Image-based feature representation plays a critical role in visual localization, enabling robots to estimate their position and orientation in GPS-denied environments. However, this task is often undermined by significant variations in camera viewpoints and scene appearances. Recently, map-free visual relocalization (MFVR) has emerged as a promising paradigm due to its compatibility with lightweight deployment and privacy isolation on mobile devices. In this paper, we propose the Debiased Multiplex Tokenizer (DeMT) as a novel method for versatile and efficient MFVR. Specifically, DeMT performs relative pose regression through an integrated framework built upon a pretrained vision Mamba encoder, comprising three key modules: First, Multiplex Interactive Tokenization yields robust image tokens with non-local affinities and cross-domain descriptions; Second, Debiased Anchor Registration facilitates anchor token matching through proximity graph retrieval and causal pointer attribution; Third, Geometry-Informed Pose Regression empowers multi-layer perceptrons with a gating mechanism and spectral normalization to support both pair-wise and multi-view modes. Extensive evaluations across nine public datasets demonstrate that DeMT substantially outperforms existing baselines and ablation variants in diverse indoor and outdoor environments.
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Vision-based 3D Semantic Scene Completion (SSC) has received growing attention due to its potential in autonomous driving. While most existing approaches follow an ego-centric paradigm by aggregating and diffusing features over the entire scene, they often overlook fine-grained object-level details, leading to semantic and geometric ambiguities, especially in complex environments. To address this limitation, we propose Ocean, an object-centric prediction framework that decomposes the scene into individual object instances to enable more accurate semantic occupancy prediction. Specifically, we first employ a lightweight segmentation model, MobileSAM, to extract instance masks from the input image. Then, we introduce a 3D Semantic Group Attention module that leverages linear attention to aggregate object-centric features in 3D space. To handle segmentation errors and missing instances, we further design a Global Similarity-Guided Attention module that leverages segmentation features for global interaction. Finally, we propose an Instance-aware Local Diffusion module that improves instance features through a generative process and subsequently refines the scene representation in the BEV space. Extensive experiments on the SemanticKITTI and SSCBench-KITTI360 benchmarks demonstrate that Ocean achieves state-of-the-art performance, with mIoU scores of 17.40 and 20.28, respectively.
We propose a novel one-stage method, NVB-Face, for generating consistent Novel-View images directly from a single Blind Face image. Existing approaches to novel-view synthesis for objects or faces typically require a high-resolution RGB image as input. When dealing with degraded images, the conventional pipeline follows a two-stage process: first restoring the image to high resolution, then synthesizing novel views from the restored result. However, this approach is highly dependent on the quality of the restored image, often leading to inaccuracies and inconsistencies in the final output. To address this limitation, we extract single-view features directly from the blind face image and introduce a feature manipulator that transforms these features into 3D-aware, multi-view latent representations. Leveraging the powerful generative capacity of a diffusion model, our framework synthesizes high-quality, consistent novel-view face images. Experimental results show that our method significantly outperforms traditional two-stage approaches in both consistency and fidelity.
Although geometric reconstruction of general objects from images has made remarkable progress in recent years, slender structures remain largely underexplored, despite their critical importance in engineering, biomedical, and agricultural applications. To bridge this gap, we propose a dedicated 2DGS-based geometric reconstruction framework tailored for slender structures, achieving accurate and faithful geometry recovery. Our method first addresses the challenge that most slender objects are texture-less, which hinders reliable feature matching and pose estimation in traditional SfM pipelines. By leveraging the curve-like nature of slender structures, we perform a curve-guided SfM process that provides robust camera poses and accurate 3D curve initialization for Gaussian primitives. To ensure SfM reliability, we introduce a high-precision mask extraction strategy that integrates geometric priors with a segmentation network, effectively handling self-occlusion and thin geometry. Furthermore, to enhance fine geometric recovery, we incorporate a differentiable Poisson reconstruction module to extract an initial mesh during training, which is then refined via image-space iterative optimization using differentiable mesh rasterization. In contrast to conventional approaches that rely on differentiable Gaussian rasterization followed by TSDF-based mesh extraction, our method avoids the additional geometric errors and artifacts introduced during the intermediate TSDF conversion, thereby improving the overall reconstruction quality. Comprehensive experiments on both synthetic and real-world datasets validate that our method achieves superior reconstruction quality compared to state-of-the-art approaches.
GEWDiff: Geometric Enhanced Wavelet-based Diffusion Model for Hyperspectral Image Super-resolution
PDF ↗Improving the quality of hyperspectral images (HSIs), such as through super-resolution, is a crucial research area. However, generative modeling for HSIs presents several challenges. Due to their high spectral dimensionality, HSIs are too memory-intensive for direct input into conventional diffusion models. Furthermore, general generative models lack an understanding of the topological and geometric structures of ground objects in remote sensing imagery. In addition, most diffusion models optimize loss functions at the noise level, leading to a non-intuitive convergence behavior and suboptimal generation quality for complex data. To address these challenges, we propose a Geometric Enhanced Wavelet-based Diffusion Model (GEWDiff), a novel framework for reconstructing hyperspectral images at 4-times super-resolution. A wavelet-based encoder-decoder is introduced that efficiently compresses HSIs into a latent space while preserving spectral-spatial information. To avoid distortion during generation, we incorporate a geometry-enhanced diffusion process that preserves the geometric features. Furthermore, a multi-level loss function was designed to guide the diffusion process, promoting stable convergence and improved reconstruction fidelity. Our model demonstrated state-of-the-art results across multiple dimensions, including fidelity, spectral accuracy, visual realism, and clarity.
CaTFormer: Causal Temporal Transformer with Dynamic Contextual Fusion for Driving Intention Prediction
PDF ↗Accurate prediction of driving intention is key to enhancing the safety and interactive efficiency of human-machine co-driving systems. It serves as a cornerstone for achieving high-level autonomous driving. However, current approaches remain inadequate for accurately modeling the complex spatiotemporal interdependencies and the unpredictable variability of human driving behavior. To address these challenges, we propose CaTFormer, a causal Temporal Transformer that explicitly models causal interactions between driver behavior and environmental context for robust intention prediction. Specifically, CaTFormer introduces a novel Reciprocal Delayed Fusion (RDF) mechanism for precise temporal alignment of interior and exterior feature streams, a Counterfactual Residual Encoding (CRE) module that systematically eliminates spurious correlations to reveal authentic causal dependencies, and an innovative Feature Synthesis Network (FSN) that adaptively synthesizes these purified representations into coherent temporal representations. Experimental results demonstrate that CaTFormer attains state-of-the-art performance on the Brain4Cars dataset. It effectively captures complex causal temporal dependencies and enhances both the accuracy and transparency of driving intention prediction.
PipeDiT: Accelerating Diffusion Transformers in Video Generation with Task Pipelining and Model Decoupling
PDF ↗Video generation has been advancing rapidly, and diffusion transformer (DiT) based models have demonstrated remarkable capabilities. However, their practical deployment is often hindered by slow inference speeds and high memory consumption. In this paper, we propose a novel pipelining framework named PipeDiT to accelerate video generation, which is equipped with three main innovations. First, we design a pipelining algorithm (PipeSP) for sequence parallelism (SP) to enable the computation of latent generation and communication among multiple GPUs to be pipelined, thus reducing the inference latency. Second, we propose DeDiVAE to decouple the diffusion module and the VAE module into two GPU groups whose executions can also be pipelined to reduce the memory consumption and inference latency. Third, to better utilize the GPU resources in the VAE group, we propose an attention co-processing (Aco) method to further reduce the overall video generation latency. We integrate our PipeDiT into both OpenSoraPlan and HunyuanVideo, two state-of-the-art open-source video generation frameworks, and conduct extensive experiments on two 8-GPU systems. Experimental results show that, under many common resolution and timestep configurations, our PipeDiT achieves 1.06× to 4.02× speedups over OpenSoraPlan and HunyuanVideo.
Mainstream multimodal large language models (MLLMs) rely on patch-based tokenization methods, which compromise the integrity of objects and thereby limit the model's perception capabilities while triggering object-related hallucinations. To address this issue, we propose ObjecTok, an innovative object tokenization framework. ObjecTok generates a single, holistic object token for each object in an image. This token is produced by a specially trained object encoder that embeds the object's semantic, positional, and shape information into a single compact representation, thereby preserving the object's integrity. To mitigate the imperfections of upstream object proposer models, we introduce learnable confidence embeddings. These embeddings enable the MLLM to learn the reliability of each object's information, significantly enhancing the model's robustness. Additionally, ObjecTok employs a hybrid input strategy, combining object tokens with traditional image patch tokens, allowing the model to leverage both object-level information and global scene context. By integrating ObjecTok into the LLaVA architecture, we achieve notable performance improvements on multiple object-centric benchmarks, effectively reducing object hallucinations and enhancing perception capabilities. Experimental results robustly demonstrate that the object tokens generated by our ObjecTok framework hold great potential for building more powerful and reliable MLLMs.
Vision-Language Navigation (VLN) tasks often leverage panoramic RGB and depth inputs to provide rich spatial cues for action planning, but these sensors can be costly or less accessible in real-world deployments. Recent approaches based on Vision-Language Action (VLA) models achieve strong results with monocular input, yet they still lag behind methods using panoramic RGB-D information. We present MonoDream, a lightweight VLA framework that enables monocular agents to learn a Unified Navigation Representation (UNR). This shared feature representation jointly aligns navigation-relevant visual semantics (e.g., global layout, depth, and future cues) and language-grounded action intent, enabling more reliable action prediction. MonoDream further introduces Latent Panoramic Dreaming (LPD) tasks to supervise the UNR, which train the model to predict latent features of panoramic RGB and depth observations at both current and future steps based on only monocular input. Experiments on multiple VLN benchmarks show that MonoDream consistently improves monocular navigation performance and significantly narrows the gap with panoramic-based agents.
Exploring Modality-Aware Fusion and Decoupled Temporal Propagation for Multi-Modal Object Tracking
PDF ↗Most existing multi-modal trackers adopt uniform fusion strategies, overlooking the inherent differences between modalities. Moreover, they propagate temporal information through mixed tokens, leading to entangled and less discriminative temporal representations. To address these limitations, we propose MDTrack, a novel framework for modality-aware fusion and decoupled temporal propagation in multi-modal object tracking. Specifically, for modality-aware fusion, we allocate dedicated experts to each modality (Infrared, Event, Depth, and RGB) to process their respective representations. The gating mechanism within the Mixture of Experts (MoE) then dynamically selects the optimal experts based on the input features, enabling adaptive and modality-specific fusion. For decoupled temporal propagation, we introduce two separate State Space Model (SSM) structures to independently store and update the hidden states h of the RGB and X-modal streams, effectively capturing their distinct temporal information. To ensure synergy between the two temporal representations, we incorporate a set of cross-attentions between the input features of the two SSMs, facilitating implicit information exchange. The resulting temporally enriched features are then integrated into the backbone via another set of cross-attention, enhancing MDTrack’s ability to leverage temporal information. Extensive experiments demonstrate the effectiveness of our proposed method. Both MDTrack-S (Modality-Specific Training) and MDTrack-U (Unified-Modality Training) achieve state-of-the-art performance across five multi-modal tracking benchmarks.
Cascade-based multi-scale architectures are currently the mainstream in Multi-view Stereo (MVS), achieving a balance between computational efficiency and reconstruction accuracy. However, existing cascade MVS methods suffer from significant limitations in cross-scale information utilization, where depth estimation processes operate independently across scales without fully exploiting the rich relevance between adjacent scales. To address this fundamental limitation, we propose an Enhanced Cascade Multi-View Stereo framework (EC-MVSNet), which introduces a novel cross-scale relevance integration strategy. Specifically, we introduce a Cross-Scale Feature-based Joint Construction (CFC) module to synergistically combine features from adjacent scales to build more reliable cost volumes. Additionally, a Cross-Scale Probability-guided Enhancement (CPE) module is proposed to propagate depth probability distributions across scales to guide cost volume enhancement. Furthermore, we propose a Monocular Feature-based Refinement (MFR) module to further enhance depth prediction accuracy by leveraging monocular priors. Extensive experiments demonstrate that EC-MVSNet achieves state-of-the-art performance on multiple benchmarks, validating the effectiveness of the cross-scale integration in improving MVS reconstruction quality.
Flow Matching (FM) is an efficient generative modeling framework, but aligning it with human preferences remains underexplored.~Although applying Direct Preference Optimization (DPO) to diffusion models has yielded improvements, directly extending DPO-like methods to FM poses three challenges: 1) Incompatibility with ODE-based models, 2) Heavy computational cost from full model fine-tuning, and 3) Reliance on reference model quality. To address these limitations, we propose Preference Classifier for Flow Matching (PC-Flow), a novel reference-free preference alignment framework. Specifically, we reinterpret FM’s deterministic ODE as an equivalent SDE to enable DPO-style learning. Then, we introduce a lightweight classifier to model relative preferences exclusively. This approach decouples alignment from the generative model, eliminating the need for costly fine-tuning or a reference model. Theoretically, PC-Flow guarantees consistent preference-guided distribution evolution, achieves a DPO-equivalent objective without a reference model, and progressively steers generation toward preferred outputs. Experiments show that PC-Flow achieves DPO-level alignment with significantly lower training costs.
Real-time, high-fidelity monocular depth estimation from remote sensing imagery is crucial for numerous applications, yet existing methods face a stark trade-off between accuracy and efficiency. Although using Vision Transformer (ViT) backbones for dense prediction is fast, they often exhibit poor perceptual quality. Conversely, diffusion models offer high fidelity but at a prohibitive computational cost. To overcome these limitations, we propose Depth Detail Diffusion for Remote Sensing Monocular Depth Estimation (D³-RSMDE), an efficient framework designed to achieve an optimal balance between speed and quality. Our framework first leverages a ViT-based module to rapidly generate a high-quality preliminary depth map construction, which serves as a structural prior, effectively replacing the time-consuming initial structure generation stage of diffusion models. Based on this prior, we propose a Progressive Linear Blending Refinement (PLBR) strategy, which uses a lightweight U-Net to refine the details in only a few iterations. The entire refinement step operates efficiently in a compact latent space supported by a Variational Autoencoder (VAE). Extensive experiments demonstrate that D³-RSMDE achieves a notable 11.85% reduction in the Learned Perceptual Image Patch Similarity (LPIPS) perceptual metric over leading models like Marigold, while also achieving over a 40× speedup in inference and maintaining VRAM usage comparable to lightweight ViT models.
Bokeh is used in photography to emphasize the selected subject by smoothly blurring the out-of-focus region with appealing highlights. While recent advances have achieved impressive results in rendering realistic blur, existing frameworks typically rely on disparity maps and bokeh-relevant inputs (e.g., focal distance and blur size), and face significant challenges in video bokeh rendering due to limited temporal consistency. In this paper, we propose BokehCrafter, the first video diffusion framework that generates temporally coherent and visually pleasing bokeh effects from all-in-focus video inputs under user-friendly input conditions. Specifically, we leverage a dual-stream attention mechanism, integrating a reference image branch and a rendering instruction branch. We propose a Bokeh Image Extraction (BIE) module and a CLIP-based text encoder to extract image and text features, respectively, whose outputs are fused via a Text-Image Fusion (TIF) module to enable fine-grained and controllable bokeh rendering. To support the novel capabilities of our model, we construct Video Bokeh Scenes (VBS), a large-scale dataset containing a wide variety of bokeh videos with corresponding rendering instructions, across various scenes and rendering settings. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art methods in both bokeh rendering quality and temporal consistency.
In recent years, lossy compression algorithms such as H.264/AVC, H.265/HEVC, and H.266/VVC have been proposed and widely applied in image and video encoding. However, these compression algorithms inevitably introduce various complex types of compression artifacts, which severely degrade image quality. Although existing methods have attempted to remove artifacts through filter design or probabilistic prior modeling, they are often effective only for specific types of artifacts, lacking generalization and adaptability. To address this, we propose a novel image compression artifacts removal model: ARMoE, which combines multiple frequency domain transformations with the Mixture of Experts (MoE). Considering the frequency distribution and energy distribution differences of images, we introduce various frequency domain transformations as expert branches and use the Sparse Activation Strategy to adaptively select the optimal frequency domain expert to suppress compression artifacts, achieving an efficient artifacts removal method. Furthermore, we reencode and decode multiple original uncompressed high-quality datasets, including DF2K and Kodak24, using the VTM-20.0 codec under the H.266/VVC standard, constructing a more challenging artifacts dataset. We conducted rigorous comparative experiments with current state-of-the-art image restoration methods and the results demonstrate that ARMoE exhibits outstanding image restoration capability.
Fair incomplete multi-view clustering (FIMVC) confronts a critical yet unresolved challenge, as existing methods often fail to address the intertwined issues of data missingness and algorithmic bias simultaneously. In this paper, we propose a novel FIMVC method named Adversarial Fair Incomplete Multi-View Clustering (AFIMVC). The core of AFIMVC is a new adaptive adversarial disentanglement mechanism. This mechanism trains the feature encoder to produce representations that are invariant to sensitive attributes by adversary learning, where the adversarial intensity is dynamically controlled by the model's real-time bias. Additionally, we develop a probabilistic cross-view contrastive learning strategy to achieve semantic consistency in latent space. To handle missing data, AFIMVC employs a context-aware fusion strategy that leverages cross-sample attention to robustly synthesize a unified representation from incomplete views. Extensive experiments demonstrate that AFIMVC achieves a state-of-the-art balance between clustering accuracy and fairness, significantly outperforming existing methods.
Purple flare, a diffuse chromatic aberration artifact commonly found around highlight areas, severely degrades the tone transition and color of the image. Existing traditional methods are based on hand-crafted features, which lack flexibility and rely entirely on fixed priors, while the scarcity of paired training data critically hampers deep learning. To address this issue, we propose a novel network built upon decoupled HSV Look-Up Tables (LUTs). The method aims to simplify color correction by adjusting the Hue (H), Saturation (S), and Value (V) components independently. This approach resolves the inherent color coupling problems in traditional methods. Our model adopts a two-stage architecture: First, a Chroma-Aware Spectral Tokenizer (CAST) converts the input image from RGB space to HSV space and independently encodes the Hue (H) and Value (V) channels into a set of semantic tokens describing the Purple flare status; second, the HSV-LUT module takes these tokens as input and dynamically generates independent correction curves (1D-LUTs) for the three channels H, S, and V. To effectively train and validate our model, we built the first large-scale purple flare dataset with diverse scenes. We also proposed new metrics and a loss function specifically designed for this task. Extensive experiments demonstrate that our model not only significantly outperforms existing methods in visual effects but also achieves state-of-the-art performance on all quantitative metrics.
Accurately simulating existing 3D objects and a wide variety of materials often demands expert knowledge and time-consuming physical parameter tuning to achieve the desired dynamic behavior. We introduce MotionPhysics, an end‑to‑end differentiable framework that infers plausible physical parameters from a user-proved natural language prompt for a chosen 3D scene of interest, removing the need for guidance from ground‑truth trajectories or annotated videos. Our approach first utilizes a multimodal large language model to estimate material parameter values, which are constrained to be within plausible ranges. We further propose a learnable motion distillation loss, which extracts robust motion priors from pretrained video diffusion models while minimizing appearance and geometry inductive biases to guide the simulation. We evaluate MotionPhysics across more than thirty scenarios, including real-world, human-designed, and AI-generated 3D objects, spanning a wide range of materials such as elastic solids, metals, foams, sand, and both Newtonian and non-Newtonian fluids. We demonstrate that it produces visually realistic dynamic simulations guided by natural language, surpassing the state of the art, with physically plausible parameters that are automatically determined.
Face super-resolution (FSR) aims to reconstruct high-resolution (HR) face images from low-resolution (LR) inputs. While recent methods have advanced this task through architectural innovations and generative modeling, but they often leads to semantically inconsistent structures and unrealistic textures, particularly under high magnification. To mitigate these limitations, we draw inspiration from the human artistic process of “structuring before detailing” and propose a progressive prior-guided restoration strategy. Specifically, we first introduce a Sketching Structure Prior (SSP) module that embeds global semantics and refines local geometry through implicit parsing guidance and explicit spatial modulation. Then, an Associative Texture Prior (ATP) module leverages a High-Quality Dictionary (HD) learned from high-quality reconstruction to guide fine-grained detail recovery. Finally, to unify structure and detail features, we design a Holistic Prior Fusion (HPF) module that adaptively integrates them within semantically consistent facial regions. Our method surpasses state-of-the-art on CelebA and Helen in both structural fidelity and texture realism.
Modality-Aware Bias Mitigation and Invariance Learning for Unsupervised Visible-Infrared Person Re-Identification
PDF ↗Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match individuals across visible and infrared cameras without relying on any annotation. Given the significant gap across visible and infrared modality, estimating reliable cross-modality association becomes a major challenge in USVI-ReID. Existing methods usually adopt optimal transport to associate the intra-modality clusters, which is prone to propagating the local cluster errors, and also overlooks global instance-level relations. By mining and attending to the visible-infrared modality bias, this paper focuses on addressing cross-modality learning from two aspects: bias-mitigated global association and modality-invariant representation learning. Motivated by the camera-aware distance rectification in single-modality re-ID, we propose modality-aware Jaccard distance to mitigate the distance bias caused by modality discrepancy, so that more reliable cross-modality associations can be estimated through global clustering. To further improve cross-modality representation learning, a `split-and-contrast' strategy is designed to obtain modality-specific global prototypes. By explicitly aligning these prototypes under global association guidance, modality-invariant yet ID-discriminative representation learning can be achieved. While conceptually simple, our method obtains state-of-the-art performance on benchmark VI-ReID datasets and outperforms existing methods by a significant margin, validating its effectiveness.