ICCV 2025 论文列表

Recognizing objects in images is a fundamental problem in computer vision. Although detecting objects in 2D images is common, many applications require determining their pose in 3D space. Traditional category-level methods rely on RGB-D inputs, which may not always be available, or employ two-stage approaches that use separate models and representations for detection and pose estimation. For the first time, we introduce a unified model that integrates detection and pose estimation into a single framework for RGB images by leveraging neural mesh models with learned features and multi-model RANSAC. Our approach achieves state-of-the-art results for RGB category-level pose estimation on REAL275, improving on the current state-of-the-art by 22.9% averaged across all scale-agnostic metrics. Finally, we demonstrate that our unified method exhibits greater robustness compared to single-stage baselines.

Current palmprint recognition models achieve strong performance on constrained datasets, yet exhibit significant limitations in handling challenging palmprint samples with geometric distortions and textural degradations. Data augmentation is widely adopted to improve model generalization. However, existing augmentation methods struggle to generate palmprint-specific variations while preserving identity consistency, leading to suboptimal performance. To address these problems, we propose a unified adversarial augmentation framework. It first utilizes an adversarial training paradigm for palmprint recognition, optimizing for challenging augmented samples by incorporating the feedback from the recognition network. We enhance palmprint images with both geometric and textual variations. Specifically, it adopts a spatial transformation module and a new identity-preserving module, which synthesizes palmprints with diverse textural variations while maintaining consistent identity. For more effective adversarial augmentation, a dynamic sampling strategy is proposed. Extensive experiments demonstrate the superior performance of our method on both challenging and constrained palmprint datasets.

This paper tackles the challenge of robust reconstruction, i.e., the task of reconstructing a 3D scene from a set of inconsistent multi-view images. Some recent works have attempted to simultaneously remove image inconsistencies and perform reconstruction by integrating image degradation modeling into neural 3D scene representations. However, these methods rely heavily on dense observations for robustly optimizing model parameters. To address this issue, we propose to decouple robust reconstruction into two subtasks: restoration and reconstruction, which naturally simplifies the optimization process. To this end, we introduce UniVerse, a unified framework for robust reconstruction based on a video diffusion model. Specifically, UniVerse first converts inconsistent images into initial videos, then uses a specially designed video diffusion model to restore them into consistent images, and finally reconstructs the 3D scenes from these restored images. Compared with case-by-case per-view degradation modeling, the diffusion model learns a general scene prior from large-scale data, making it applicable to diverse image inconsistencies. Extensive experiments on both synthetic and real-world datasets demonstrate the strong generalization capability and superior performance of our method in robust reconstruction. Moreover, UniVerse can control the style of the reconstructed 3D scene. The code will be released for the reproducibility.

Text-to-Image (T2I) diffusion models have shown impressive results in generating visually compelling images following user prompts. Building on this, various methods further fine-tune the pre-trained T2I model for specific tasks. However, this requires separate model architectures, training designs, and multiple parameter sets to handle different tasks. In this paper, we introduce UniVG, a generalist diffusion model capable of supporting a diverse range of image generation tasks with a single set of weights. UniVG treats multi-modal inputs as unified conditions to enable various downstream applications, ranging from T2I generation, inpainting, instruction-based editing, identity-preserving generation, and layout-guided generation, to depth estimation and referring segmentation. Through comprehensive empirical studies on data mixing and multi-task training, we provide detailed insights into the training processes and decisions that inform our final designs. For example, we show that T2I generation and other tasks, such as instruction-based editing, can coexist without performance trade-offs, while auxiliary tasks like depth estimation and referring segmentation enhance image editing. Notably, our model can even outperform some task-specific models on their respective benchmarks, marking a significant step towards a unified image generation model.

Real-world image restoration is hampered by diverse degradations stemming from varying capture conditions, capture devices and post-processing pipelines. Existing works make improvements through simulating those degradations and leveraging image generative priors, however generalization to in-the-wild data remains an unresolved problem. In this paper, we focus on complex degradations, i.e., arbitrary mixtures of multiple types of known degradations, which is frequently seen in the wild. A simple yet flexible diffusion-based framework, named UniRes, is proposed to address such degradations in an end-to-end manner. It combines several specialized models during the diffusion sampling steps, hence transferring the knowledge from several well-isolated restoration tasks to the restoration of complex in-the-wild degradations. This only requires well-isolated training data for several degradation types. The framework is flexible as extensions can be added through a unified formulation, and the fidelity-quality trade-off can be adjusted through a new paradigm. Our proposed method is evaluated on both complex-degradation and single-degradation image restoration datasets. Extensive qualitative and quantitative experimental results show consistent performance gain especially for images with complex degradations.

This paper presents UniPortrait, an innovative human image personalization framework that unifies single- and multi-ID customization with high face fidelity, extensive facial editability, free-form input description, and diverse layout generation. UniPortrait consists of only two plug-and-play modules: an ID embedding module and an ID routing module. The ID embedding module extracts versatile editable facial features with a decoupling strategy for each ID and embeds them into the context space of diffusion models. The ID routing module then combines and distributes these embeddings adaptively to their respective regions within the synthesized image, achieving the customization of single and multiple IDs. With a carefully designed two-stage training scheme, UniPortrait achieves superior performance in both single- and multi-ID customization. Quantitative and qualitative experiments demonstrate the advantages of our method over existing approaches as well as its good scalability, e.g., the universal compatibility with existing generative control tools.

Generating natural and physically plausible character motion remains challenging, particularly for long-horizon control with diverse guidance signals. While prior work combines high-level diffusion-based motion planners with low-level physics controllers, these systems suffer from domain gaps that degrade motion quality and require task-specific fine-tuning.To tackle this problem, we introduce UniPhys, a diffusion-based behavior cloning framework that unifies motion planning and control into a single model. UniPhys enables flexible, expressive character motion conditioned on multi-modal inputs such as text, trajectories, and goals. To address accumulated prediction errors over long sequences, UniPhys is trained with the Diffusion Forcing paradigm, learning to denoise noisy motion histories and handle discrepancies introduced by the physics simulator. This design allows UniPhys to robustly generate physically plausible, long-horizon motions. Through guided sampling, UniPhys generalizes to a wide range of control signals, including unseen ones, without requiring task-specific fine-tuning. Experiments show that UniPhys outperforms prior methods in motion naturalness, generalization, and robustness across diverse control tasks.

We introduce UniOcc, a comprehensive, unified benchmark and toolkit for occupancy forecasting (i.e., predicting future occupancies based on historical information) and occupancy prediction (i.e., predicting current-frame occupancy from camera images. UniOcc unifies the data from multiple real-world datasets (i.e., nuScenes, Waymo) and high-fidelity driving simulators (i.e., CARLA, OpenCOOD), providing 2D/3D occupancy labels and annotating innovative per-voxel flows. Unlike existing studies that rely on suboptimal pseudo labels for evaluation, UniOcc incorporates novel evaluation metrics that do not depend on ground-truth labels, enabling robust assessment on additional aspects of occupancy quality. Through extensive experiments on state-of-the-art models, we demonstrate that large-scale, diverse training data and explicit flow information significantly enhance occupancy prediction and forecasting performance. Our data and code are available at https://uniocc.github.io/.

The creation of diverse and realistic driving scenarios has become essential to enhance perception and planning capabilities of the autonomous driving system. However, generating long-duration, surround-view consistent driving videos remains a significant challenge. To address this, we present UniMLVG, a unified framework designed to generate extended street multi-perspective videos under precise control. By integrating single- and multi-view driving videos into the training data, our approach updates a DiT-based diffusion model equipped with cross-frame and cross-view modules across three stages with multi training objectives, substantially boosting the diversity and quality of generated visual content. Importantly, we propose an innovative explicit viewpoint modeling approach for multi-view video generation to effectively improve motion transition consistency. Capable of handling various input reference formats (e.g., text, images, or video), our UniMLVG generates high-quality multi-view videos according to the corresponding condition constraints such as 3D bounding boxes or frame-level text descriptions. Compared to the best models with similar capabilities, our framework achieves improvements of 48.2% in FID and 35.2% in FVD.

Text-to-image generation has transformed content creation, yet precise visual text rendering remains challenging for generative models due to blurred glyphs, semantic inconsistencies, and limited style controllability. Current methods typically employ pre-rendered glyph images as conditional inputs, but their inability to preserve original font styles and color information forces reliance on multi-branch architectures to compensate for missing details. This leads to increased model complexity, higher computational costs, and reduced reusability.To address these limitations, we propose a segmentation-guided framework that leverages pixel-level visual text segmentation masks--complete representations preserving glyph shapes, colors, and spatial details--as unified conditional inputs. Our approach integrates two key innovations: (1) a fine-tuned bilingual segmentation model for extracting precise text masks from source images, and (2) a streamlined diffusion model enhanced with adaptive glyph condition and glyph region loss to ensure semantic and stylistic fidelity. On the AnyText-benchmark, our method achieves a sentence accuracy (Sen.Acc) of 0.8267 and a Normalized Edit Distance (NED) of 0.8976 for Chinese text generation, while the English test set delivers even stronger performance with 0.9018 Sen.Acc and 0.9582 NED, surpassing prior methods by substantial margins. To address broader evaluation needs, we introduce two novel benchmarks: GlyphMM-benchmark (for holistic glyph consistency assessment) and MiniText-benchmark (targeting small-scale glyph fidelity analysis). Experimental results demonstrate our method's dominance across these new benchmarks: 16% Sen.Acc improvement on the Chinese subset of GlyphMM-benchmark and 50% gain on its English counterpart. Notably, our approach achieves over a 100% Sen.Acc boost on the challenging MiniText test set designed for localized text regions. This breakthrough validates our architecture's dual strengths: simplified deployment-ready design and superior generalization for cross-lingual text rendering tasks.

In this work, we introduce UniGS, a novel 3D Gaussian reconstruction and novel view synthesis model that predicts a high-fidelity representation of 3D Gaussians from arbitrary number of posed sparse-view images.Previous methods often regress 3D Gaussians locally on a per-pixel basis for each view and then transfer them to world space and merge them through point concatenation.In contrast, Our approach involves modeling unitary 3D Gaussians in world space and updating them layer by layer.To leverage information from multi-view inputs for updating the unitary 3D Gaussians, we develop a DETR (DEtection TRansformer)-like framework, which treats 3D Gaussians as queries and updates their parameters by performing multi-view cross-attention (MVDFA) across multiple input images, which are treated as keys and values.This approach effectively avoids 'ghosting' issue and allocates more 3D Gaussians to complex regions.Moreover, since the number of 3D Gaussians used as decoder queries is independent of the number of input views, our method allows arbitrary number of multi-view images as input without causing memory explosion or requiring retraining.Extensive experiments validate the advantages of our approach, showcasing superior performance over existing methods quantitatively (improving PSNR by 4.2 dB when trained on Objaverse and tested on the GSO benchmark) and qualitatively. Codes are at https://github.com/jwubz123/UNIG.

Current multimodal medical image fusion typically assumes that source images are of high quality and perfectly aligned at the pixel level. Its effectiveness heavily relies on these conditions and often deteriorates when handling misaligned or degraded medical images. To address this, we propose UniFuse, a general fusion framework. By embedding a degradation-aware prompt learning module, UniFuse seamlessly integrates multi-directional information from input images and correlates cross-modal alignment with restoration, enabling joint optimization of both tasks within a unified framework. Additionally, we design an Omni Unified Feature Representation scheme, which leverages Spatial Mamba to encode multi-directional features and mitigate modality differences in feature alignment. To enable simultaneous restoration and fusion within an All-in-One configuration, we propose a Universal Feature Restoration & Fusion module, incorporating the Adaptive LoRA Synergistic Network (ALSN) based on LoRA principles. By leveraging ALSN's adaptive feature representation along with degradation-type guidance, we enable joint restoration and fusion within a single-stage framework. Compared to staged approaches, UniFuse unifies alignment, restoration, and fusion within a single framework. Experimental results across multiple datasets demonstrate the method's effectiveness and significant advantages over existing approaches. code is available at https://github.com/slrl123/UniFuse.

Egocentric human motion generation and forecasting with scene-context is crucial for enhancing AR/VR experiences, improving human-robot interaction, advancing assistive technologies, and enabling adaptive healthcare solutions by accurately predicting and simulating movement from a first-person perspective. However, existing methods primarily focus on third-person motion synthesis with structured 3D scene contexts, limiting their effectiveness in real-world egocentric settings where limited field of view, frequent occlusions, and dynamic cameras hinder scene perception. To bridge this gap, we introduce Egocentric Motion Generation and Egocentric Motion Forecasting, two novel tasks that utilize first-person images for scene-aware motion synthesis without relying on explicit 3D scene. We propose UniEgoMotion, a unified conditional motion diffusion model with a novel head-centric motion representation tailored for egocentric devices. UniEgoMotion's simple yet effective design supports egocentric motion reconstruction, forecasting, and generation from first-person visual inputs in a unified framework. Unlike previous works that overlook scene semantics, our model effectively extracts image-based scene context to infer plausible 3D motion. To facilitate training, we introduce EE4D-Motion, a large-scale dataset derived from EgoExo4D, augmented with pseudo-ground-truth 3D motion annotations. UniEgoMotion achieves state-of-the-art performance in egocentric motion reconstruction and is the first to generate motion from a single egocentric image. Extensive evaluations demonstrate the effectiveness of our unified framework, setting a new benchmark for egocentric motion modeling and unlocking new possibilities for egocentric applications.

Modality or domain distribution shifts pose formidable challenges in 3D semantic segmentation. Existing methods predominantly address either cross-modal or cross-domain adaptation in isolation, leading to insufficient exploration of semantic associations and complementary features in heterogeneous data. To bridge this gap, we present UniDxMD, a unified representation method for cross-modal unsupervised domain adaptation (UDA) in 3D semantic segmentation that simultaneously tackles both cross-modal and cross-domain adaptation objectives. Our core insight is deriving a unified discrete representation from heterogeneous data to mitigate distribution shifts, inspired by vector quantization. Specifically, we propose a differentiable, cluster-based soft quantization mechanism (CSQM) that maps heterogeneous data (spanning modalities and domains) into a shared discrete latent space. Then, we introduce latent space regularization (LSR), leveraging joint prototypes that satisfy semantic relation consistency as learnable anchors to enhance the compactness and semantic discriminability of the discrete latent space. Our method paves the way for advancing cross-modal UDA in 3D semantic segmentation towards the unified representation. Extensive results across four challenging cross-modal UDA scenarios demonstrate the superiority of our method.

Convolutional neural networks (ConvNets) with large effective receptive field (ERF), still in their early stages, have demonstrated promising effectiveness while constrained by high parameters and FLOPs costs and disrupted asymptotically Gaussian distribution (AGD) of ERF. This paper proposes an alternative paradigm: rather than merely employing extremely large ERF, it is more effective and effcient to expand the ERF while maintaining AGD of ERF by proper combination of smaller kernels, such as 7x 7 , 9x 9 , 11x 11 . This paper introduces a Three-layer Receptive Field Aggregator and designs a Layer Operator as the fundamental operator from the perspective of receptive field. The ERF can be expanded to the level of existing large-kernel ConvNets through the stack of proposed modules while maintaining AGD of ERF. Using these designs, we propose a universal ConvNet, termed UniConvNet. Extensive experiments on ImageNet-1K, COCO2017, and ADE20K demonstrate that UniConvNet outperforms state-of-the-art CNNs and ViTs across various vision recognition tasks for both lightweight and large-scale models with comparable throughput. Surprisingly, UniConvNet-T achieves 84.2% ImageNet top-1 accuracy with 30M parameters and 5.1G FLOPs. UniConvNet-XL also shows competitive scalability to big data and large models, acquiring 88.4% top-1 accuracy on ImageNet.

With the rapid development of diffusion models in image generation, the demand for more powerful and flexible controllable frameworks is increasing. Although existing methods can guide generation beyond text prompts, the challenge of effectively combining multiple conditional inputs while maintaining consistency with all of them remains unsolved. To address this, we introduce UniCombine, a DiT-based multi-conditional controllable generative framework capable of handling any combination of conditions, including but not limited to text prompts, spatial maps, and subject images. Specifically, we introduce a novel Conditional MMDiT Attention mechanism and incorporate a trainable LoRA module to build both the training-free and training-based versions. Additionally, we propose a new pipeline to construct SubjectSpatial200K, the first dataset designed for multi-conditional generative tasks covering both the subject-driven and spatially-aligned conditions. Extensive experimental results on multi-conditional generation demonstrate the outstanding universality and powerful capability of our approach with state-of-the-art performance.

Deep Unfolding Networks (DUNs) have emerged as a powerful framework for pansharpening due to their interpretable fusion strategies. However, existing DUNs are limited by their serial iterative architectures, which hinder cross-stage and cross-modal feature interactions at different abstraction levels. This limitation results in insufficient integration of multi-level multimodal features and compromised reconstruction accuracy. To address these challenges, we propose the Unfolding-Associative Encoder-Decoder Network (UED-Net), an innovative framework that iteratively extracts multi-level cross-modal degradation encodings and recursively refines features for cross-stage adaptive aggregation decoding through lightweight processes. Specifically, we first introduce the spatial-spectral encoding module, which progressively and interpretably perceives the hierarchical degradation encoding features of both space and spectrum. Moreover, we develop the unfolding-associative attention module to capture pixel-level attention across stages, thereby leveraging the causal relationships of multi-level features for aggregation during decoding. Meanwhile, we implement a progressive alignment mechanism, which coordinates both feature distribution and alignment of spatial and spectral modalities between iterative stages to facilitate adaptive fusion. These modules enable UED-Net to achieve efficient pansharpening by aggregating multi-level features. Extensive qualitative and quantitative experiments confirm the superiority of UED-Net.

Underwater visual simultaneous localization and mapping (SLAM) faces critical challenges in light attenuation and degraded geometric consistency. Despite recent advances of visual SLAM in indoor and urban scenes, these approaches typically assume a clear medium and neglect medium-light interactions, leading to performance degradation in underwater environments. To overcome these limitations, we propose DUV-SLAM, a dense underwater visual SLAM framework that integrates uncertainty-aware geometry estimation with physics-inspired neural scattering modeling. Our method introduces two core innovations: i) depth uncertainty quantification derived from differentiable bundle adjustment, which propagates geometric confidence to guide mapping optimization; and ii) a neural-Gaussian hybrid representation that combines adaptive 3D Gaussians for underwater reconstruction with a neural field capturing wavelength-dependent medium properties, optimized using a combination of photometric, geometric, and distribution losses. Experiments on synthetic and real-world datasets demonstrate that DUV-SLAM achieves high-quality monocular reconstruction while maintaining real-time efficiency and robust tracking accuracy.

While open-vocabulary semantic segmentation (OVSS) can segment an image into semantic regions based on arbitrarily given text descriptions even for classes unseen during training, it fails to understand personal texts (e.g. 'my mug cup') for segmenting regions of specific interest to users. This paper addresses challenges like recognizing 'my mug cup' among 'multiple mug cups'. To overcome this challenge, we introduce a novel task termed personalized open-vocabulary semantic segmentation and propose a text prompt tuning-based plug-in method designed to recognize personal visual concepts using a few pairs of images and masks, while maintaining the performance of the original OVSS. Based on the observation that reducing false predictions is essential when applying text prompt tuning to this task, our proposed method employs 'negative mask proposal' that captures visual concepts other than the personalized concept. We further improve the performance by enriching the representation of text prompts by injecting visual embeddings of the personal concept into them. This approach enhances personalized OVSS without compromising the original OVSS performance. We demonstrate the superiority of our method on our newly established benchmarks for this task, including FSS^per, CUB^per, and ADE^per.