Synthetic images serve as a promising alternative to real images in 3D hand pose estimation, providing accurate annotations at a lower cost. However, the domain gap between real and synthetic images constrains the generalization ability of hand pose estimation trained on synthetic data. Previous methods rely on Generative Adversarial Networks (GANs) for domain translation; however, they fail to achieve realistic depth synthesis due to instability and limited image quality. Diffusion models provide high-quality synthesis due to their stability and controllability. However, existing methods often ignore the 3D structure awareness in hand image generation. In this paper, we propose a Dual-Branch 3D Spatial-Aware Latent Diffusion (DSW-LD) for realistic depth image generation. The Global Structure Module (GSM) and the Local Geometry Module (LGM) complement each other, with GSM capturing global spatial structure through coarse-grained 3D joint features and LGM focusing on local geometric details using fine-grained 3D mesh representations. To maintain the global structure consistency, we adopt a layer-aware injection mechanism that enables the model to adaptively learn the optimal representation from fused 2D latent representations and 3D joint features. To explicitly align 3D and 2D features of local regions and enhance the flexibility of feature matching, we design a dynamic depth-aware interpolation to project 3D mesh features into 2D image space. Both quantitative and qualitative experimental results demonstrate the superiority of our method over the state-of-the-arts for realistic depth synthesis. Compared to training only on real depth images, our method enables the hand pose estimator to achieve significantly better performance with our synthetic data and less real data (10%).
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3D digital garment generation and editing play a pivotal role in fashion design, virtual try-on, and gaming. Traditional methods struggle to meet the growing demand due to technical complexity and high resource costs. Learning-based approaches offer faster, more diverse garment synthesis based on specific requirements and reduce human efforts and time costs. However, they still face challenges such as inconsistent multi-view geometry or textures and heavy reliance on detailed garment topology and manual rigging. We propose SemanticGarment, a 3D Gaussian-based method that realizes high-fidelity 3D garment generation from text or image prompts and supports semantic-based interactive editing for flexible user customization. To ensure multi-view consistency and garment fitting, we propose to leverage structural human priors for the generative model by introducing a 3D semantic clothing model, which initializes the geometry structure and lays the groundwork for view-consistent garment generation and editing. Without the need to regenerate or rely on existing mesh templates, our approach allows for rapid and diverse modifications to existing Gaussians, either globally or within a local region. To address the artifacts caused by self-occlusion for garment reconstruction based on single image, we develop a self-occlusion optimization strategy to mitigate holes and artifacts that arise when directly animating self-occluded garments. Extensive experiments are conducted to demonstrate our superior performance in 3D garment generation and editing.
Existing prototype learning-based Multiple Instance Learning (MIL) methods mainly focus on learning a single set of prototypes for each class or generating a generic prototype from the overall data distribution. This design forces the model to compress general and heterogeneous features into identical prototype embeddings, prioritizing general features over subtle but discriminative features. Additionally, these methods often guide prototype updates by jointly optimizing attention score distributions and the distances between instances and prototypes, resulting in prototype biases due to over-concentration. To address these issues, we propose a dual prototype learning MIL (DP-MIL) framework that introduces two distinct sets of prototypes: primary prototypes, which capture general WSI features, and boundary prototypes, which capture discriminative features near the decision boundary. The DP-MIL framework employs three prototype-tailored losses: an alienation loss to encourage primary prototypes to be distant from decision boundaries, an affinity loss to anchor boundary prototypes near these boundaries, and a distance loss to enforce separation between the two prototype sets. To mitigate prototype semantic drift during training, we introduce a prototype joint updating and refinement strategy: for each prototype, we use its corresponding global token to filter out the most similar instances to momentum update the corresponding prototype set, while the boundary prototype set is refined with the mean pooled feature of hard samples. Extensive experiments on four datasets demonstrate the effectiveness of our DP-MIL framework and prototype updating strategy.
3D Gaussian Splatting (3DGS) has revolutionized 3D scene reconstruction, which effectively balances rendering quality, efficiency, and speed. However, existing 3DGS approaches usually generate plausible outputs and face significant challenges in complex scene reconstruction, manifesting as incomplete holistic structural outlines and unclear local lighting effects. To address these issues simultaneously, we propose a novel decoupled optimization framework, which integrates wavelet decomposition into 3D Gaussian Splatting and 2D sampling. Technically, through 3D wavelet decomposition, our approach divides point clouds into high-frequency and low-frequency components, enabling targeted optimization for each. The low-frequency component captures global structural outlines and manages the distribution of Gaussians through voxelization. In contrast, the high-frequency component restores intricate geometric and textural details while incorporating a relight module to mitigate lighting artifacts and enhance photorealistic rendering. Additionally, a 2D wavelet decomposition is applied to the training images, simulating radiance variations. This provides critical guidance for high-frequency detail reconstruction, ensuring seamless integration of details with the global structure. Extensive experiments on challenging datasets demonstrate our method achieves state-of-the-art performance across various metrics, surpassing existing approaches and advancing the field of 3D scene reconstruction.
In recent years, adversarial attacks on video recognition models have attracted increasing attention. However, most existing strategies are extensions of image-based methods, where adversarial perturbations are computed independently and embedded into individual frames. This independent per-frame perturbation process wastes computational resources and leads to excessive query consumption. To address this problem, we introduce Frequency Domain Distributed Perturbations (FDP), a straightforward yet effective black-box video attack method using temporal correlations between video frames. Specifically, FDP first converts the input video into the frequency domain and calculates globally coordinated adversarial perturbations in the spectral space. By conducting global optimization in the frequency domain, FDP improves the effectiveness of each query, significantly decreasing the total number of queries needed. The resulting perturbations are temporally distributed across frames to preserve the spatiotemporal structure. Furthermore, we introduce a frequency-sensitive mask to identify the spectral regions most critical to the model's predictions. By applying perturbations only to these key frequency bands, FDP further reduces the perturbation search space and improves query efficiency. Extensive experiments demonstrate that our method significantly reduces query consumption while achieving higher attack success rates than state-of-the-art approaches.
3D Gaussian Splatting has exhibited remarkable capabilities in 3D scene reconstruction. However, reconstructing high-quality 3D scenes from motion-blurred images caused by camera motion poses a significant challenge. The performance of existing 3DGS-based deblurring methods are limited due to their inherent mechanisms, such as extreme dependence on the accuracy of camera poses and inability to effectively control erroneous Gaussian primitives densification caused by motion blur. To solve these problems, we introduce a novel framework, Bi-Stage 3D Gaussian Splatting, to accurately reconstruct 3D scenes from motion-blurred images. BSGS contains two stages. First, Camera Pose Refinement roughly optimizes camera poses to reduce motion-induced distortions. Second, with fixed rough camera poses, Global Rigid Transformation further corrects motion-induced blur distortions. To alleviate multi-subframe gradient conflicts, we propose a subframe gradient aggregation strategy to optimize both stages. Furthermore, a space-time bi-stage optimization strategy is introduced to dynamically adjust primitive densification thresholds and prevent premature noisy Gaussian generation in blurred regions. Comprehensive experiments verify the effectiveness of our proposed deblurring method and show its superiority over the state of the arts.
DINOv2 Driven Gait Representation Learning for Video-Based Visible-Infrared Person Re-identification
Video-based Visible-Infrared person re-identification (VVI-ReID) aims to retrieve the same pedestrian across visible and infrared modalities from video sequences. Existing methods tend to exploit modality-invariant visual features but largely overlook gait features, which are not only modality-invariant but also rich in temporal dynamics, thus limiting their ability to model the spatiotemporal consistency essential for cross-modal video matching. To address these challenges, we propose a DINOv2-Driven Gait Representation Learning (DinoGRL) framework that leverages the rich visual priors of DINOv2 to learn gait features complementary to appearance cues, facilitating robust sequence-level representations for cross-modal retrieval. Specifically, we introduce a Semantic-Aware Silhouette and Gait Learning (SASGL) model, which generates and enhances silhouette representations with general-purpose semantic priors from DINOv2 and jointly optimizes them with the ReID objective to achieve semantically enriched and task-adaptive gait feature learning. Furthermore, we develop a Progressive Bidirectional Multi-Granularity Enhancement (PBMGE) module, which progressively refines feature representations by enabling bidirectional interactions between gait and appearance streams across multiple spatial granularities, fully leveraging their complementarity to enhance global representations with rich local details and produce highly discriminative features. Extensive experiments on HITSZ-VCM and BUPT datasets demonstrate the superiority of our approach, significantly outperforming existing state-of-the-art methods.
Recent advancements in image editing have utilized large-scale multimodal models to enable intuitive, natural instruction-driven interactions. However, conventional methods still face significant challenges, particularly in spatial reasoning, precise region segmentation, and maintaining semantic consistency, especially in complex scenes.To overcome these challenges, we introduce SmartFreeEdit, a novel end-to-end framework that integrates a multimodal large language model (MLLM) with a hypergraph-enhanced inpainting architecture, enabling precise, mask-free image editing guided exclusively by natural language instructions. The key innovations of SmartFreeEdit include: (1) the introduction of region-aware tokens and a mask embedding paradigm that enhance the model's spatial understanding of complex scenes; (2) a reasoning segmentation pipeline designed to optimize the generation of editing masks based on natural language instructions; and (3) a hypergraph-augmented inpainting module that ensures the preservation of both structural integrity and semantic coherence during complex edits, overcoming the limitations of local-based image generation. Extensive experiments on the Reason-Edit benchmark demonstrate that SmartFreeEdit surpasses current state-of-the-art methods across multiple evaluation metrics, including segmentation accuracy, instruction adherence, and visual quality preservation, while addressing the issue of local information focus and improving global consistency in the edited image. Our project will be available at https://github.com/smileformylove/SmartFreeEdit.
Accurate prediction of placental diseases via whole slide images (WSIs) is critical for preventing severe maternal and fetal complications. However, WSI analysis presents significant computational challenges due to the massive data volume. Existing WSI classification methods encounter critical limitations: (1) inadequate patch selection strategies that either compromise performance or fail to sufficiently reduce computational demands, and (2) the loss of global histological context resulting from patch-level processing approaches. To address these challenges, we propose an Efficient multimodal framework for Patient-level placental disease Diagnosis, named EmmPD. Our approach introduces a two-stage patch selection module that combines parameter-free and learnable compression strategies, optimally balancing computational efficiency with critical feature preservation. Additionally, we develop a hybrid multimodal fusion module that leverages adaptive graph learning to enhance pathological feature representation and incorporates textual medical reports to enrich global contextual understanding. Extensive experiments conducted on both a self-constructed patient-level Placental dataset and two public datasets demonstrating that our method achieves state-of-the-art diagnostic performance. The code is available at https://github.com/ECNU-MultiDimLab/EmmPD.
Local motion blur in digital images originates from the relative motion between dynamic objects and static imaging systems during exposure. Existing deblurring methods face significant challenges in addressing this problem due to their inefficient allocation of computational resources and inadequate handling of spatially varying blur patterns. To overcome these limitations, we first propose a trainable mask predictor that identifies blurred regions in the image. During training, we employ blur masks to exclude sharp regions. For inference optimization, we implement structural reparameterization by converting 3 x 3 convolutions to computationally efficient 1 x 1 convolutions, enabling pixel-level pruning of sharp areas to reduce computation. Second, we develop an intra-frame motion analyzer that translates relative pixel displacements into motion trajectories, establishing adaptive guidance for region-specific blur restoration. Our method is trained end-to-end using a combination of reconstruction loss, reblur loss, and mask loss guided by annotated blur masks. Extensive experiments demonstrate superior performance over state-of-the-art methods on both local and global blur datasets while reducing FLOPs by 49% compared to SOTA models (e.g., LMD-ViT). The source code is available at https://github.com/shangwei5/M2AENet.
In 3D point cloud object tracking, the motion-centric methods have emerged as a promising avenue due to its superior performance in modeling inter-frame motion. However, existing two-stage motion-based approaches suffer from fundamental limitations: (1) error accumulation due to decoupled optimization caused by explicit foreground segmentation prior to motion estimation, and (2) computational bottlenecks from sequential processing. To address these challenges, we propose FocusTrack, a novel one-stage paradigms tracking framework that unifies motion-semantics co-modeling through two core innovations: Inter-frame Motion Modeling (IMM) and Focus-and-Suppress Attention. The IMM module employs a temp-oral-difference siamese encoder to capture global motion patterns between adjacent frames. The Focus-and-Suppress attention that enhance the foreground semantics via motion-salient feature gating and suppress the background noise based on the temporal-aware motion context from IMM without explicit segmentation. Based on above two designs, FocusTrack enables end-to-end training with compact one-stage pipeline. Extensive experiments on prominent 3D tracking benchmarks, such as KITTI, nuScenes, and Waymo, demonstrate that the FocusTrack achieves new SOTA performance while running at a high speed with 105 FPS.
Dense multi-label action detection in untrimmed long videos is a formidable task, with end-to-end training particularly challenging due to computational constraints, typically involving separate stages of off-the-shelf feature extraction and subsequent global modeling for action prediction.Existing methods fail to optimize all modules jointly for better performance. We introduce FreETAD, a Frequency-based End-to-end Temporal Action Detection approach, which shifts the focus from local actionness scores to frequency component estimation. Using the short-term Fourier Transform, FreETAD reconstructs the global action curve seamlessly. With a DETR-like decoder and frequency-encoded vectors for queries, it enhances multi-scale time-frequency interactions. FreETAD leverages end-to-end training effectively, boosting the mAP by 1.5% on Charades and 2.7% on MultiTHUMOS.
Super-resolution (SR) images, generated by advanced algorithms to enhance resolution under hardware constraints, are increasingly applied across various multimedia tasks. However, the absence of paired high-resolution (HR) reference image and the inherent ill-posedness of SR reconstruction present key challenges for SR image quality assessment (SR-IQA). Full-reference methods become inapplicable, while the reduced-reference methods relying on one low-resolution (LR) image offer limited reliability. To address these issues, I propose the SQer, a no-reference SR-IQA method based on a graph perceptron with semantic fidelity. The SQer first extracts perceptual and hierarchical SR image features using a superposition nonlinear feature pooling. These features are transformed into graph vector representations, allowing semantic information learning via a graph-structured attention perceptron. Finally, the resulting graphs are globally average-pooled into a semantic embedding, which is then processed by a multilayer perceptron to predict the SR image quality score. Extensive experiments on multiple SR-IQA benchmarks demonstrate that my proposed SQer significantly outperforms existing state-of-the-art reference-based methods, exhibiting superior accuracy and a stronger ability to capture fine-grained perceptual cues and SR-specific artifacts. The SQer method provides a promising direction for guiding the optimization and application of image super-resolution models.
We focus on the approximate nearest neighbor search (ANNS) in high dimensional space, which is a fundamental technique in computer vision and multimedia database. Among the ANNS solutions, graph-based approaches achieve excellent performance by executing a routing algorithm on a proximity graph to retrieve the nearest neighbors. However, most of their routing strategies are heuristic-based greedy routing, leading to suboptimal search results with large number of hops. In this paper, we propose a novel routing paradigm on graphs for ANNS problem by deep reinforcement learning. We design a reinforcement model to learn the routing policy by making use of both graph global and local topology information. A hops-optimized reward mechanism is devised to enable the model to be more efficient and effective. The final searching algorithm with the learned model is able to find the nearest neighbors without any backtracking in a small number of hops. Comprehensive experiments on real-world datasets demonstrate the superiorities of the proposed method over the state-of-the-art ANNS approaches.
Image captioning bridges the gap between visual perception and natural language understanding by transforming image content into descriptive text. While existing methods have made significant progress in visual feature extraction, encoding, and cross-modal semantic alignment, challenges remain in terms of fine-grained feature representation, cross-modal alignment efficiency, and suboptimal search strategies. To address these issues, a multimodal-guided and search space-optimized image captioning model is proposed. In the visual encoding stage, we construct a hierarchical network that integrates regional and grid features through a geometry-constrained multi-layer feature aggregation mechanism, which enhances the model's capability to jointly capture global semantics and local details. In the decoding stage, we introduce a dynamic grouped beam width adjustment strategy to improve semantic path exploration. Additionally, a diversity-driven scoring function is designed to enforce intra-group diversity rewards and inter-group similarity penalties, encouraging the generation of more diverse captions. Finally, we incorporate a two-level pruning algorithm based on syntactic and spatial logic constraints to refine the search space from both hard and soft constraint perspectives, improving both the accuracy and diversity of generated captions. A 3% improvement in CIDEr is achieved by the proposed method over state-of-the-art (SOTA) models, as demonstrated by experiments on the COCO and Flickr30k datasets.
Monocular 3D lane detection is a challenging task for autonomous driving systems. Recent advances primarily focus on one-step methods for lane detection based on front-view features, which show promising results on straight lanes. However, curved lanes are difficult to handle with one-step prediction, which performs prediction in a single leap without gradual refinement. To address this issue, we propose a novel Denoising Diffusion Model for 3D Lane Detection framework (D3L). The main idea is to leverage the progressive generation capability of the diffusion model to generate accurate 3D curved lanes, and ensuring lane continuity through curvature constraints. The framework includes three creative components: coarse-to-fine denoiser (CFD), curvature-constrained loss (CCL) and multi-sampling aggregation strategy (MSAS). In CFD, both lane-level and point-level transformer blocks are integrated to accurately denoise 3D lanes, which effectively captures both global and local features. CCL is designed to reduce deviations in lane curvature, resulting in smoother lane continuity. This loss enhances both the accuracy and geometric consistency of lane detection, especially in complex curved scenes. MSAS is proposed to select the optimal lane point-by-point from multiple candidates, thus robustness of the lane prediction is significantly improved. Extensive experiments on two popular 3D lane detection benchmarks demonstrate that our D3 L outperforms the state-of-the-art methods.
The rapid evolution of deepfake generation technologies necessitates the development of robust face forgery detection algorithms. Recent studies have demonstrated that wavelet analysis can enhance the generalization abilities of forgery detectors. Wavelets effectively capture key facial contours, often slender, fine-grained, and globally distributed, that may conceal subtle forgery artifacts imperceptible in the spatial domain. However, current wavelet-based approaches fail to fully exploit the distinctive properties of wavelet data, resulting in sub-optimal feature extraction and limited performance gains. To address this challenge, we introduce WMamba, a novel wavelet-based feature extractor built upon the Mamba architecture. WMamba maximizes the utility of wavelet information through two key innovations. First, we propose Dynamic Contour Convolution (DCConv), which employs specially crafted deformable kernels to adaptively model slender facial contours. Second, by leveraging the Mamba architecture, our method captures long-range spatial relationships with linear complexity. This efficiency allows for the extraction of fine-grained, globally distributed forgery artifacts from small image patches. Extensive experiments show that WMamba achieves state-of-the-art (SOTA) performance, highlighting its effectiveness in face forgery detection.
Editing long videos remains a challenging task due to the need for maintaining both global consistency and temporal coherence across thousands of frames. Existing methods often suffer from structural drift or temporal artifacts, particularly in minute-long sequences. We introduce AnchorSync, a novel diffusion-based framework that enables high-quality, long-term video editing by decoupling the task into sparse anchor frame editing and smooth intermediate frame interpolation. Our approach enforces structural consistency through a progressive denoising process and preserves temporal dynamics via multimodal guidance. Extensive experiments show that AnchorSync produces coherent, high-fidelity edits, surpassing prior methods in visual quality and temporal stability. The source code is available at https://github.com/VISION-SJTU/AnchorSync.
Multi-view 3D reconstruction has remained an essential yet challenging problem in the field of computer vision. While DUSt3R and its successors have achieved breakthroughs in 3D reconstruction from unposed images, these methods exhibit significant limitations when scaling to multi-view scenarios, including high computational cost and cumulative error induced by global alignment. To address these challenges, we propose Regist3R, a novel stereo foundation model tailored for efficient and scalable incremental reconstruction. Regist3R leverages an incremental reconstruction paradigm, enabling large-scale 3D reconstructions from unordered and many-view image collections. We evaluate Regist3R on public datasets for camera pose estimation and 3D reconstruction. Our experiments demonstrate that Regist3R achieves comparable performance with optimization-based methods while significantly improving computational efficiency, and outperforms existing multi-view reconstruction models. Furthermore, to assess its performance in real-world applications, we introduce a challenging oblique aerial dataset which has long spatial spans and hundreds of views. The results highlight the effectiveness of Regist3R. We also demonstrate the first attempt to reconstruct large-scale scenes encompassing over thousands of views through pointmap-based foundation models, showcasing its potential for practical applications in large-scale 3D reconstruction tasks, including urban modeling, aerial mapping, and beyond.
NavigScene: Bridging Local Perception and Global Navigation for Beyond-Visual-Range Autonomous Driving
PDF ↗Autonomous driving systems have made significant advances in Q&A, perception, prediction, and planning based on local visual information, yet they struggle to incorporate broader navigational context that human drivers routinely utilize. We address this critical gap between local sensor data and global navigation information by proposing NavigScene, an auxiliary navigation-guided natural language dataset that simulates a human-like driving environment within autonomous driving systems. Moreover, we develop three complementary paradigms to leverage NavigScene: (1) Navigation-guided Reasoning, which enhances vision-language models by incorporating navigation context into the prompting approach; (2) Navigation-guided Preference Optimization, a reinforcement learning method that extends Direct Preference Optimization to improve vision-language model responses by establishing preferences for navigation-relevant summarized information; and (3) Navigation-guided Vision-Language-Action model, which integrates navigation guidance and vision-language models with conventional driving models through feature fusion. Extensive experiments demonstrate that our approaches significantly improve performance across perception, prediction, planning, and question-answering tasks by enabling reasoning capabilities beyond visual range and improving generalization to diverse driving scenarios. This work represents a significant step toward more comprehensive autonomous driving systems capable of navigating complex, unfamiliar environments with greater reliability and safety.