidelity editing with faster inference speeds, processing a 1K resolution image in as fast as 1.2 seconds. Extensive experiments demonstrate that our method achieves performance comparable to, or even surpassing, existing diffusion- and rectified flow-based approaches in both quantitative metrics and visual quality. The code will be released.

Yicheng Feng, Yijiang Li, Wanpeng Zhang, Sipeng Zheng, Hao Luo, Zihao Yue, Zongqing Lu

We present VideoOrion, a Video Large Language Model (Video-LLM) that explicitly captures the key semantic information in videos--the spatial-temporal dynamics of objects throughout the videos. VideoOrion employs expert vision models to extract object dynamics through a detect-segment-track pipeline, encoding them into a set of object tokens by aggregating spatial-temporal object features. Our method addresses the persistent challenge in Video-LLMs of efficiently compressing high-dimensional video data into semantic tokens that are comprehensible to LLMs. Compared to prior methods which resort to downsampling the original video or aggregating visual tokens using resamplers, leading to information loss and entangled semantics, VideoOrion not only offers a more natural and efficient way to derive compact, disentangled semantic representations but also enables explicit object modeling of video content with minimal computational cost. Moreover, the introduced object tokens naturally allow VideoOrion to accomplish video-based referring tasks. Experimental results show that VideoOrion can learn to make good use of the object tokens, and achieves competitive results on both general video question answering and video-based referring benchmarks.

Jiahao Xia, Yike Wu, Wenjian Huang, Jianguo Zhang, Jian Zhang

Part-level features are crucial for image understanding, but few studies focus on them because of the lack of fine-grained labels. Although unsupervised part discovery can eliminate the reliance on labels, most of them cannot maintain robustness across various categories and scenarios, which restricts their application range. To overcome this limitation, we present a more effective paradigm for unsupervised part discovery, named Masked Part Autoencoder (MPAE). It first learns part descriptors as well as a feature map from the inputs and produces patch features from a masked version of the original images. Then, the masked regions are filled with the learned part descriptors based on the similarity between the local features and descriptors. By restoring these masked patches using the part descriptors, they become better aligned with their part shapes, guided by appearance features from unmasked patches. Finally, MPAE robustly discovers meaningful parts that closely match the actual object shapes, even in complex scenarios. Moreover, several looser yet more effective constraints are proposed to enable MPAE to identify the presence of parts across various scenarios and categories in an unsupervised manner. This provides the foundation for addressing challenges posed by occlusion and for exploring part similarity across multiple categories. Extensive experiments demonstrate that our method robustly discovers meaningful parts across various categories and scenarios. The code is available at the project website.

Yiming Wu, Huan Wang, Zhenghao Chen, Jianxin Pang, Dong Xu

Diffusion Policies have significantly advanced robotic manipulation tasks via imitation learning, but their application on resource-constrained mobile platforms remains challenging due to computational inefficiency and extensive memory footprint. In this paper, we propose LightDP, a novel framework specifically designed to accelerate Diffusion Policies for real-time deployment on mobile devices. LightDP addresses the computational bottleneck through two core strategies: network compression of the denoising modules and reduction of the required sampling steps. We first conduct an extensive computational analysis on existing Diffusion Policy architectures, identifying the denoising network as the primary contributor to latency. To overcome performance degradation typically associated with conventional pruning methods, we introduce a unified pruning and retraining pipeline, optimizing the model's post-pruning recoverability explicitly. Furthermore, we combine pruning techniques with consistency distillation to effectively reduce sampling steps while maintaining action prediction accuracy. Experimental evaluations on the standard datasets, i.e., PushT, Robomimic, CALVIN, and LIBERO, demonstrate that LightDP achieves real-time action prediction on mobile devices with competitive performance, marking an important step toward practical deployment of diffusion-based policies in resource-limited environments. Extensive real-world experiments also show the proposed LightDP can achieve performance comparable to state-of-the-art Diffusion Policies.

Longxin Kou, Fei Ni, Yan Zheng, Peilong Han, Jinyi Liu, Haiqin Cui, Rui Liu, Jianye Hao

Recent advances in robotics have produced numerous valuable large-scale demonstration datasets, yet their potential remains underutilized due to annotation limitations. Current datasets often suffer from sparse temporal annotations, and inconsistent labeling granularity, particularly for complex long-horizon demonstrations. Traditional manual annotation methods are expensive and poorly scalable while existing automated methods struggle with temporal coherence and semantic richness across extended demonstrations. For this, we propose RoboAnnotatorX, a reliable annotation tool that enhances multimodal large language model to generate high-quality, context-rich annotations for complex long-horizon demonstrations. Specifically, we introduce a multi-scale token-efficient encoder to maintain computational efficiency while simultaneously capturing fine-grained visual details and preserving temporal information by jointly integrating scene-level anchoring, clip-level temporal dynamics, and video-level global modeling. We further construct a comprehensive dataset RoboX-VQA that synthesizes diverse QA pairs from both real-world and simulated data, bridging the significant domain gap in robotics demonstrations. Moreover, we leverage a curriculum-inspired three-stage training to progressively develop capabilities from basic visual perception to sophisticated temporal reasoning. Extensive experiments demonstrate that RoboAnnotatorX significantly outperforms existing approaches in annotation quality and exhibits strong generalization across diverse robotic environments, helping unlock the full potential of existing robotic datasets.

Yujeong Chae, Heejun Park, Hyeonseong Kim, Kuk-Jin Yoon

Robust 3D object detection across diverse weather con- ditions is crucial for safe autonomous driving, and RADAR is increasingly leveraged for its resilience in adverse weather. Recent advancements have explored 4D RADAR and LiDAR-RADAR fusion to enhance 3D perception capabilities, specifically targeting weather robustness. However, existing methods often handle Doppler in ways that are not well-suited for multi-modal settings or lack tailored encoding strategies, hindering effective feature fusion and performance. To address these shortcomings, we propose a novel Doppler-aware LiDAR-4D RADAR fusion (DLR-Fusion) framework for robust 3D object detection. We introduce a multi-path iterative interaction module that integrates LiDAR, RADAR power, and Doppler, enabling a structured feature fusion process. Doppler highlights dynamic regions, refining RADAR power and enhancing LiDAR features across multiple stages, improving detection confidence. Extensive experiments on the K-RADAR dataset demonstrate that our approach effectively exploits Doppler information, achieving state-of-the-art performance in both normal and adverse weather conditions.