ECCV 2026 Papers

X-ray computed tomography (CT) reconstruction is inher-ently an ill-posed inverse problem, particularly with sparse-view pro-jections. Unlike traditional frame-based sensors that acquire projectionsat fixed intervals, neuromorphic event cameras operate asynchronouslyat the pixel level and trigger events only when the log-intensity changeexceeds a threshold. This asynchronous sensing naturally captures high-frequency angular variations during object rotation, providing comple-mentary information between sparsely sampled projections. However,the application of event cameras to CT reconstruction remains largelyunexplored. To bridge this gap, we introduce a Neuromorphic X-rayCT framework. Specifically, we propose an Event-enhanced Neural At-tenuation Field (ENAF) that models the volumetric attenuation fieldby jointly leveraging sparse projections and event streams. To supportand validate this framework, we construct synthetic neuromorphic CTdatasets and acquire a real-world dataset. Experiments on both syn-thetic and real-world datasets demonstrate that ENAF consistently out-performs state-of-the-art frame-only methods while maintaining train-ing efficiency. To the best of our knowledge, this work presents thefirst application of neuromorphic sensing to X-ray CT and establishesthe feasibility of event-enhanced sparse-view CT reconstruction. Thecode and datasets are available at https://wanghongjian98.github.io/projects/neuroxct/.

Flow-based generative methods offer a simple and effectiveframework for high-fidelity generation, yet pretrained flow models arerarely used in their vanilla conditional form: in image generation, sam-ples without guidance often appear diffuse and lack fine-grained detail.Existing guidance techniques such as classifier-free guidance (CFG) im-prove fidelity but reduce sample diversity. We introduce MomentumGuidance (MG), a guidance method that improves sample quality byextrapolating the current velocity away from an exponential moving av-erage of past velocities along the ODE trajectory, while preserving thestandard one-evaluation-per-step cost. MG provides gains beyond CFG,improving the precision–recall Pareto frontier. Experiments demonstratethe effectiveness of MG across benchmarks. On ImageNet-256, MG im-proves FID by 36.54% without CFG and 25.42% with CFG on averageacross sampling settings, attaining an FID of 1.553 at 16 sampling steps.Evaluations on large flow-based models, including Stable Diffusion 3 andFLUX.1-dev, further confirm improvements across standard metrics.

Vision-Language Action (VLAs) models promise to extendthe remarkable success of vision-language models (VLMs) to robotics. Yet,unlike VLMs in the vision-language domain, VLAs for robotics requirefinetuning to contend with varying physical factors like robot embodiment,environment characteristics, and spatial relationships of each task. Wepropose leveraging few-shot demonstrations of tasks to better capture thisphysical variability. Overfitting to the trained task is yet another problemof existing methods. Inspired by functional specificity, we hypothesizethat finetuning only task-specific sparse model representations is bothmore effective and better retains pretrained model generality. In thiswork, we introduce Robotic Steering, a finetuning approach groundedin mechanistic interpretability that leverages few-shot demonstrations toidentify and selectively finetune task-specific attention heads aligned withthe physical, visual, and linguistic requirements of robotic tasks. Throughcomprehensive on-robot evaluations with a Franka Emika robot arm, wedemonstrate that Robotic Steering outperforms LoRA while achievingsuperior robustness under task variation, reduced computational cost,and enhanced interpretability for adapting VLAs to diverse robotic tasks.

Video frame interpolation (VFI) remains a challenging task,particularly when dealing with large, non-linear motions and complexocclusions. While flow-based methods are prevalent, they often strugglewith ambiguous correspondences. Recent VFI methods based on selectiveState Space Models (SSMs) are still limited by static grid-based scanningthat misaligns with physical motion. In this paper, we propose Motion-Aligned Selective Scan (MASS), a novel framework that reformulates fea-ture scanning from static spatial grids to dynamic motion trajectories.MASS builds a feature sequence along each pixel’s flow-guided trajectoryand aggregates it with an SSM. Specifically, we introduce a learnable non-linear path integration to approximate complex curved trajectories viaresidual velocity updates, and a velocity-aware SSM that dynamicallyadjusts the sampling budget and step size based on motion magnitude.This adaptive strategy allocates denser sampling to fast-motion regionswhile keeping static regions efficient. Furthermore, the aggregated statesguide a refinement module to rectify intermediate flows and masks in anend-to-end manner. Extensive experiments indicate that MASS achieveshighly competitive overall performance on standard benchmarks, estab-lishing state-of-the-art results particularly in challenging scenarios withlarge displacements and complex dynamics.

Synthesizing high-fidelity and controllable 4D LiDAR datais crucial for creating scalable simulation environments for autonomousdriving. This task is inherently challenging due to the sensor’s uniquespherical geometry, the temporal sparsity of point clouds, and the com-plexity of dynamic scenes. To address these challenges, we present LiS-TAR, a novel generative world model that operates directly on the sen-sor’s native geometry. LiSTAR introduces a Hybrid-Cylindrical-Spherical(HCS) representation to preserve data fidelity by mitigating quanti-zation artifacts common in Cartesian grids. To capture complex dy-namics from sparse temporal data, it utilizes a Spatio-Temporal At-tention with Ray-Centric Transformer (START) that explicitly modelsfeature evolution along individual sensor rays for robust temporal co-herence. Furthermore, for controllable synthesis, we propose a novel 4Dpoint cloud-aligned voxel layout for conditioning and a correspondingdiscrete Masked Generative START (MaskSTART) framework, whichlearns a compact, tokenized representation of the scene, enabling effi-cient, high-resolution, and layout-guided compositional generation. Com-prehensive experiments validate LiSTAR’s state-of-the-art performanceacross 4D LiDAR reconstruction, prediction, and conditional genera-tion. Our method achieves substantial quantitative gains: improving re-construction IoU by up to 85%, lowering prediction L1 Med by up to64%, and reducing generation MMD by 61%. This level of performanceprovides a powerful new foundation for creating realistic and control-lable simulations for autonomous driving systems. Project Page: https://ocean-luna.github.io/LiSTAR.github.io/.

In autonomous driving scenarios, the collected LiDAR pointclouds can be affected by occlusion and long-range sparsity, limitingthe perception of autonomous driving systems. Scene completion meth-ods can infer the missing parts of incomplete single LiDAR scans. Re-cent methods adopt point-level denoising diffusion probabilistic modelsthat rely on approximations to handle scene-scale data, leading to amismatch between training and inference initial distributions. We pro-pose LiFlow, the first point-level flow matching method for 3D LiDARscene completion. LiFlow improves upon existing diffusion-based meth-ods by directly aligning single LiDAR scans to complete scenes, en-suring consistent initial distributions between training and inference.LiFlow introduces nearest neighbor flow matching and Chamfer distancematching to enhance both local structure and global coverage in thealignment of point clouds. Compared to existing diffusion-based meth-ods, LiFlow reduces the number of inference steps, enabling more ef-ficient scene completion while maintaining stability and high genera-tion quality. Extensive experiments demonstrate that LiFlow achievesstate-of-the-art performance across multiple metrics. Code is availableat https://github.com/matteandre/LiFlow.

Video understanding has become more and more importantwith the growth of Artificial Intelligence (AI) for video generation. Re-cently, Multimodal Large Language Model (M-LLM) has shown its ca-pability in video understanding. Video summarization, a specific domainof video understanding, has proven its importance for efficient naviga-tion and retrieval. Both video understanding and video summarizationrequire a good selection of key frames in a video. Current video summa-rization methods heavily focus on the selected key frames and correlatedsegment captions. However, existing approaches overlook the perspectiveof treating the importance of the frames globally. We argue that usingdiscrete selected frames for summarization will not only reduce the un-derstanding coherence, but also lost important information in the video,as well as wasting the original capacity of the MLLMs. In this paper, wepropose HAS, a Highlight-guided Attention Steering method for videosummarization. We consider a challenging but practical setting wherethe video given to MLLMs for summarize should be continuous but withhighlight guidance. HAS mainly consists of two parts: The first part isto find a continuous frame-level highlight distribution for the video glob-ally. The second part is to apply the highlight distribution as an attentionsteering vector for the MLLM, targeting a better understanding of thevideo, and thus during the model inference time, putting more attentionon the highlighted frames, while avoiding lost entire information on lesshighlighted frames through putting less attention instead of forgettingthem. We evaluated HAS on a variety of benchmarks, and it has shownconvincing performance in video summarization.

Point cloud segmentation is critical for 3D scene understand-ing. However, sparse and irregular point distributions provide limitedappearance evidence, making geometry-only features insufficient to dis-tinguish objects with similar shapes but distinct appearances (e.g., color,texture, and material). We propose Gaussian-to-Point (G2P), which trans-fers Gaussian attributes from 3D Gaussian Splatting to point clouds formore discriminative and appearance-consistent segmentation. Our G2Paddresses the misalignment between optimized Gaussians and originalpoint geometry by establishing point-wise correspondences. By distillingopacity-derived visibility cues, we mitigate the geometric ambiguity thatlimits existing models. Additionally, Gaussian scale attributes enableprecise boundary localization in complex 3D scenes. Extensive experi-ments demonstrate that our approach achieves competitive performanceon standard benchmarks and shows notable improvements on geomet-rically challenging classes, without pretrained 2D features or languagesupervision in our segmentation pipeline.

3D Gaussian Splatting (3DGS) achieves state-of-the-art rendering quality at real-time speeds but suffers from “model bloat”—a large number of redundant, low-opacity Gaussians that inflate memory usage and training costs. This inefficiency stems from the standard “densifythen-prune” paradigm, which expands the model aggressively before relying on pruning to achieve compactness. To mitigate this problem, we present an efficient training framework that builds an intrinsically compact representation, replacing the conventional densify-then-prune cycle. Our method leverages a synergistic design: an L reconstruction loss to 2 provide error-proportional gradients that stabilize optimization, and a novel Polarized Opacity Prior (POP) to actively manage the Gaussian population. POP steers informative primitives toward full opacity and uninformative ones toward transparency, enabling natural pruning and accelerating rendering through Early Ray Termination. Experiments on three public datasets demonstrate that our approach consistently achieves accelerated 3DGS training with significantly fewer Gaussians while maintaining comparable visual reconstruction quality. These results show that the proposed framework provides a simple and effective path toward fast and inherently compact 3DGS training.

Vision-Language Models (VLMs) provide strong image/video-level reasoning, but their use in embodied scene understanding remainslimited by the fixed viewpoints available in episodic RGB-D memories.Such observations can miss query-relevant evidence due to occlusion, ob-ject truncation, limited fields of view, or suboptimal view composition. Wepresent SplatReasoner, a framework that introduces novel view synthesisinto the VLM reasoning process by leveraging 3D Gaussian Splatting(3DGS). Given a user query about a 3D scene, SplatReasoner retrievesand synthesizes camera viewpoints that provide the most relevant visualevidence for answering the query and grounding the referred entitiesin 3D. Experiments show that query-conditioned novel view synthesisimproves embodied reasoning and grounding over fixed-view memory andlanguage-embedded 3DGS baselines.

Diffusion models are primarily trained for image synthesis,yet their denoising trajectories encode rich, spatially aligned visual pri-ors. In this paper, we demonstrate that these priors can be utilized fortext-conditioned semantic and open-vocabulary segmentation, and thisapproach can be generalized to various downstream tasks to make ageneral-purpose diffusion segmentation framework. Concretely, we intro-duce DiGSeg (Diffusion Models as a Generalist Segmentation Learner),which repurposes a pretrained diffusion model into a unified segmenta-tion framework. Our approach encodes the input image and ground-truthmask into the latent space and concatenates them as conditioning sig-nals for the diffusion U-Net. A parallel CLIP-aligned text pathway injectslanguage features across multiple scales, enabling the model to align tex-tual queries with evolving visual representations. This design transformsan off-the-shelf diffusion backbone into a universal interface that pro-duces structured segmentation masks conditioned on both appearanceand arbitrary text prompts. Extensive experiments demonstrate state-of-the-art performance on standard semantic segmentation benchmarks, aswell as strong open-vocabulary generalization and cross-domaintransfer to medical, remote sensing, and agricultural scenarios—withoutdomain-specific architectural customization. These results indicate thatmodern diffusion backbones, can serve as generalist segmentation learn-ers rather than pure generators, narrowing the gap between visual gen-eration and visual understanding.

In the task of human mesh recovery (HMR), multi-personscenes are particularly difficult to handle due to the many entities thatappear and occlusions between them over time. In particular for videoinputs, there is a need to track each entity reliably and consistently.Existing methods rely on pretrained human detection modules, increas-ing their runtime and limiting the number of tracked entities. We presentDETRAM, a unified framework for multi-person HMR and tracking thatsimultaneously detects, reconstructs, and tracks humans across time,both automatically and via user prompts. DETRAM uses a single trans-former decoder with an identity-consistent set of learnable query embed-dings that persist across frames: detection queries discover new people,tracking queries maintain pose and shape for existing individuals, andprompt queries follow user-specified identities. Our approach achievesstate-of-the-art tracking results on PoseTrack21, 3DPW, BEDLAM, andMuPoTS-3D, and competitive reconstruction accuracy on BEDLAM and3DPW, while uniquely supporting prompt-based tracking of individualsin multi-person scenes. To our knowledge, this is the first method tounify promptability and multi-person HMR with tracking in an end-to-end trainable framework, enabling user-directed human analysis in

Existing latent di!usion models typically couple scale withcontent complexity, using more latent tokens to represent higher-resolutionimages or higher–frame rate videos. However, the latent capacity re-quired to represent visual data primarily depends on content complex-ity, with scale serving only as an upper bound. Motivated by this obser-vation, we propose DCS-LDM, a novel paradigm for visual generationthat decouples information complexity from scale. DCS-LDM constructsa hierarchical, scale-independent latent space that models sample com-plexity through multi-level tokens and supports decoding to arbitraryresolutions and frame rates within a fixed latent representation. This la-tent space enables DCS-LDM to achieve a flexible computation–qualitytradeo!. Furthermore, by decomposing structural and detailed informa-tion across levels, DCS-LDM supports a progressive coarse-to-fine gen-eration paradigm. Experimental results show that DCS-LDM deliversperformance comparable to state-of-the-art methods while o!ering flex-ible generation across diverse scales and visual qualities.

Stereo-based 3D obstacle perception for autonomous driv-ing is currently constrained by an imbalanced triplet: deployment cost,detection accuracy, and open-set adaptability. While existing methodsstruggle to balance these three competing objectives, there is an urgentdemand for high-precision, real-time algorithms capable of detecting ar-bitrary obstacles in the wild. In this paper, we present DDStereo, a novelDual-Decoder Stereo Transformer that achieves a synergistic integrationof 3D object detection and Out-of-Distribution (OoD) road anomaly de-tection. Leveraging the geometric priors of stereo disparity, our approacheffectively couples 3D attribute regression with open-set foreground de-tection within a streamlined dual-branch decoder architecture. Conven-tional methods rely on complex feature-level fusion; DDStereo main-tains execution efficiency by employing a decoupled decoding strategyand shared object-level queries to ensure cross-modal target alignment.Extensive evaluations of public benchmarks demonstrate that DDStereonot only achieves state-of-the-art accuracy under open-set and closed-set protocols. Our method delivers real-time performance comparable tomonocular 3D detection baselines, providing a cost-effective solution forthe perception of obstacles of the normal and OoD category. Code andmodels are available at https://github.com/shiyi-mu/DDStereo.

Image demoiréing remains a challenging task due to the com-plex interplay between texture corruption and color distortions causedby moiré patterns. Existing methods, especially those relying on directimage-to-image restoration, often fail to disentangle these intertwinedartifacts effectively. While frequency-aware approaches offer a promisingdirection, their potential is hindered by the discrete transform (e.g., Haarwavelet or block-based DCT), which may suffer from spatial discontinu-ity, channel redundancy, and further cause error accumulation duringtheir fixed inverse processes. In this paper, we present Freqformer, aTransformer-based framework specifically designed for image demoiréingthrough targeted frequency separation. Our method performs an effectivefrequency decomposition that splits moiré patterns into high-frequencyspatially-localized textures and low-frequency scale-robust color distor-tions, which are then handled by a dual-branch architecture and anasymmetric training scheme tailored to their distinct characteristics. Wefurther propose a learnable Frequency Composition Transform (FCT)module to adaptively fuse the frequency-specific outputs, enabling con-sistent and high-fidelity reconstruction. To better aggregate the spatialdependencies and the inter-channel complementary information, we in-troduce a Spatial-Aware Channel Attention (SA-CA) module that refinesmoiré-sensitive regions without incurring high computational cost. Ex-tensive experiments on various demoiréing benchmarks demonstrate thatFreqformer achieves state-of-the-art performance with a compact modelsize. The code will be made publicly available at https://github.com/xyLiu339/Freqformer.

Despite significant advances in image segmentation, evenstate-of-the-art models produce masks with imperfect boundaries, se-mantic inconsistencies, and structural errors. Mask refinement addressesthese limitations, yet current approaches rely on simplistic syntheticnoise that fails to capture the complex error patterns of real segmen-tation models. We introduce Phoenix, a novel framework that lever-ages adversarial learning to generate semantically meaningful noise pat-terns and contrastive learning to model refinement relationships. Ourapproach consists of two key innovations: (1) Adversarial Mask Per-turbation, which employs embedding attacks to create semantic-awarenoise that mimics real segmentation errors, and (2) Contrastive MaskRefinement Learning, which establishes a tri-directional framework thatensures feature consistency within semantic regions while maintainingseparation between classes. Experiments demonstrate that Phoenix sig-nificantly outperforms existing methods across diverse tasks, while con-sistently enhancing state-of-the-art segmentation models with substan-tial improvements. Our code and project page are publicly available atTargetMaskMorphologicalMask (b) Morphological NoiseLess-Semantic, Unrealistic, and Non-Contextual NoiseAdversarialMask(ours)Semantic, Realistic, and Contextual False-Negative & False-Positive Noise(a) Qualitative Samples of Morphological and Adversarial Noise Masks (c) Adversarial NoiseFig. 2: Qualitative Comparison of noise patterns (a) between morphological andour adversarial noise masks. (b, c) Distribution of the Pearson correlation betweennoise location and image edge/texture maps on LVIS val. (b) Morphological noise isnarrowly concentrated near 0 (semantically uncorrelated), while (c) our adversarialnoise spans [−0.6, 0.8], where positive values indicate alignment with semantic struc-tures and negative values indicate alignment with homogeneous regions, mirroring thediverse error patterns of real models.

Online skeleton-based action recognition requires per-frameclassification using only past and current observations. We identify twokey challenges in this setting: (1) Not all frames are equal: discrimina-tive motion concentrates within specific temporal regions whose loca-tion varies across action categories, yet existing temporal modeling relieson fixed sampling patterns and causal attention that treat all positionsequally. (2) Not all futures need to be predicted: prior methods attempt toexplicitly predict future features to compensate for the unobserved por-tion, but the inherent ambiguity of human motion causes such predictionto degenerate into trivial identity mapping of the input. To address thesechallenges, we propose DART (Deformable Adaptive Reasoning withTemporal Queries). DART employs a Deformable Multi-scale TemporalNetwork (DMTN) that introduces input-dependent offsets into causalconvolutions, enabling content-adaptive sampling of the most informa-tive historical moments. The learned sampling positions naturally forma compact temporal memory, upon which a Temporal Query Decoder(TQD) operates: learnable queries interact with the memory to distillhigh-level action evolution patterns, providing complementary temporalreasoning without explicit future forecasting. Extensive experiments onNTU RGB+D 60, NTU RGB+D 120, and NW-UCLA demonstrate theeffectiveness of DART. On NTU RGB+D 60, DART achieves 73.99%AUC, surpassing the SOTA by +3.04%. Code is available.

The emergence of large-scale text-to-image diffusion (T2ID)models has led to significant advancements in generating high-qualityvisual content from textual prompts. However, these powerful capabil-ities have also raised growing concerns about the generation of harm-ful and copyrighted material. While existing concept erasure techniquescan effectively block the production of specific unwanted concepts fromprompts, they often fall short when it comes to erasing an entire cate-gory (including subcategories) and are typically limited to handling onlya few concepts at a time. In this paper, we introduce SubcategoricalUnlearning via Regularized Erasure (SURE), a novel method for re-moving entire subcategories from text-to-image diffusion models usingonly a single parent category as the target. Unlike prior approaches,SURE does not rely on sets of synonyms. Instead, it employs conceptspace to discover and eliminate the target category while preserving themodel’s overall utility. To further enhance erasure, SURE incorporatesLipschitz regularization, which encourages smoother model responses toperturbations around the target category. Specifically, the regularizationpromotes consistent behavior in the model’s latent space when exposedto slight variations of the category to be forgotten. This smoothnessconstraint aids in erasure while maintaining the model’s ability to gen-erate unrelated content. Extensive experiments conducted across threetasks—object removal, suppression of explicit content, and elimination ofartistic styles demonstrate that SURE achieves balanced performance inboth effective category erasure and preservation of non-target concepts.

Aerial-ground person re-identification (AG-ReID) is signifi-cantly challenged by heterogeneous geometric regimes and severe foreground-background imbalance. Traditional shared-backbone architectures suf-fer from transformation conflicts when modeling disparate aerial andground views, while residual connections in transformers often lead toidentity embedding contamination from persistent background activa-tions. To address these issues, we propose View-conditioned and Identity-Guided Adaptation (VIGA), a unified framework that mitigates AG-ReID-specific aerial-ground discrepancies at both the parameter and ac-tivation levels. VIGA incorporates View-Conditioned Low-Rank Adap-tation to dynamically modulate feed-forward weights using view-awarelow-rank residuals, thereby alleviating view-dependent geometric map-ping conflicts without substantial parameter overhead. Simultaneously,we introduce Identity-Guided Sparsification, which employs a soft mask-ing mechanism guided by identity semantics to suppress irrelevant back-ground noise while preserving the 2D spatial topology. Extensive evalua-tions on the LAGPeR and AG-ReID.v2 datasets demonstrate that VIGAachieves state-of-the-art performance, particularly in challenging cross-platform retrieval scenarios, by effectively learning robust, view-invariantrepresentations.