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Yongchan Chun, Chanhee Park, Jeongho Yoon, Jaehyung Seo, Heuiseok Lim

Pretrained models have become standard in both vision and language, yet they typically do not provide reliable measures of confidence. Existing uncertainty estimation methods--such as deep ensembles and MC dropout--are often too computationally expensive to deploy in practice. Evidential Deep Learning (EDL) offers a more efficient alternative, but it requires models to be trained to output evidential quantities from the start, which is rarely true for pretrained networks. To enable EDL-style uncertainty estimation in pretrained models, we propose the Evidential Transformation Network (ETN), a lightweight post-hoc module that converts a pretrained predictor into an evidential model. ETN operates in logit space: it learns a sample-dependent affine transformation of the logits and interprets the transformed outputs as parameters of a Dirichlet distribution for uncertainty estimation. We evaluate ETN on image classification and large language model question-answering benchmarks, under both in-distribution and out-of-distribution settings. ETN consistently improves uncertainty estimation over post-hoc baselines, while preserving accuracy and adding only minimal computational overhead.

Boyuan Cheng, Yingjie Xi, Rui He, Jinhe Na, Ying Cao, Pengjie Wang, Jian J. Zhang, Xiaosong Yang

To tell a story effectively, a 3D animation often necessitates carefully planned behaviors of both characters and the camera in the 3D scene, where the camera placement and movement determine how the characters are displayed on screen. Thus, creating storytelling animations can be challenging. While significant progress has been made in the fields of character motion synthesis and virtual cinematography, previous methods focus on either character motion generation or camera motion generation, falling short of handling the two tasks simultaneously. In this paper, we propose a novel diffusion-based generative model to jointly synthesize character and camera motions in 3D space for creating storytelling 3D animations. Our model treats individual characters and the camera in a 3D scene as independent, equally important entities, and explicitly models pairwise interactions among them in the generation process. By being trained on a mixture dataset of real and synthetic character-camera motion data, our model is capable of generating high-quality multi-character motions coupled with compelling camera motions. We show that our model outperforms existing specialized approaches on the human motion generation and camera motion tasks.

Shihua Zhang, Tianhao Xu, Zizhuo Li, Qing Ma, Jiayi Ma

Image-to-point cloud matching (2D-3D matching) establishes accurate correspondences between image keypoints and 3D points for 6-DoF camera pose estimation. Existing methods either suffer from poor generalization due to scene-specific coordinate regression requiring per-scene retraining, or incur high storage and maintenance costs from descriptor-based matching that relies on large descriptor sets. Consequently, descriptor-free approaches have gained attention by avoiding heavy storage while improving generalizability; however, most rely only on low-level geometric cues, which limits performance. Leveraging the benefits of semantics in providing context, resolving ambiguities, and enhancing robustness in challenging scenes, we propose the Semantic-Aware Guided Graph Neural Network (SAG-GNN), integrating high-level semantics into descriptor-free 2D-3D matching. Specifically, we design a compact semantic extraction scheme encoding each 3D point as a low-dimensional semantic probability distribution, offering effective guidance with minimal storage. A bidirectionally-aligned fusion block merges geometric features with semantic context for more unified and consistent representations. Additionally, semantic priors guide the 2D-3D information exchange within the interaction framework from a high-level semantic perspective. Extensive indoor and outdoor experiments validate that SAG-GNN achieves state-of-the-art results in descriptor-free 2D-3D matching and visual localization, with low storage and strong generalization. Code is available at https://github.com/tinxu0203/SAG-GNN.

David Novikov, Eilon Vaknin, Narek Tumanyan, Mark Sheinin

The task of capturing and rendering 3D dynamic scenes from 2D images has become increasingly popular in recent years. However, most conventional cameras are bandwidth-limited to 30-60 FPS, restricting these methods to static or slowly evolving scenes. While overcoming bandwidth limitations is difficult in general scenes, recent years have seen a flurry of computational imaging methods that yield high-speed videos using conventional cameras for specific scenarios (e.g., motion capture and particle image velocimetry). However, most of these methods require modifications to camera optics or the addition of mechanically moving components, limiting them to a single-view high-speed capture. Consequently, these cannot be readily used to capture a 3D representation of rapid scene motion. In this paper, we propose a novel method to capture and reconstruct a volumetric representation of a high-speed scene using only unaugmented low-speed cameras. Instead of modifying the hardware or optics of each individual camera, we encode high-speed scene dynamics by illuminating the scene with a rapid, sequential color sequence. This results in simultaneous multi-view capture of the scene, in which high-speed temporal information is encoded in the images' color. To construct a high-speed volumetric representation of the dynamic scene, we develop a novel dynamic Gaussian Splatting-based approach that decodes the temporal information from the images. We evaluate our approach on simulated scenes and real-world experiments using a multi-camera imaging setup, showing first-of-a-kind high-speed volumetric scene reconstructions.

Sohyun Lee, Yeho Gwon, Lukas Hoyer, Konrad Schindler, Christos Sakaridis, Suha Kwak

The performance of promptable video object segmentation (PVOS) models substantially degrades under input corruptions, which prevents PVOS deployment in safety-critical domains. This paper offers the first comprehensive study on robust PVOS (RobustPVOS). We first construct a new, comprehensive benchmark with two real-world evaluation datasets of 351 video clips and more than 2,500 object masks under real-world adverse conditions. At the same time, we generate synthetic training data by applying diverse and temporally varying corruptions to existing VOS datasets. Moreover, we present a new RobustPVOS method, dubbed Memory-object-conditioned Gated-rank Adaptation (MoGA). The key to successfully performing RobustPVOS is two-fold: effectively handling object-specific degradation and ensuring temporal consistency in predictions. MoGA leverages object-specific representations maintained in memory across frames to condition the robustification process, which allows the model to handle each tracked object differently in a temporally consistent way. Extensive experiments on our benchmark validate MoGA's efficacy, showing consistent and significant improvements across diverse corruption types on both synthetic and real-world datasets, establishing a strong baseline for future RobustPVOS research. Our benchmark is publicly available at https://sohyun-l.github.io/RobustPVOS_project_page/.

Bedrettin Cetinkaya, Sinan Kalkan, Emre Akbas

Generating crisp, i.e., one-pixel-wide, edge maps remains one of the fundamental challenges in edge detection, affecting both traditional and learning-based methods. To obtain crisp edges, most existing approaches rely on two hand-crafted post-processing algorithms, Non-Maximum Suppression (NMS) and skeleton-based thinning, which are non-differentiable and hinder end-to-end optimization. Moreover, all existing crisp edge detection methods still depend on such post-processing to achieve satisfactory results. To address this limitation, we propose MatchED, a lightweight, only 21K additional parameters, and plug-and-play matching-based supervision module that can be appended to any edge detection model for joint end-to-end learning of crisp edges. At each training iteration, MatchED performs one-to-one matching between predicted and ground-truth edges based on spatial distance and confidence, ensuring consistency between training and testing protocols. Extensive experiments on four popular datasets demonstrate that integrating MatchED substantially improves the performance of existing edge detection models. In particular, MatchED increases the Average Crispness (AC) metric by up to 2--4x compared to baseline models. Under the crispness-emphasized evaluation (CEval), MatchED further boosts baseline performance by up to 20--35% in ODS and achieves similar gains in OIS and AP, achieving SOTA performance that matches or surpasses standard post-processing for the first time. Code is available at https://cvpr26-matched.github.io.

Xiaolong Qian, Qi Jiang, Yao Gao, Lei Sun, Zhonghua Yi, Kailun Yang, Luc Van Gool, Kaiwei Wang

Prevalent Computational Aberration Correction (CAC) methods are typically tailored to specific optical systems, leading to poor generalization and labor-intensive re-training for new lenses.Developing CAC paradigms capable of generalizing across diverse photographic lenses offers a promising solution to these challenges.However, efforts to achieve such cross-lens universality within consumer photography are still in their early stages due to the lack of a comprehensive benchmark that encompasses a sufficiently wide range of optical aberrations.Furthermore, it remains unclear which specific factors influence existing CAC methods and how these factors affect their performance.In this paper, we present comprehensive experiments and evaluations involving 24 image restoration and CAC algorithms, utilizing our newly proposed UNICAC, a large-scale benchmark for photographic cameras constructed via automatic optical design.The Optical Degradation Evaluator (ODE) is introduced as a novel framework to objectively assess the difficulty of CAC tasks, offering credible quantification of optical aberrations and enabling reliable evaluation.Drawing on our comparative analysis, we identify three key factors -- prior utilization, network architecture, and training strategy -- that most significantly influence CAC performance, and further investigate their respective effects.We believe that our benchmark, dataset, and observations contribute foundational insights to related areas and lay the groundwork for future investigations.Benchmarks, codes, and Zemax files will be available.

Kai Kohyama, Yoshimitsu Aoki, Guillermo Gallego, Shintaro Shiba

Event cameras offer a high temporal resolution over traditional frame-based cameras, which makes them suitable for motion and structure estimation. However, it has been unclear how event-based 3D Gaussian Splatting (3DGS) approaches could leverage fine-grained temporal information of sparse events. This work proposes GPERT, a framework to address the trade-off between accuracy and temporal resolution in event-based 3DGS. Our key idea is to decouple the rendering into two branches: event-by-event geometry (depth) rendering and snapshot-based radiance (intensity) rendering, by using ray-tracing and the image of warped events. The extensive evaluation shows that our method achieves state-of-the-art performance on the real world datasets and competitive performance on the synthetic dataset. Also, the proposed method works with out prior information (e.g., pretrained image reconstruction models) or COLMAP-based initialization, is more flexible in the event selection number, and achieves sharp reconstruction on scene edges with fast training time. We hope that this work deepens our understanding of the sparse nature of events for 3D reconstruction. https://github.com/e3ai/gpert

Amélie Royer, Moritz Böhle, Laurent Mazaré, Neil Zeghidour, Alexandre Défossez, Patrick Pérez

The recent successes of Vision-Language models raise the question of how to equivalently imbue a pretrained speech model with vision understanding, an important milestone towards building a multimodal speech model able to freely converse about images. Building such a conversational Vision-Speech model brings its unique challenges: (i) paired image-speech datasets are much scarcer than their image-text counterparts, (ii) ensuring real-time latency at inference is crucial thus bringing compute and memory constraints, and (iii) the model should preserve prosodic features (e.g., speaker tone) which cannot be inferred from text alone. In this work, we introduce MoshiVis, augmenting a recent dialogue speech LLM, Moshi, with visual inputs through lightweight adaptation modules. An additional dynamic gating mechanism enables the model to more easily switch between the visual inputs and unrelated conversation topics. To reduce training costs, we design a simple one-stage, parameter-efficient fine-tuning pipeline in which we leverage a mixture of image-text (i.e., "speechless") and image-speech samples. We evaluate the model on downstream visual understanding tasks with both audio and text prompts, and report qualitative samples of interactions with MoshiVis.

Yihao Zhen, Mingyue Xu, Qiang Wang, Baojie Fan, Jiahua Dong, Tinghui Zhao, Huijie Fan

Existing Multi-Camera Multi-Target (MCMT) tracking models typically adopt a two-stage framework, involving single-camera tracking followed by inter-camera tracking. However, in this paradigm, the use of multiple views is confined to recovering missed matches in the first stage, providing a limited contribution to overall tracking. To address this issue, we propose a novel global MCMT tracking framework termed GMT, which effectively leverages the advantage of multi-view by performing global-level trajectory-target matching. Specifically, instead of assigning trajectories independently for each view, we propose a Cross-View Feature Consistency Enhancement(CFCE) module to reduce the feature discrepancies across different views, and encode the same historical targets across different views as global trajectories. The Global Trajectory Associate (GTA) module is then introduced to associate new targets to global trajectories, allowing the model to jointly exploit both intra-view and inter-view cues during tracking. Compared with the two-stage framework, the GMT achieves significant improvements on existing datasets, with gains of up to 13.1% in CVMA in and 19.2% in CVIDF1. Moreover, we present VisionTrack, a high-quality, large-scale MCMT dataset encompassing diverse scenes with varying illumination and target distributions, providing significantly greater diversity than existing datasets. Our code and dataset will be released at https://github.com/FoxCanned/GMT.

Weiran Wang, Jialing Wu, Yaqi Chang, Gang He, Li Xu, Chang Wu, Yunsong Li

Multi-taskdensepredictionaims tosimultaneously addressmultiplepixel-level tasksthroughaunifiednetworkforvisualsceneunderstanding.However,adverse environmental conditions limit thegeneralizationand practicality of such tasks. Toaddress this, we proposeALLNet, anovel framework that effectively explores degradationpatterns and integratesmulti-task collaborative information. Specifically, we designa MoE-basedMixtureofAdaptiveExperts(MaE)restorationcomponentnetworkthatenhancesdegradationfeaturesthroughdynamicroutingandguidestask-specific featureextraction. Furthermore,weformulateaTaskawareCollaborativeRefinement (TCR)moduletocaptureglobalsemanticcorrelationsandcross-taskdependencies,facilitatingbidirectionalcollaborationbetween restorationandtask-specific featuresondegradedimages. Tothebestofourknowledge, thisrepresentsthe firstattemptatmulti-taskdensepredictionunderimage degradation.ExperimentalresultsondegradedNYUDv2andPASCAL-Contextbenchmarksdemonstratethat ourarchitecturesignificantlyoutperformsexistingmethodsindegradedscenarios.

Jincen Jiang, Qianyu Zhou, Yuhang Li, Kui Su, Meili Wang, Jian Chang, Jian Jun Zhang, Xuequan Lu

While recent Transformer and Mamba architectures have advanced point cloud representation learning, they are typically developed for single-task or single-domain settings. Directly applying them to multi-task domain generalization (DG) leads to degraded performance. Transformers effectively model global dependencies but suffer from quadratic attention cost and lack explicit structural ordering, whereas Mamba offers linear-time recurrence yet often depends on coordinate-driven serialization, which is sensitive to viewpoint changes and missing regions, causing structural drift and unstable sequential modeling. In this paper, we propose Structure-Aware Domain Generalization (SADG), a Mamba-based In-Context Learning framework that preserves structural hierarchy across domains and tasks. We design structure-aware serialization (SAS) that generates transformation-invariant sequences using centroid-based topology and geodesic curvature continuity. We further devise hierarchical domain-aware modeling (HDM) that stabilizes cross-domain reasoning by consolidating intra-domain structure and fusing inter-domain relations. At test time, we introduce a lightweight spectral graph alignment (SGA) that shifts target features toward source prototypes in the spectral domain without updating model parameters, ensuring structure-preserving test-time feature shifting. In addition, we introduce MP3DObject, a real-scan object dataset for multi-task DG evaluation. Comprehensive experiments demonstrate that the proposed approach improves structural fidelity and consistently outperforms state-of-the-art methods across multiple tasks including reconstruction, denoising, and registration.

Dongwon Kim, Gawon Seo, Jinsung Lee, Minsu Cho, Suha Kwak

World models provide a powerful framework for simulating environment dynamics conditioned on actions or instructions, enabling downstream tasks such as action planning or policy learning.Recent approaches leverage world models as learned simulators, but its application to decision-time planning remains computationally prohibitive for real-time control.A key bottleneck lies in latent representations: conventional tokenizers encode each observation into hundreds of tokens, making planning both slow and resource-intensive.To address this, we propose CompACT, a discrete tokenizer that compresses each observation into as few as 8 tokens, drastically reducing computational cost while preserving essential information for planning.An action-conditioned world model that occupies CompACT tokenizer achieves competitive planning performance with orders-of-magnitude faster planning, offering a practical step toward real-world deployment of world models.

Chao Wang, Chengan Che, Xinyue Chen, Sophia Tsoka, Luis C. Garcia-Peraza-Herrera

Counterfactual explanations (CFEs) are minimal and semantically meaningful modifications of the input of a model that alter the model predictions. They highlight the decisive features the model relies on, providing contrastive interpretations for classifiers. State-of-the-art visual counterfactual explanation methods have primarily focused on interpreting image classifiers, leaving the domain of video models relatively underexplored. For the video CFEs to be useful, they have to be physically plausible, temporally coherent, and exhibit smooth motion trajectories. Existing CFE image-based methods, designed to explain image classifiers, lack the capacity to generate temporally coherent, smooth and physically plausible video CFEs. To address this, we propose Back To The Feature (BTTF), an optimization framework that generates video CFEs. Our method introduces two novel features, 1) an optimization scheme to retrieve the initial latent noise conditioned by the first frame of the input video, 2) a two-stage optimization strategy to enable the search for counterfactual videos in the vicinity of the input video. Both optimization processes are guided solely by the target classifier, ensuring the explanation is faithful. To accelerate convergence, we also introduce a progressive optimization strategy that incrementally increases the number of denoising steps. Extensive experiments on video datasets such as Shape-Moving (motion classification), MEAD (emotion classification), and NTU RGB+D (action classification) show that our BTTF effectively generates valid, visually similar and realistic counterfactual videos that provide concrete insights into the classifier's decision-making mechanism.

Dekel Galor, Adam Pikielny, Zhoutong Zhang, Ke Wang, Laura Waller, Jiawen Chen, Ilya Chugunov

Photorealistic style transfer aims to match the color and tone of an input image to that of a style target while preserving the content and details of the original scene. Although existing large image models can facilitate these kinds of appearance edits, their high computational demands, potential for hallucinations, and limited user control make them unsuitable for high-resolution, real-time workflows. We introduce Hist2Style, a bilateral-grid formulation for fast, edge-aware stylization that preserves visual fidelity by constraining operations to locally affine transforms in bilateral space. Our model distills a large image editing model into a lightweight network by training on a large supervised corpus generated with language and vision-language models, targeting spatially varying color edits. The network conditions on a histogram-based embedding of the style target to provide an interpretable interface for adjusting the output style by modifying the target color distribution. Overall, Hist2Style maintains content structure by construction, avoids hallucinations, and supports real-time, high-resolution photorealistic stylization with interactive user-controllable color and tone adjustments. Our project page is available at \href https://dgalor.github.io/hist2style/ dgalor.github.io/hist2style/ .

Enzhuo Zhang, Sijie Zhao, Dilxat Muhtar, Zhenshi Li, Xueliang Zhang, Pengfeng Xiao

Generative diffusion priors have recently achieved state-of-the-art performance in natural image super-resolution, demonstrating a powerful capability to synthesize photorealistic details. However, their direct application to remote sensing image super-resolution (RSISR) reveals significant shortcomings. Unlike natural images, remote sensing images exhibit a unique texture distribution where ground objects are globally stochastic yet locally clustered, leading to highly imbalanced textures. This imbalance severely hinders the model's spatial perception. To address this, we propose TexADiff, a novel framework that begins by estimating a Relative Texture Density Map (RTDM) to represent the texture distribution. TexADiff then leverages this RTDM in three synergistic ways: as an explicit spatial conditioning to guide the diffusion process, as a loss modulation term to prioritize texture-rich regions, and as a dynamic adapter for the sampling schedule. These modifications are designed to endow the model with explicit texture-aware capabilities. Experiments demonstrate that TexADiff achieves superior or competitive quantitative metrics. Furthermore, qualitative results show that our model generates faithful high-frequency details while effectively suppressing texture hallucinations. This improved reconstruction quality also results in significant gains in downstream task performance. The source code of our method can be found at https://github.com/ZezFuture/TexAdiff.

Merve Gulle, Junno Yun, Yasar Utku Alcalar, Mehmet Akcakaya

Diffusion models have found extensive use in solving inverse problems, by sampling from an approximate posterior distribution of data given the measurements. Recently, consistency models (CMs) have been proposed to directly predict the final output from any point on the diffusion ODE trajectory, enabling high-quality sampling in just a few neural function evaluations (NFEs). CMs have also been utilized for inverse problems, but existing CM-based solvers either require additional task-specific training or utilize data fidelity operations with slow convergence, limiting their applicability to large-scale problems and making them difficult to extend to nonlinear settings. In this work, we reinterpret CMs as proximal operators of a prior, enabling their integration into plug-and-play (PnP) frameworks. Specifically, we propose PnP-CM, an ADMM-based PnP solver that provides a unified framework for solving a wide range of inverse problems, and incorporates noise perturbations and momentum-based updates to improve performance in the low-NFE regime. We evaluate our approach on a diverse set of linear and nonlinear inverse problems. We also train and apply CMs to MRI data for the first time. Our results show that PnP-CM achieves high-quality reconstructions in as few as 4 NFEs, and produces meaningful results in 2 steps, highlighting its effectiveness in real-world inverse problems while outperforming existing CM-based approaches.

Yicheng Feng, Wanpeng Zhang, Ye Wang, Hao Luo, Haoqi Yuan, Sipeng Zheng, Zongqing Lu

Vision-Language-Action (VLA) models provide a promising paradigm for robot learning by integrating visual perception with language-guided policy learning. However, most existing approaches rely on 2D visual inputs to perform actions in 3D physical environments, creating a significant gap between perception and action grounding. To bridge this gap, we propose a Spatial-Aware VLA Pretraining paradigm that enables models to acquire 3D spatial understanding before robot policy learning. Starting from pretrained vision-language models, we leverage large-scale human demonstration videos to extract 3D visual and 3D action annotations, forming a new source of supervision that aligns 2D visual observations with 3D spatial reasoning. We instantiate this paradigm with VIPA-VLA, a dual-encoder architecture that incorporates a 3D visual encoder to augment semantic visual representations with 3D-aware features, and aligns the two through visual-physical alignment pretraining. When adapted to downstream robot tasks, VIPA-VLA achieves significantly improved grounding between 2D vision and 3D action, resulting in more robust and generalizable robotic policies.

Berthy T. Feng, Andrew A. Chael, David Bromley, Aviad Levis, William T. Freeman, Katherine L. Bouman

With the success of static black-hole imaging, the next frontier is the dynamic and 3D imaging of black holes. Recovering the dynamic 3D gas near a black hole would reveal previously-unseen parts of the universe and inform new physics models. However, only sparse radio measurements from a single viewpoint are possible, making the dynamic 3D reconstruction problem significantly ill-posed. Previously, BH-NeRF addressed the ill-posed problem by assuming Keplerian dynamics of the gas, but this assumption breaks down near the black hole, where the strong gravitational pull of the black hole and increased electromagnetic activity complicate fluid dynamics. To overcome the restrictive assumptions of BH-NeRF, we propose PI-DEF, a physics-informed approach that uses differentiable neural rendering to fit a 4D (time + 3D) emissivity field given EHT measurements. Our approach jointly reconstructs the 3D velocity field with the 4D emissivity field and enforces the velocity as a soft constraint on the dynamics of the emissivity. In experiments on simulated data, we find significantly improved reconstruction accuracy over both BH-NeRF and a physics-agnostic approach. We demonstrate how our method may be used to estimate other physics parameters of the black hole, such as its spin.

Jinjia Peng, Jican Tan, Jiazuo Yu, Zeze Tao, Huibing Wang

Lifelong Person Re-Identification aims to adapt to new domains while preserving old knowledge. Existing methods, whether distillation-based or rehearsal-based, attempt to consolidate diverse knowledge within a fixed model architecture. However, the limited adaptability of such architectures often leads to catastrophic forgetting by overwriting previously acquired knowledge. To overcome this limitation, we propose Versatile Incremental Adaptation (VIA), a novel dynamical expansion framework for LReID, which unleashes restricted knowledge during continuous learning by large pre-trained models. Specifically, Unseen-domain person Adapter (UnA) is embedded in VIA, which employs incremental modular learning to capture the domain's specific knowledge, thereby reducing cross-domain interference and releasing task-specific capacity that is previously limited by static parameter sharing. Meanwhile, considering the substantial amount of sharing knowledge across domains in LReID, we design the Domain-aware Dispatch module to enable inter-domain collaboration and knowledge reuse through adaptive cooperation among multiple shared adapters. Besides, a Holistic Domain Controller is designed to dynamically regulate the learning capacity for new domains based on knowledge similarity, thereby effectively unleashing the generalization potential of pre-trained models. Furthermore, a lightweight Similarity-Guided Auto-Selector is proposed to assign inputs to relevant adapters during inference automatically. Extensive experiments validate VIA's effectiveness, surpassing state-of-the-art methods in both seen and unseen domains.