ICCV 2025 Papers

Image denoising is a fundamental challenge in computer vision, with applications in photography and medical imaging. While deep learning-based methods have shown remarkable success, their reliance on specific noise distributions limits generalization to unseen noise types and levels. Existing approaches attempt to address this with extensive training data and high computational resources but they still suffer from overfitting. To address these issues, we conduct image denoising by utilizing dynamically generated kernels via efficient operations. This approach helps prevent overfitting and improves resilience to unseen noise. Specifically, our method leverages a Feature Extraction Module for robust noise-invariant features, Global Statistics and Local Correlation Modules to capture comprehensive noise characteristics and structural correlations. The Kernel Prediction Module then employs these cues to produce pixel-wise varying kernels adapted to local structures, which are then applied iteratively for denoising. This ensures both efficiency and superior restoration quality. Despite being trained on single-level Gaussian noise, our compact model ( 0.04 M) excels across diverse noise types and levels, demonstrating the promise of iterative dynamic filtering for practical image denoising.

As large Vision-Language Models (VLMs) gain prominence, ensuring their safe deployment has become critical. Recent studies have explored VLM robustness against jailbreak attacks--techniques that exploit model vulnerabilities to elicit harmful outputs. However, the limited availability of diverse multimodal data has constrained current approaches to rely heavily on adversarial or manually crafted images derived from harmful text datasets, which often lack effectiveness and diversity across different contexts. In this paper, we propose IDEATOR, a novel jailbreak method that autonomously generates malicious image-text pairs for black-box jailbreak attacks. IDEATOR is grounded in the insight that VLMs themselves could serve as powerful red team models for generating multimodal jailbreak prompts. Specifically, IDEATOR leverages a VLM to create targeted jailbreak texts and pairs them with jailbreak images generated by a state-of-the-art diffusion model. Extensive experiments demonstrate IDEATOR's high effectiveness and transferability, achieving a 94% attack success rate (ASR) in jailbreaking MiniGPT-4 with an average of only 5.34 queries, and high ASRs of 82%, 88%, and 75% when transferred to LLaVA, InstructBLIP, and Chameleon, respectively. Building on IDEATOR's strong transferability and automated process, we introduce the VLJailbreakBench, a safety benchmark comprising 3,654 multimodal jailbreak samples. Our benchmark results on 11 recently released VLMs reveal significant gaps in safety alignment. For instance, our challenge set achieves ASRs of 46.31% on GPT-4o and 19.65% on Claude-3.5-Sonnet, underscoring the urgent need for stronger defenses.

Image generation has witnessed significant advancements in the past few years. However, evaluating the performance of image generation models remains a formidable challenge. In this paper, we propose ICE-Bench, a unified and comprehensive benchmark designed to rigorously assess image generation models. Its comprehensiveness could be summarized in the following key features: (1) Coarse-to-Fine Tasks: We systematically deconstruct image generation into four task categories: No-ref/Ref Image Creating/Editing, based on the presence or absence of source images and reference images. And further decompose them into 31 fine-grained tasks covering a broad spectrum of image generation requirements, culminating in a comprehensive benchmark. (2) Multi-dimensional Metrics: The evaluation framework assesses image generation capabilities across 6 dimensions: aesthetic quality, imaging quality, prompt following, source consistency, reference consistency, and controllability. 11 metrics are introduced to support the multi-dimensional evaluation. Notably, we introduce VLLM-QA, an innovative metric designed to assess the success of image editing by leveraging large models. (3) Hybrid Data: The data comes from real scenes and virtual generation, which effectively improves data diversity and alleviates the bias problem in model evaluation. Through ICE-Bench, we conduct a thorough analysis of existing generation models, revealing both the challenging nature of our benchmark and the gap between current model capabilities and real-world generation requirements. To foster further advancements in the field, we will open-source ICE-Bench, including its dataset, evaluation code, and models, thereby providing a valuable resource for the research community.

Despite modifying only a small localized input region, adversarial patches can drastically change the prediction of computer vision models. However, prior methods either cannot perform satisfactorily under targeted attack scenarios or fail to produce contextually coherent adversarial patches, causing them to be easily noticeable by human examiners and insufficiently stealthy against automatic patch defenses. In this paper, we introduce IAP, a novel attack framework that generates highly invisible adversarial patches based on perceptibility-aware localization and perturbation optimization schemes. Specifically, IAP first searches for a proper location to place the patch by leveraging classwise localization and sensitivity maps, balancing the susceptibility of patch location to both victim model prediction and human visual system, then employs a perceptibility-regularized adversarial loss and a gradient update rule that prioritizes color constancy for optimizing invisible perturbations. Comprehensive experiments across various image benchmarks and model architectures demonstrate that IAP consistently achieves competitive attack success rates in targeted settings with significantly improved patch invisibility compared to existing baselines. In addition to being highly imperceptible to humans, IAP is shown to be stealthy enough to render several state-of-the-art patch defenses ineffective.

Motion controllability is crucial in video synthesis. However, most previous methods are limited to single control types, and combining them often results in logical conflicts. In this paper, we propose a disentangled and unified framework, namely I2VControl, to overcome the logical conflicts. We rethink camera control, object dragging, and motion brush, reformulating all tasks into a consistent representation based on point trajectories, each managed by a dedicated formulation. Accordingly, we propose a spatial partitioning strategy, where each unit is assigned to a concomitant control category, enabling diverse control types to be dynamically orchestrated within a single synthesis pipeline without conflicts. Furthermore, we design an adapter structure that functions as a plug-in for pre-trained models and is agnostic to specific model architectures. We conduct extensive experiments, achieving excellent performance on various control tasks, and our method further facilitates user-driven creative combinations, enhancing innovation and creativity. Project page: https://wanquanf.github.io/I2VControl.

We present I2V3D, a novel framework for animating static images into dynamic videos with precise 3D control, leveraging the strengths of both 3D geometry guidance and advanced generative models. Our approach combines the precision of a computer graphics pipeline, enabling accurate control over elements such as camera movement, object rotation, and character animation, with the visual fidelity of generative AI to produce high-quality videos from coarsely rendered inputs. To support animations with any initial start point and extended sequences, we adopt a two-stage generation process guided by 3D geometry: 1) 3D-Guided Keyframe Generation, where a customized image diffusion model refines rendered keyframes to ensure consistency and quality, and 2) 3D-Guided Video Interpolation, a training-free approach that generates smooth, high-quality video frames between keyframes using bidirectional guidance. Experimental results highlight the effectiveness of our framework in producing controllable, high-quality animations from single input images by harmonizing 3D geometry with generative models.

Forecasting the evolution of 3D scenes and generating unseen scenarios through occupancy-based world models offers substantial potential to enhance the safety of autonomous driving systems. While tokenization has revolutionized image and video generation, efficiently tokenizing complex 3D scenes remains a critical challenge for 3D world models. To address this, we propose I^ 2 -World, an efficient framework for 4D occupancy forecasting. Our method decouples scene tokenization into intra-scene and inter-scene tokenizers. The intra-scene tokenizer employs a multi-scale residual quantization strategy to hierarchically compress 3D scenes while preserving spatial details. The inter-scene tokenizer residually aggregates temporal dependencies across timesteps. This dual design retains the compactness of 3D tokenizers while capturing the dynamic expressiveness of 4D approaches. Unlike decoder-only GPT-style autoregressive models, I^ 2 -World adopts an encoder-decoder architecture. The encoder aggregates spatial context from the current scene and predicts a transformation matrix to guide future scene generation. The decoder, conditioned on this matrix and historical tokens, ensures temporal consistency during generation. Experiments demonstrate that I^ 2 -World achieves state-of-the-art performance, surpassing existing approaches by 41.8% in 4D occupancy forecasting with exceptional efficiency--requiring only 2.9 GB of training memory and achieving real-time inference at 94.8 FPS.

Machine learning for image classification is an active and rapidly developing field. With the proliferation of classifiers of different sizes and different architectures, the problem of choosing the right model becomes more and more important. While we can assess a model's classification accuracy statistically, our understanding of the way these models work is unfortunately quite limited. In order to gain insight into the decision-making process of different vision models, we propose using minimal sufficient pixels sets. These pixels capture the essence of an image through the lens of the model. By comparing position, overlap and size of sets of pixels, we identify that different architectures have statistically different minimal pixels sets, in both size and position. In particular, ConvNext and EVA models differ markedly from the others. We also identify that images which are misclassified are associated with statistically significant larger pixels sets than correct classifications.

Existing hypergraph clustering methods typically assume that node attributes are fully available. However, in real-world scenarios, missing node attributes are common for the sake of privacy or due to data noise. While some approaches attempt to handle missing attributes in traditional graphs, they are not designed for hypergraphs, which encode higher-order relationships and introduce additional challenges. To bridge this gap, we propose Hypergraph Clustering Network with Partial Attribute Imputation (HCN-PAI). Specifically, we first leverage higher-order neighborhood propagation to impute missing node attributes by minimizing the Dirichlet energy, ensuring smooth feature propagation across the hypergraph. Next, we introduce a hypergraph smoothing preprocessing that efficiently captures structural information, replacing the hypergraph convolution operation, and significantly reducing computational costs. Finally, we design a dual-level contrastive mechanism, which employs two independent MLPs to encode node representations into two distinct views and enforces consistency at both node and hyperedge levels. Extensive experiments on multiple benchmark datasets validate the effectiveness and superiority of our proposed method.

Geometric constraints between feature matches are critical in 3D point cloud registration problems. Existing approaches typically model unordered matches as a consistency graph and sample consistent matches to generate hypotheses. However, explicit graph construction introduces noise, posing great challenges for handcrafted geometric constraints to render consistency. To overcome this, we propose HyperGCT, a flexible dynamic Hyper-GNN-learned geometric ConstrainT that leverages high-order consistency among 3D correspondences. To our knowledge, HyperGCT is the first method that mines robust geometric constraints from dynamic hypergraphs for 3D registration. By dynamically optimizing the hypergraph through vertex and edge feature aggregation, HyperGCT effectively captures the correlations among correspondences, leading to accurate hypothesis generation. Extensive experiments on 3DMatch, 3DLoMatch, KITTI-LC, and ETH show that HyperGCT achieves state-of-the-art performance. Furthermore, HyperGCT is robust to graph noise, demonstrating a significant advantage in terms of generalization.

Monocular depth estimation (MDE) is a fundamental problem in computer vision with wide-ranging applications in various downstream tasks. While multi-scale features are perceptually critical for MDE, existing transformer-based methods have yet to leverage them explicitly. To address this limitation, we propose a hypergraph-based multi-scale representation fusion framework, Hyper-Depth.The proposed Hyper-Depth incorporates two key components: a Semantic Consistency Enhancement (SCE) module and a Geometric Consistency Constraint (GCC) module. The SCE module, designed based on hypergraph convolution, aggregates global information and enhances the representation of multi-scale patch features. Meanwhile, the GCC module provides geometric guidance to reduce over-fitting errors caused by excessive reliance on local features. In addition, we introduce a Correlation-based Conditional Random Fields (C-CRFs) module as the decoder to filter correlated patches and compute attention weights more effectively.Extensive experiments demonstrate that our method significantly outperforms state-of-the-art approaches across all evaluation metrics on the KITTI and NYU-Depth-v2 datasets, achieving improvements of 6.21% and 3.32% on the main metric RMSE, respectively. Furthermore, zero-shot evaluations on the nuScenes and SUN-RGBD datasets validate the generalizability of our approach.

Few-shot image generation aims to generate diverse and high-quality images for an unseen class given only a few examples in that class. A key challenge in this task is balancing category consistency and image diversity, which often compete with each other. Moreover, existing methods offer limited control over the attributes of newly generated images. In this work, we propose Hyperbolic Diffusion Autoencoders (HypDAE), a novel approach that operates in hyperbolic space to capture hierarchical relationships among images from seen categories. By leveraging pre-trained foundation models, HypDAE generates diverse new images for unseen categories with exceptional quality by varying stochastic subcodes or semantic codes. Most importantly, the hyperbolic representation introduces an additional degree of control over semantic diversity through the adjustment of radii within the hyperbolic disk. Extensive experiments and visualizations demonstrate that HypDAE significantly outperforms prior methods by achieving a better balance between preserving category-relevant features and promoting image diversity with limited data. Furthermore, HypDAE offers a highly controllable and interpretable generation process.

End-to-end autonomous driving research currently faces a critical challenge in bridging the gap between open-loop training and closed-loop deployment. Current approaches are trained to predict trajectories in an open-loop environment, which struggle with quick reactions to other agents in closed-loop environments and risk generating kinematically infeasible plans due to the gap between open-loop training and closed-loop driving. In this paper, we introduce Hydra-NeXt, a novel multi-branch planning framework that unifies trajectory prediction, control prediction, and a trajectory refinement network in one model. Unlike current open-loop trajectory prediction models that only handle general-case planning, Hydra-NeXt further utilizes a control decoder to focus on short-term actions, which enables faster responses to dynamic situations and reactive agents. Moreover, we propose the Trajectory Refinement module to augment and refine the planning decisions by effectively adhering to kinematic constraints in closed-loop environments. This unified approach bridges the gap between open-loop training and closed-loop driving, demonstrating superior performance of 65.89 Driving Score (DS) and 48.20% Success Rate (SR) on the Bench2Drive dataset without relying on external experts for data collection. Hydra-NeXt surpasses the previous state-of-the-art by 22.98 DS and 17.49 SR, marking a significant advancement in autonomous driving.

Current self-supervised monocular depth estimation (MDE) approaches encounter performance limitations due to insufficient semantic-spatial knowledge extraction. To address this challenge, we propose Hybrid-depth, a novel framework that systematically integrates foundation models (e.g., CLIP and DINO) to extract visual priors and acquire sufficient contextual information for MDE. Our approach introduces a coarse-to-fine progressive learning framework: 1) Firstly, we aggregate multi-grained features from CLIP (global semantics) and DINO (local spatial details) under contrastive language guidance. A proxy task comparing close-distant image patches is designed to enforce depth-aware feature alignment using text prompts; 2) Next, building on the coarse features, we integrate camera pose information and pixel-wise language alignment to refine depth predictions. This module seamlessly integrates with existing self-supervised MDE pipelines (e.g., Monodepth2, ManyDepth) as a plug-and-play depth encoder, enhancing continuous depth estimation. By aggregating CLIP's semantic context and DINO's spatial details through language guidance, our method effectively addresses feature granularity mismatches. Extensive experiments on the KITTI benchmark demonstrate that our method significantly outperforms SOTA methods across all metrics, which also indeed benefits downstream tasks like BEV perception.

The Text-to-Video Retrieval (T2VR) task aims to retrieve unlabeled videos by textual queries with the same semantic meanings. Recent CLIP-based approaches have explored two frameworks: Two-Tower versus Single-Tower framework, yet the former suffers from low effectiveness, while the latter suffers from low efficiency. In this study, we explore a new Hybrid-Tower framework that can hybridize the advantages of the Two-Tower and Single-Tower framework, achieving high effectiveness and efficiency simultaneously. We propose a novel hybrid method, Fine-grained Pseudo-query Interaction and Generation for T2VR, i.e. \name , which includes a new pseudo-query generator designed to generate a pseudo-query for each video. This enables the video feature and the textual features of pseudo-query to interact in a fine-grained manner, similar to the Single-Tower approaches to hold high effectiveness, even before the real textual query is received. Simultaneously, our method introduces no additional storage or computational overhead compared to the Two-Tower framework during the inference stage, thus maintaining high efficiency. Extensive experiments on five commonly used text-video retrieval benchmarks demonstrate that our method achieves a significant improvement over the baseline, with an increase of 1.6% ~ 3.9% in R@1. Furthermore, our method matches the efficiency of Two-Tower models while achieving near state-of-the-art performance, highlighting the advantages of the Hybrid-Tower framework.

Continual Test Time Adaptation (CTTA) has emerged as a critical approach to bridge the domain gap between controlled training environments and real-world scenarios.Since it is important to balance the trade-off between adaptation and stabilization, many studies have tried to accomplish it by either introducing a regulation to fully trainable models or updating a limited portion of the models.This paper proposes Hybrid-TTA, a holistic approach that dynamically selects the instance-wise tuning method for optimal adaptation. Our approach introduces Dynamic Domain Shift Detection (DDSD), which identifies domain shifts by leveraging temporal correlations in input sequences, and dynamically switches between Full or Efficient Tuning for effective adaptation toward varying domain shifts. To maintain model stability, Masked Image Modeling Adaptation (MIMA) leverages auxiliary reconstruction task for enhanced generalization and robustness with minimal computational overhead.Hybrid-TTA achieves 0.6%p gain on the Cityscapes-to-ACDC benchmark dataset for semantic segmentation, surpassing previous state-of-the-art methods. It also delivers about 20-fold increase in FPS compared to the recently proposed fastest methods, offering a robust solution for real-world continual adaptation challenges.

Text-to-image generation models often struggle to interpret spatially aware text prompts effectively. To overcome this, existing approaches typically require millions of high-quality semantic layout annotations consisting of bounding boxes and regional prompts. This paper shows that the large amounts of regional prompts are non-necessary for the latest diffusion transformers like SD3 or FLUX.In this paper, we propose an efficient hybrid layout framework for diffusion transformers. Our approach drastically reduces need for extensive layout annotations and minimizes reliance on regional prompt annotations--incurring only minimal additional computational cost during inference--while maintaining high-quality layout adherence. Our key insight is to break the layout-control task into two sequential stages: first, generating the target objects within the designated regions specified by an anonymous layout, and second, refining these outputs to ensure they strictly adhere to the regional prompts in the semantic layout. Building on this insight, we propose a hybrid layout control scheme that first fine-tunes the DiTs (e.g., SD3) to follow an anonymous layout, then continues fine-tuning the DiTs to follow the semantic layout, and finally includes a quality-tuning stage to enhance visual aesthetics. We show that this hybrid design is highly data-efficient, as we find only using a small amount of semantic layout annotations is sufficient, thereby significantly reducing dependency on regional prompts. In addition, we propose an efficient regional diffusion transformer to encode the spatial layout information using just a set of lower-resolution regional tokens instead of various carefully designed layout tokens. The region-wise diffusion loss over these regional tokens can guide the diffusion transformer learn to follow the given layout implicitly. We empirically validate the effectiveness of our approach by comparing it with the latest version of SiamLayout and show that our method achieves better results while being more than 10 times more data efficient and ensuring superior aesthetics.

Most frame-based learned video codecs can be interpreted as recurrent neural networks (RNNs) propagating reference information along the temporal dimension. This work revisits the limitations of the current approaches from an RNN perspective. The output-recurrence methods, which propagate decoded frames, are intuitive but impose dual constraints on the output decoded frames, leading to suboptimal rate-distortion performance. In contrast, the hidden-to-hidden connection approaches, which propagate latent features within the RNN, offer greater flexibility but require large buffer sizes. To address these issues, we propose HyTIP, a learned video coding framework that combines both mechanisms. Our hybrid buffering strategy uses explicit decoded frames and a small number of implicit latent features to achieve competitive coding performance. Experimental results show that our HyTIP outperforms the sole use of either output-recurrence or hidden-to-hidden approaches. Furthermore, it achieves comparable performance to state-of-the-art methods but with a much smaller buffer size, and outperforms VTM 17.0 (Low-delay B) in terms of PSNR-RGB and MS-SSIM-RGB. The source code of HyTIP is available at https://github.com/NYCU-MAPL/HyTIP.

Recently, Mask2Former has achieved significant success as a universal image segmentation framework, with its Multi-Scale Deformable Attention (MSDeformAttn) Pixel Decoder becoming a widely adopted component in current segmentation models. However, the inefficiency of MSDeformAttn has become a performance bottleneck for segmenters. To address this, we propose the Hyper Pixel Decoder (HyPiDecoder), an improved Pixel Decoder design that replaces parts of the MSDeformAttn layers with convolution-based FPN layers, introducing explicit locality information and significantly boosting inference speed. Experimental results show that HyPiDecoder can be applied to both universal segmentation models and unified segmentation and detection models, achieving improvements in both speed and accuracy across object detection, semantic, instance, and panoptic segmentation tasks. The Mask DINO model integrated with HyPiDecoder achieves a new SOTA of 58.8 PQ on COCO panoptic segmentation with SwinL-scale backbone and no extra training data, with a 127% increase in inference speed compared to the original model. Code is available at \href https://github.com/HyPiDecoder/HybridPixelDecoder https://github.com/HyPiDecoder/HybridPixelDecoder .