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Xiaodong Wang, Zhirong Wu, Langling Huang, Yuxi Zheng, Peixi Peng

Multimodal Large Language Models (MLLMs) have made great progress in video understanding tasks. However, when it comes to understanding complex or lengthy videos, MLLMs tend to overlook details or produce hallucinations. To alleviate these issues, recent work has attempted to leverage reinforcement learning (RL) to boost models' deep linguistic reasoning of complex videos. But these methods have two main problems: First, the RL framework they used has unstable training, high training costs, and is difficult to train satisfactory video reasoning models; Second, the linguistic reasoning process is difficult to guarantee the reliability of visual information. To alleviate these problems, we propose to use multimodal elements for reasoning, and we design a novel framework to build and enhance versatile video reasoning capabilities on MLLMs. We carefully design a multi-task cold start and multi-task reinforcement learning to improve the model's visual perception and proficiency in multiple capabilities. In the inference phase, we leverage multimodal reasoning and dynamic sampling to further improve the performance. We verified the efficiency of the framework on a base MLLM (Qwen2-VL-7B-Base). Through cold-start with 3k data and reinforcement learning training with 5k data, combined with inference design, our final model significantly outperforms the base model on seven public video benchmarks, even surpassing and approaching the state-of-the-art Instruct Models such as Qwen2.5-VL-7B-Instruct trained with large-scale data. Our code will be available at https://github.com/Wang-Xiaodong1899/VideoReasoner.

Yifan Liu, Fangneng Zhan, Wanhua Li, Haowen Sun, Katerina Fragkiadaki, Hanspeter Pfister

Estimating robot pose from a monocular RGB image is a challenge in robotics and computer vision. Existing methods typically build networks on top of 2D visual backbones and depend heavily on labeled data for training, which is often scarce in real-world scenarios, causing a sim-to-real gap. Moreover, these approaches reduce the 3D-based problem to 2D domain, neglecting the 3D priors. To address these, we propose Robot Topological Alignment Graph (RoboTAG), which incorporates a 3D branch to inject 3D priors while enabling co-evolution of the 2D and 3D representations, alleviating the reliance on labels. Specifically, the RoboTAG consists of a 3D branch and a 2D branch, where nodes represent the states of the camera and robot system, and edges capture the dependencies between these variables or denote alignments between them. Closed loops are then defined in the graph, on which a consistency supervision across branches can be applied. Experimental results demonstrate that our method is effective across robot types, suggesting new possibilities of alleviating the data bottleneck in robotics.

Wenchao Guan, Chuan Lin, Sihan Huang, Xiongzhen Wang, Xintao Pang

In remote sensing images, small objects often exhibit low color contrast and blurred edges, leading to suboptimal feature extraction. Physiological studies indicate that the LGN/V1-V2-V4 pathway provides color-opponent sensitivity and hierarchical enhancement for color information extraction, whereas the V1-V4 pathway exhibits strong orientation selectivity for edge extraction. Integrating these complementary visual signals in the V4 region can substantially improve target discrimination. Motivated by these findings, we propose a dual-backbone network (BDNet) to enhance feature extraction for small objects. BDNet adopts a parallel architecture to capture fine-grained features from color and edge cues. Specifically, the color-extraction backbone simulates the color-opponent mechanism in LGN/V1 via a Color Antagonism Module (CAM) to amplify color differences, and further mimics the chromatic processing hierarchy in V2 using a Visual Cortex Hue Enhancement Module (VCHM) to enrich hue representations. Together, these two modules alleviate low color contrast. The edge-extraction backbone simulates the orientation selectivity of receptive fields in V1 through an Orientation Selective Module (OrSM) to select and enhance salient edges, thereby reducing edge blurring from fragmented edge responses. Finally, the two feature types are fused via a Feature Fusion Module (FFM) that emulates integration in V4, yielding a comprehensive feature representation. Experiments demonstrate that BDNet outperforms state-of-the-art methods on the VisDrone2019, NWPU VHR-10, and AI-TODv2 datasets, providing a bio-inspired solution for small-object detection in remote sensing images.

Jianwei Fei, Yunshu Dai, Xiaoyu Zhou, Zhihua Xia, Alessandro Piva

Extracting reliable generative traces in generated images is critical for AI-generated images (AIGIs) detection. However, a fundamental challenge exists: AIGIs inherently contain generative traces with no trace-free counterpart available, making supervised extraction of these artifacts infeasible. In this work, we overcome this through a surrogate supervision framework. We design a dynamic reconstructor that simulates diverse generative traces on real images through stochastically varied architectures and parameters. The reconstruction residuals serve as supervision to train an extractor that learns to isolate traces, i.e., generative signatures (GenSign). A detector then fuses extracted GenSign with RGB features to distinguish real images from AIGIs. Our key insight is that sufficient architectural diversity in simulation enables effective transfer to real-world generators, resolving the absence of ground truth GenSign. Extensive experiments across four benchmarks demonstrate state-of-the-art generalization, confirming that our simulation-based learning paradigm is capable of extracting general and transferable forensic features.

Shuhan Tan, Kashyap Chitta, Yuxiao Chen, Ran Tian, Yurong You, Yan Wang, Wenjie Luo, Yulong Cao, Philipp Krähenbühl, Marco Pavone 等

Recent Vision-Language-Action (VLA) models for autonomous driving explore inference-time reasoning as a way to improve driving performance and safety in challenging scenarios. Most prior work uses natural language to express chain-of-thought (CoT) reasoning before producing driving actions. However, text may not be the most efficient representation for reasoning. In this work, we present Latent-CoT-Drive (LDrive): a model that expresses CoT in a latent language that captures possible outcomes of the driving actions being considered. Our approach unifies CoT reasoning and decision making by representing both in an action-aligned latent space. Instead of natural language, the model reasons by interleaving (1) action-proposal tokens, which use the same vocabulary as the model's output actions; and (2) world model tokens, which are grounded in a learned latent world model and express future outcomes of these actions. We cold start latent CoT by supervising the model's action proposals and world model tokens based on ground-truth future rollouts of the scene. We then post-train with closed-loop reinforcement learning to strengthen reasoning capabilities. On a large-scale end-to-end driving benchmark, LDrive achieves faster inference, better trajectory quality, and larger improvements from interactive reinforcement learning compared to both non-reasoning and text-reasoning baselines.

Yujian Zhao, Hankun Liu, Guanglin Niu

Cross-modal ship re-identification (ReID) between optical and synthetic aperture radar (SAR) imagery has recently emerged as a critical yet underexplored task in maritime intelligence and surveillance. However, the substantial modality gap between optical and SAR images poses a major challenge for robust identification. To address this issue, we propose MOS, a novel framework designed to Mitigate the Optical-SAR modality gap and achieve modality-consistent feature learning for optical-SAR cross-modal ship ReID. MOS consists of two core components: (1) Modality-Consistent Representation Learning (MCRL) applies denoise SAR image procession and a class-wise modality alignment loss to align intra-identity feature distributions across modalities. (2) Cross-modal Data Generation and Feature fusion (CDGF) leverages a brownian bridge diffusion model to synthesize cross-modal samples, which are subsequently fused with original features during inference to enhance alignment and discriminability. Extensive experiments on the HOSS ReID dataset demonstrate that MOS significantly surpasses state-of-the-art methods across all evaluation protocols, achieving notable improvements of +3.0%, +6.2%, and +16.4% in R1 accuracy under the ALL to ALL,Optical to SAR, and SAR to Optical settings, respectively. The source codes are available at https://github.com/yjzhao1019/MOS.

Xuelu Li, Zhaonan Wang, Xiaogang Wang, Lei Wu, Manyi Li, Changhe Tu

Reconstructing articulated objects into high-fidelity digital twins is crucial for applications such as robotic manipulation and interactive simulation. Recent self-supervised methods using differentiable rendering frameworks like 3D Gaussian Splatting remain highly sensitive to the initial part segmentation. Their reliance on heuristic clustering or pre-trained models often causes optimization to converge to local minima, especially for complex multi-part objects.To address these limitations, we propose ArtPro, a novel self-supervised framework that introduces adaptive integration of mobility proposals. Our approach begins with an over-segmentation initialization guided by geometry features and motion priors, generating part proposals with plausible motion hypotheses. During optimization, we dynamically merge these proposals by analyzing motion consistency among spatial neighbors, while a collision-aware motion pruning mechanism prevents erroneous kinematic estimation. Extensive experiments on both synthetic and real-world objects demonstrate that ArtPro achieves robust reconstruction of complex multi-part objects, significantly outperforming existing methods in accuracy and stability.

Jun Myeong Choi, Jae Shin Yoon, Luchao Qi, Roni Sengupta, Joon-Young Lee

We present a method for harmonizing the lighting of a foreground video to match a target background scene, adjusting shadows, color tone, and illumination intensity (relightful harmonization). Unlike images, acquiring labeled data for videos, where identical motions are recorded under different lighting conditions, is practically infeasible and non-scalable. While one way to create such paired data is to apply existing image-based harmonization models frame by frame to a video, the resulting outputs often suffer from significant temporal jitters. We overcome this problem by introducing a novel lighting deflickering model that can stabilize the global and local lighting flickering artifacts. Our video diffusion model learns from these upgraded deflickered data with a volume of real and synthetic videos to generate high-quality video harmonization results. We further propose an asymmetric alpha mask conditioning technique to learn the clean boundaries from real videos. Experiments demonstrate that our model achieves strong temporal coherence, naturalness, cleaner boundaries, and physically meaningful lighting behavior, while maintaining strong relighting expressiveness compared to prior image-based and video-based harmonization methods.

Rong Wang, Ruyi Zha, Ziang Cheng, Jiayu Yang, Pulak Purkait, Hongdong Li

We present a novel method for generating geometrically realistic and consistent orbital videos from a single image of an object. Existing video generation works mostly rely on pixel-wise attention to enforce view consistency across frames. However, such mechanism does not impose sufficient constraints for long-range extrapolation, e.g. rear-view synthesis, in which pixel correspondences to the input image are limited. Consequently, these works often fail to produce results with a plausible and coherent structure. To tackle this issue, we propose to leverage rich shape priors from a 3D foundational generative model as an auxiliary constraint, motivated by its capability of modeling realistic object shape distributions learned from large 3D asset corpora. Specifically, we prompt the video generation with two scales of latent features encoded by the 3D foundation model: (i) a denoised global latent vector as an overall structural guidance, and (ii) a set of latent images projected from volumetric features to provide view-dependent and fine-grained geometry details. In contrast to commonly used 2.5D representations such as depth or normal maps, these compact features can model complete object shapes, and help to improve inference efficiency by avoiding explicit mesh extraction. To achieve effective shape conditioning, we introduce a multi-scale 3D adapter to inject feature tokens to the base video model via cross-attention, which retains its capabilities from general video pretraining and enables a simple and model-agonistic fine-tuning process. Extensive experiments on multiple benchmarks show that our method achieves superior visual quality, shape realism and multi-view consistency compared to state-of-the-art methods, and robustly generalizes to complex camera trajectories and in-the-wild images.

Claudia Cuttano, Gabriele Trivigno, Christoph Reich, Daniel Cremers, Carlo Masone, Stefan Roth

In-context segmentation (ICS) aims to segment arbitrary concepts, e.g., objects, parts, or personalized instances, given one annotated visual examples. Existing work relies on (i) fine-tuning vision foundation models (VFMs), which improves in-domain results but harms generalization, or (ii) combines multiple frozen VFMs, which preserves generalization but yields architectural complexity and fixed segmentation granularities. We revisit ICS from a minimalist perspective and ask: Can a single self-supervised backbone support both semantic matching and segmentation, without any supervision or auxiliary models? We show that scaled-up dense self-supervised features from DINOv3 exhibit strong spatial structure and semantic correspondence. We introduce INSID3, a training-free approach that segments concepts at varying granularities only from frozen DINOv3 features, given an in-context example. INSID3 achieves state-of-the-art results across one-shot semantic, part, and personalized segmentation, outperforming previous work by +7.5 % mIoU, while using 3x fewer parameters and without any mask or category-level supervision.

Yongrui Ma, Shijie Zhao, Mingde Yao, Junlin Li, Li Zhang, Xiaohong Liu, Qi Dou, Jinwei Gu, Tianfan Xue

Despite recent progress, diffusion-based video frame interpolation methods still struggle with large, complex motions, resulting in discontinuous motions and inconsistent object appearances across frames. We observe that these limitations arise from both the current full-sequence interpolation strategy and the pixel reconstruction training objective. To solve these challenges, we propose ARVFI, a novel video diffusion-based interpolation method for large complex motion interpolation. Instead of generating all intermediate frames simultaneously, ARVFI interpolates in an autoregressive manner from two input frames to the middle ones. Thus, ARVFI interpolates a frame that is further away from the inputs based on all previous interpolation results, resulting in smoother motion transitions and better temporal consistency. Additionally, ARVFI further utilizes DINOv3 features as motion representations, which provide high-level semantics for accurate motion estimation, compared with a simple pixel-level loss. With all these designs, ARVFI generates the intermediate DINOv3 features first and then the frames with an effective conditional generation method for frames. Our ARVFI consistently outperforms existing methods with superior interpolation accuracy and visual quality.

Xu Wang, Zhiru Wang, Shiyun Xie, Chengwei Pan, Yisong Chen

While 3D Gaussian Splatting (3DGS) achieves real-time photorealistic rendering, its performance degrades significantly when training images contain transient objects that violate multi-view consistency. Existing methods face a circular dependency: accurate transient detection requires a well-reconstructed static scene, while clean reconstruction itself depends on reliable transient masks. We address this challenge with DualSplat, a Failure-to-Prior framework that converts first-pass reconstruction failures into explicit priors for a second reconstruction stage. We observe that transients, which appear in only a subset of views, often manifest as incomplete fragments during conservative initial training.We exploit these failures to construct object-level pseudo-masks by combining photometric residuals, feature mismatches, and SAM2 instance boundaries. These pseudo-masks then guide a clean second-pass 3DGS optimization, while a lightweight MLP refines them online by gradually shifting from prior supervision to self-consistency. Experiments on RobustNeRF and NeRF On-the-go show that DualSplat outperforms existing baselines, demonstrating particularly clear advantages in transient-heavy scenes and transient regions.

Cheng Peng, Zhuo Su, Liao Wang, Chen Guo, Zhaohu Li, Chengjiang Long, Zheng Lv, Jingxiang Sun, Chenyangguang Zhang, Yebin Liu

We present FlexAvatar, a flexible large reconstruction model for high-fidelity 3D head avatars with detailed dynamic deformation from single or sparse images, without requiring camera poses or expression labels. It leverages a transformer-based reconstruction model with structured head query tokens as a canonical anchor to aggregate flexible input-number-agnostic, camera-pose-free and expression-free inputs into a robust canonical 3D representation. For detailed dynamic deformation, we introduce a lightweight UNet decoder conditioned on UV-space position maps, which can produce detailed expression-dependent deformations in real time. To better capture rare but critical expressions like wrinkles and bared teeth, we also adopt a data distribution adjustment strategy during training to balance the distribution of these expressions in the training set. Moreover, a lightweight 10-second refinement can further enhance identity-specific details in extreme identities without affecting deformation quality. Extensive experiments demonstrate that our FlexAvatar achieves superior 3D consistency, detailed dynamic realism compared with previous methods, providing a practical solution for animatable 3D avatar creation.

Zhengpeng Feng, Clement Atzberger, Sadiq Jaffer, Jovana Knezevic, Silja Sormunen, Robin Young, Madeline C. Lisaius, Markus Immitzer, Toby Jackson, James Ball 等

Satellite Earth-observation (EO) time series in the optical and microwave ranges are often irregular due to orbital patterns and cloud obstruction, and while compositing addresses these issues, it loses critical phenological information. To overcome this, we present TESSERA, a pixel-wise foundation model for multi-modal (Sentinel-1/2) EO time series that learns robust, label-efficient embeddings. During training, TESSERA uses Barlow Twins and sparse random temporal sampling to enforce invariance to the selection of valid observations, aided by two key regularizers: global shuffling to decorrelate spatial neighborhoods and mix-based regulation for invariance under extreme sparsity. We find that for diverse classification, segmentation, and regression tasks, TESSERA embeddings deliver state-of-the-art accuracy with high label efficiency, often requiring only a small task head and minimal computation. To democratize access, adhere to FAIR principles, and simplify use, we release global, annual, 10m, pixel-wise int8 embeddings together with open weights/code and lightweight adaptation heads, providing practical tooling for large-scale retrieval and inference at planetary scale. All code and data are available at https://github.com/ucam-eo/tessera.

Yik San Cheng, Runkai Zhao, Weidong Cai

2D visual foundation models, such as DINOv3, a self-supervised model trained on large-scale natural images, have demonstrated strong zero-shot generalization, capturing rich global context and fine-grained structural cues. However, an analogous 3D foundation model for downstream volumetric neuroimaging remains lacking, largely due to the challenges of 3D image acquisition and the scarcity of high-quality annotations. To address this gap, we propose to adapt the 2D visual representations learned by DINOv3 to a 3D biomedical segmentation model, enabling more data-efficient and morphologically faithful neuronal reconstruction. Specifically, we design an inflation-based adaptation strategy that inflates 2D filters into 3D operators, preserving semantic priors from DINOv3 while adapting to 3D neuronal volume patches. In addition, we introduce a topology-aware skeleton loss to explicitly enforce structural fidelity of graph-based neuronal arbor reconstruction. Extensive experiments on four neuronal imaging datasets, including two from BigNeuron and two public datasets, NeuroFly and CWMBS, demonstrate consistent improvements in reconstruction accuracy over SoTA methods, with average gains of 2.9% in Entire Structure Average, 2.8% in Different Structure Average, and 3.8% in Percentage of Different Structure.

Seunghoi Kim, Henry F. J. Tregidgo, Chen Jin, Matteo Figini, Daniel C. Alexander

Generative models are prone to hallucinations: plausible but incorrect structures absent in the ground truth. This issue is problematic in image restoration for safety-critical domains such as medical imaging, industrial inspection, and remote sensing, where such errors undermine reliability and trust. For example, in low-field MRI, widely used in resource-limited settings, restoration models are essential for enhancing low-quality scans, yet hallucinations can lead to serious diagnostic errors.Progress has been hindered by a circular dependency: evaluating hallucinations requires labeled data, yet such labels are costly and subjective.We introduce HalluGen, a diffusion-based framework that synthesizes realistic hallucinations with controllable type, location, and severity, producing perceptually realistic but semantically incorrect outputs (segmentation IoU drops from 0.86 to 0.36).Using HalluGen, we construct the first large-scale hallucination dataset comprising 4,350 annotated images derived from 1,450 brain MR images for low-field enhancement, enabling systematic evaluation of hallucination detection and mitigation.We demonstrate its utility in two applications: (1) benchmarking image quality metrics and developing Semantic Hallucination Assessment via Feature Evaluation (SHAFE), a feature-based metric with soft-attention pooling that improves hallucination sensitivity over traditional metrics; and (2) training reference-free hallucination detectors that generalize to real restoration failures.Together, HalluGen and its open dataset establish the first scalable foundation for evaluating hallucinations in safety-critical image restoration.

Boce Kang

Medical image segmentation demands both high accuracy and computational efficiency, yet existing methods face a critical trade-off: CNNs lack global context while transformers incur prohibitive costs for deployment on resource-constrained devices. To address this challenge, we propose a Physics-informed Multi-scale Refinement Network (PMRNet), integrating symplectic geometry, renormalization group theory, and entropy diffusion to guide feature learning. PMRNet features three innovations: (1) a physics-informed encoder with Enhanced Symplectic Convolution for boundary detection and Renormalization Group-inspired Downsampling for information preservation; (2) a Pseudo-Global Receptive Field module achieving near-global context with linear complexity through entropy-driven diffusion; and (3) a boundary-aware decoder for precise delineation. With only 0.87M parameters and 3.43 GFLOPs, PMRNet achieves 87.25% IoU and 92.56% Dice on the challenging Clinic dataset, outperforming state-of-the-art (SOTA) models with even 100x more parameters across 12 medical imaging datasets while maintaining computational efficiency. Code is available at https://github.com/KangBoce/PMRNet.

Jiayuan Chen, Ruoqi Liu, Zishan Gu, Ping Zhang

Microscopy-based phenotypic profiling is scalable for drug discovery but lacks the mechanistic depth of transcriptomics, which remains costly and scarce. Existing multimodal approaches either use images to support other modalities or naively align representations by sample identity, ignoring cell-type and dose variations in weakly paired data-limiting generalization to unseen interventions. In this paper, we introduce an intervention-aware distillation framework that leverages perturbational transcriptomics to guide image representation learning. A transcriptome-conditioned teacher integrates gene expression and intervention metadata to produce soft distributions over a chemistry-aware codebook organized by drug similarity. The teacher employs a fine-tuned single-cell foundation model to encode cell-type context and disentangle dose effects. An image-only student learns to predict these distributions from microscopy alone, distilling mechanistic knowledge while operating independently at test time. This design emphasizes intervention semantics rather than identity alignment and explicitly handles dose and cell-type mismatches. We provide theoretical guarantees showing that transcriptomic guidance tightens the risk bound for image-based prediction. On Cell Painting and RxRx datasets paired with L1000, our method significantly improves one-shot transfer to unseen interventions and drug-target gene discovery compared to self-supervised and alignment baselines

Wanhee Lee, Klemen Kotar, Rahul Mysore Venkatesh, Jared Watrous, Daniel LK Yamins

Predicting how a scene will evolve after a desired 3D transformation from images is a central goal in vision, graphics, and robotics. Yet unlike ideal simulators with full access to 3D geometry and dynamics, real world systems must rely on perceptual inputs and local actions that are inherently partial and incomplete. In this work, we present P3Sim, a physical world modeling system that simulates future scene states under both partial observations and incomplete 3D transformation signals. P3Sim is composed of three interacting components: a learned physical world model, a geometric conditioning module, and a persistent scene memory. The world model interprets perception as probabilistic inference over multimodal scene variables, providing predictions of the distributions of any scene variable conditioned on any combination of others. The geometric conditioning module provides a partial 3D transform signal for conditioning the world model at inference time. The persistent scene memory integrates predictions over time, enabling online updates and consistency under uncertainty. By combining learned inference with explicit geometric structure, P3Sim balances data-driven flexibility with built-in inductive bias. This design yields a flexible perceptual simulator that generalizes across diverse 3D transformation tasks, such as novel view synthesis, object manipulation, and dynamic scene prediction, advancing toward general purpose 3D scene understanding and transformation.

Qiuhai Yan, Kang Chen, Zhengjie Lu, Tingting Wang, Faming Fang, Guixu Zhang

Implicit neural representation (INR) based methods learn a continuous mapping from a low-resolution (LR) target magnetic resonance (MR) image and a high-resolution (HR) reference image to achieve arbitrary-scale super-resolution (SR). However, their inherent spectral bias favors learning low-frequency (LF) components, often failing to capture the sharp transitions at anatomical boundaries and resulting in the loss of high-frequency (HF) details. Inspired by 3D Gaussian splatting, we propose GaussM2ASR (Gaussian Multi-contrast MRI Arbitrary-scale Super-Resolution), which converts the challenging task of HF anatomical reconstruction into a smoother parameter optimization problem by learning the parameters of anisotropic 2D Gaussian kernels. To handle inter-contrast discrepancies, we introduce an anatomy-guided pipeline comprising three core modules: a Structure Prior Modulation Fusion (SPMF) module for feature enhancement; an Anatomy-Guided Dual-Domain Cross Attention (AG-DDCA) module for joint spatial-frequency modeling; and an Anatomy-Guided Gaussian Parametrizer (AGGP) that leverages gradient-based sparse attention to concentrate Gaussian centers on critical anatomical structures. Extensive experiments on multiple datasets demonstrate that GaussM2ASR surpasses state-of-the-art methods in recovering fine anatomical details. Our source codes have been released at https://github.com/Qiuhai-CV/GaussM2ASR.