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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

SHIFT: Motion Alignment in Video Diffusion Models with Adversarial Hybrid Fine-Tuning

Xi Ye, Wenjia Yang, Yangyang Xu, Xiaoyang Liu, Duo Su, Mengfei Xia, Jun Zhu

Image-conditioned video diffusion models achieve impressivevisual realism but often suffer from weakened motion fidelity, e.g., re-duced motion dynamics or degraded long-term temporal coherence, es-pecially after fine-tuning. We study motion alignment in video diffusionmodels post-training. To address this, we introduce pixel-motion rewardsbased on pixel flux dynamics, capturing both instantaneous and long-term motion consistency. We further propose Smooth Hybrid Fine-tuning(SHIFT), a scalable reward-driven framework that unifies supervisedfine-tuning and advantage-weighted fine-tuning. Benefiting from noveladversarial advantages, SHIFT improves convergence speed and miti-gates reward hacking. Experiments show that our approach efficientlyresolves dynamic-degree collapse in modern video diffusion models su-pervised fine-tuning. Project page: https://xiye20.github.io/projects/SHIFT/.

ECCV 2026

Scalable Cross-embodiment Dexterous Grasping via Morphology-Prior Diffusion

Sihang Li, Zheming Zhou, Marcelino Almeida, Omid Alizadeh, Luca Carlone, Min Sun, Chen Feng, Cheng-Hao Kuo

This paper presents SOMO, a scalable framework for crossembodiment grasp synthesis that transfers to novel robot hands using only their hand description (i.e., a Unified Robot Description Format (URDF) file), without requiring any hand–object interaction annotations. Unlike prior approaches that rely on hand-specific models or annotated grasp data for each embodiment, SOMO introduces a shared Morphology-Prior Diffusion model applicable across heterogeneous hands through three key designs. First, grasping is formulated as a 3D assembly problem by predicting per-link SE(3) poses, enabling geometry-driven generation independent of hand-specific kinematics, with feasibility enforced through a post joint optimization stage. Second, a classifier-free training strategy learns a morphology prior from physically valid hand configurations generated through forward-kinematic exploration without object–grasp annotations, enabling grasp synthesis for unseen robot hands given only their URDF files. Third, a 3D shape-aware VAE encodes link and object geometry into a shared embedding, enabling consistent reasoning about hand–object complementarity across embodiments. Experiments show that SOMO achieves state-of-the-art grasp synthesis across six robot hands and demonstrates strong annotation-free generalization to previously unseen hands. Code and models will be released soon.

ECCV 2026

Rotate Your Character: Revisiting Video Diffusion Models for High-Quality 3D Character Generation

Jin Wang, Jianxiang Lu, Comi Chen, Guangzheng Xu, Haoyu Yang, Peng Chen, na zhang, Yifan Xu, Longhuang Wu, Shuai Shao, Qinglin Lu, Ping Luo

Generating high-quality 3D characters from single images re-mains a significant challenge in digital content creation, particularly dueto complex body poses and self-occlusion. In this paper, we present RCM(Rotate your Character Model ), an advanced image-to-video diffusionframework tailored for high-quality novel view synthesis (NVS). Com-pared to existing diffusion-based approaches, RCM offers several keyadvantages: (1) transferring characters with any complex poses into acanonical pose, enabling consistent novel view synthesis across the entireviewing orbit, (2) high-resolution orbital video generation at 1024×1024resolution, (3) controllable observation positions given different initialcamera poses, and (4) multi-view conditioning supporting up to 4 inputimages, accommodating diverse user scenarios. Extensive experimentsdemonstrate that RCM outperforms state-of-the-art methods in novelview synthesis on character generations. The deliverables will be updatedhere.

ECCV 2026

ReynoldsFlow: Physics-Inspired Spatiotemporal Flow Representation for Video Understanding

Yu-Hsi Chen, Ching-Kai Lin, PingKong Huang, Chin-Tien Wu

Video understanding has largely relied on deep spatiotem-poral architectures, including 3D convolutional networks and opticalflow (OF) based models. While effective, these methods are often com-putationally expensive and depend on heuristic motion representationsthat are sensitive to illumination, scale, and structural changes. To ad-dress these limitations, we propose ReynoldsFlow, a physics-inspiredrepresentation grounded in the Reynolds transport theorem (RTT) andHelmholtz-Hodge decomposition (HHD). ReynoldsFlow decomposes mo-tion into curl-free (CF) and divergence-free (DF) components, providinga principled and interpretable characterization of scene dynamics. Bycoupling intensity information with decomposed motion cues, it producesdynamics-aware, texture-preserving features that boost downstream taskssuch as pose estimation, action recognition, and tiny object detection.Lightweight and modular, ReynoldsFlow can be readily integrated intoexisting architectures. Experiments across diverse benchmarks show thatReynoldsFlow consistently matches or surpasses existing approaches, of-fering improved generalizability and computational efficiency.

ECCV 2026

REVEAL: Reasoning-Enhanced Forensic Evidence Analysis for Explainable AI-Generated Image Detection

Huangsen Cao, Qin Mei, Zhiheng Li, Yuxi Li, Zhan Meng, Ying Zhang, Chen Li, Zhimeng Zhang, Xin Ding, Yongwei Wang, Jing Lyu, Fei Wu

The rapid progress of visual generative models has madeAI-generated images increasingly difficult to distinguish from authen-tic ones, posing growing risks to social trust and information integrity.This motivates detectors that are not only accurate but also forensicallyexplainable. While recent multimodal approaches improve interpretabil-ity, many rely on post-hoc rationalizations or coarse visual cues, withoutconstructing verifiable chains of evidence, thus often leading to poor gen-eralization. We introduce REVEAL-Bench, a reasoning-enhanced multi-modal benchmark for AI-generated image forensics, structured aroundexplicit chains of forensic evidence derived from lightweight expert mod-els and consolidated into step-by-step chain-of-evidence traces. Basedon this benchmark, we propose REVEAL (Reasoning-enhanced Foren-sic Evidence Analysis), an explainable forensic framework trained withexpert-grounded reinforcement learning. Our reward design jointly pro-motes detection accuracy, evidence-grounded reasoning stability, and ex-planation faithfulness. Extensive experiments demonstrate significantlyimproved cross-domain generalization and more faithful explanations tobaseline detectors. All data and source codes are publicly available at:https://github.com/TrustMedia-zju/REVEAL.

ECCV 2026

Read or Ignore? A Unified Benchmark for Typographic-Attack Robustness and Text Recognition in Vision-Language Models

Futa Waseda, Shojiro Yamabe, Daiki Shiono, Kento Sasaki, Tsubasa Takahashi

Large vision-language models (LVLMs) are vulnerable to ty-pographic attacks, where misleading text inserted into an image can over-ride visual understanding. However, existing evaluation protocols anddefenses are largely focused on object recognition and do not considertext-reading capability. This is a critical oversight: real-world scenariosoften require both recognizing objects and reading scene text (e.g., rec-ognizing pedestrians while reading tra!c signs), where simply ignoringall text for robustness is unacceptable in practice. To address this gap,we introduce a novel task, Read-or-Ignore VQA (RIO-VQA), whichjointly evaluates both requirements: models must decide, from context,when to read scene-text and when to ignore inserted distractor text. Toevaluate this capability, we present RIO-Bench, a same-scene counter-factual benchmark that holds the scene fixed while varying only questionintent (object vs. text) and text condition (clean vs. attack), enablingdirect comparisons of model behaviors with reduced confounding fac-tors. Using RIO-Bench, we highlight a trade-o": representative defensesdeveloped in object-centric settings can achieve robustness by suppress-ing text sensitivity, at the cost of text-reading performance (i.e., “ignor-ing” text). Motivated by this trade-o", we provide a data-driven defensebaseline that improves both requirements on RIO-Bench, complementingprior text-ignoring baselines. Overall, this work highlights a fundamen-tal misalignment between the current object-centric robustness scope andreal-world multimodal requirements, providing a principled path towardreliable LVLMs.

ECCV 2026

Puppet-CNN: Continuous Parameter Dynamics for Input-Adaptive Convolutional Networks

Yucheng Xing, Xin Wang

Modern convolutional neural networks (CNNs) organize com-putation as a discrete stack of layers whose parameters are independentlystored and learned, with the number of layers fixed as an architecturalhyperparameter. In this work, we explore an alternative perspective: cannetwork parameterization itself be modeled as a continuous dynamicalsystem? We introduce Puppet-CNN, a framework that represents con-volutional layer parameters as states evolving along a learned parameterflow governed by a neural ordinary differential equation (ODE). Underthis formulation, layer parameters are generated through continuousevolution in parameter space, and the effective number of generatedlayers is determined by the discretization resolution used to sample thelearned parameter trajectory, which can be modulated by input com-plexity to enable input-adaptive computation. We validate its feasibilityon standard image classification benchmarks and show that continuousparameter dynamics can maintain competitive predictive performancewhile substantially reducing stored trainable parameters. These resultssuggest that viewing neural network parameterization through the lensof dynamical systems provides a structured and flexible design space foradaptive convolutional models.

ECCV 2026

ProtoFair: Fair Self-Supervised Contrastive Learning via Pseudo-Counterfactual Pairs

Marah Halawa, Olaf Hellwich

Self-supervised learning methods learn high-quality visualrepresentations, yet recent studies show that these representations of-ten capture demographic biases present in the training data. Existingfairness-aware methods address this by redesigning the self-supervisedobjective itself, limiting portability across the rapidly evolving land-scape of self-supervised learning (SSL) frameworks. We propose Proto-Fair, a fairness-aware contrastive loss designed to work alongside ex-isting SSL objectives without modifying them. ProtoFair leverages un-supervised prototype clustering to identify pseudo-counterfactual pairs:samples sharing the same cluster assignment but belonging to differentsensitive groups. By pulling these content-matched, cross-group samplestogether in the embedding space, ProtoFair encourages the encoder tolearn representations that are invariant to the sensitive attribute. Themethod requires only sensitive attribute annotations, no target labels,and integrates seamlessly with both SimCLR and SupCon. Experimentson CelebA and UTKFace demonstrate consistent fairness improvementswhile maintaining competitive accuracy.

ECCV 2026

Reinforcing Vision-Language Models for Image Quality Assessment with Grounding Process Rewards

Jingwei Liu, Hongyan Li, Bo Liu, Tianlin Zhang, Yifei Qian, Congyang Zhao, Junhong Liu, Yang Cai, Ling Yang

Image Quality Assessment (IQA) requires aligning modeloutputs with human perception. While Vision-Language Models (VLMs)show promise, current methods struggle with generalization due to alack of fine-grained reasoning alignment. Existing RL-based approachesprimarily rely on outcome rewards; however, their inherent sparsity oftenresults in accurate predictions based on erroneous logical attributions.This phenomenon creates a critical bottleneck that stifles the develop-ment of robust reasoning capabilities and limits performance gains inout-of-distribution scenarios. To address this problem, we propose RGPR,which utilizes grounding process rewards to orchestrate VLM trajectoriesalong a human-emulated reasoning chain. To maintain optimization sta-bility, we introduce step-wise reward normalization to preserve trainingsignal precision. RGPR achieves state-of-the-art results across three tasks,demonstrating superior reasoning fidelity and robust generalization.

ECCV 2026

Progression as Latent Drift: Generative Forecasting of Slow-Evolving Pathologies

Yuxiang Feng, Juncheng Wang, Chao Xu, Wenlong Hou, Huihan Wang, Yijie Qian, Yang Liu, Baigui Sun, Yong Liu, Shujun Wang

Forecasting the future anatomy of slow-evolving neurode-generative diseases could enable earlier, more targeted intervention andimprove clinical trial design, but it remains challenging because true pro-gression signals are subtle in longitudinal MRI. In this low-signal regime,transferring modern generative sequence models directly is unreliable:training is dominated by stable baseline anatomy and confounded bydense, sample-specific nuisance variation. We first provide a theoreticalanalysis that explains these failures through two modes. Identity col-lapse occurs when optimization is driven toward reproducing the currentanatomy, which prevents the model from learning faint temporal change.The continuous interpolation trap arises when standard smooth networkscannot separate localized biological drift from pervasive noise, whichleads to spurious changes that diffuse across the volume. To addressboth issues, we propose Latent Drift, a progressive generative frame-work that learns change in a compressed semantic representation ratherthan synthesizing full-resolution anatomy. This design removes pixel-levelidentity from the prediction target and concentrates model capacity onprogression-relevant dynamics. We further apply Finite Scalar Quan-tization to the learned change representation, which suppresses small,high-frequency nuisance fluctuations while preserving consistent struc-tural drift. Experiments on longitudinal 3D brain MRI show that LatentDrift improves patient-specific neuro-forecasting over diffusion and au-toregressive transformer baselines across generative fidelity and clinicallyrelevant evaluation metrics.

ECCV 2026

Pol-CACTI: A System and dataset forHigh-Speed Polarized Video Compressive Imaging

Yunfeng Song, Yidong Luo, Ping Wang, Xingjian Jiang, Xin Yuan

Capturing high-speed polarization dynamics is crucial forrevealing physical cues like stress variations and surface structures thatare invisible in conventional intensity imaging. While snapshot compres-sive imaging (SCI) enables high-speed video reconstruction from a singlemeasurement, extending it to division-of-focal-plane (DoFP) polarizationsensing introduces a fundamentally more ill-posed inverse problem due tothe coupling between temporal multiplexing and spatial polarization mo-saicing. To address this challenge, we propose Polarization Coded Aper-ture Compressive Temporal Imaging (Pol-CACTI), a hardware–softwareco-designed framework for polarized video SCI. First, we build an opticalsystem that encodes DoFP polarization dynamics into a single snapshotthrough spatiotemporal modulation and integration. Second, we con-struct a large-scale high-quality polarized video dataset with carefullydenoised Stokes-domain supervision, enabling supervised learning of po-larization video SCI reconstruction models. Third, we develop a physics-driven reconstruction network that jointly performs temporal recoveryand polarization demosaicing, where the Polarization-Aware LearnableBack-Projection (PA-LBP) module is proposed to enforce measurementconsistency with the raw DoFP mosaic and mitigates error accumula-tion caused by decoupled reconstruction pipelines. Extensive experimentsdemonstrate that our method significantly improves both intensity fi-delity and polarization estimation accuracy. The dataset and code arereleased to https://github.com/kaeoqxhailesxya/Pol-CACTI.

ECCV 2026

PASTEL: Panoramic Alignment for Monocular 4D Scene Reconstruction

Yuankun Yang, Yi Wei, Bo Bai, Wenyang Zhou, Li Zhang

Reconstructing 4D scenes from casually captured monocularvideo is vital for applications in virtual reality (VR) and embodied AI.Recent advances in 4D reconstruction and novel view synthesis havesubstantially propelled this capability. However, existing reconstructionmethods generally cannot recover regions beyond visible camera limits.Consequently, we introduce a new paradigm that achieves 4D scene syn-thesis by combining visible-region reconstruction from monocular inputwith invisible-region generation beyond observable camera boundaries.A straightforward approach is to leverage video generation models as“generative priors” for invisible-region exploration. However, their inherentstochasticity and large solution space prevent stable and consistent viewsynthesis. As a result, naively incorporating generative content into thereconstruction process often causes artifacts, especially when camera tra-jectories deviate significantly from the original video. To overcome thesechallenges, we present Panoramic Alignment for Strategic Exploitation ofGenerative Priors (PASTEL). Specifically, PASTEL proposes panoramicscene alignment, a novel representation that reformulates the intractable3D "invisible region” exploration into a tractable 2D directional trajectoryplanning. This is achieved by reducing the viewpoint planning from 6-DoFsearch to a 2D directional search with explicit visibility boundaries. Byoperating within this panoramic space, our method strategically iden-tifies camera trajectories that maximize exploration beyond observableboundaries while minimizing viewpoint deviation. Experimental resultsshow that PASTEL can not only extrapolate plausible scene contentbeyond the observable boundaries of input monocular videos, but alsosubstantially boost monocular 4D reconstruction performance. PASTELoutperforms the previous state-of-the-art method by 0.9dB in full-imagePSNR on the DyCheck IPhone dataset.

ECCV 2026

Online Reasoning Video Object Segmentation

Jinyuan Liu, Yang Wang, Zeyu Zhao, Weixin Li, Song Wang, Ruize Han

Reasoning video object segmentation predicts pixel-level masksin videos from natural-language queries that may involve implicit andtemporally grounded references. However, existing methods are devel-oped and evaluated in an offline regime, where the entire video is availableat inference time and future frames can be exploited for retrospective dis-ambiguation, deviating from real-world deployments that require strictlycausal, frame-by-frame decisions. We study Online Reasoning Video Ob-ject Segmentation (ORVOS), where models must incrementally interpretqueries using only past and current frames without revisiting previouspredictions, while handling referent shifts as events unfold. To supportevaluation, we introduce ORVOSB, a benchmark with frame-level causalannotations and referent-shift labels, comprising 210 videos, 12,907 anno-tated frames, and 512 queries across five reasoning categories. We furtherpropose a baseline with continually-updated segmentation prompts anda structured temporal token reservoir for long-horizon reasoning underbounded computation. Experiments show that existing methods struggleunder strict causality and referent shifts, while our baseline establishesa strong foundation for future research.

ECCV 2026

On-Policy Diffusion Reinforcement Learning Meets Off-Policy Quality Anchoring

Zunxu Liu, Zhaofan Qiu, Yazhen Xie, Yingwei Pan, Ting Yao, Tao Mei

Reinforcement learning for diffusion models primarily relieson on-policy learning to optimize the model by using samples from itsown current policy. Despite offering a direct optimization signal, theapproach is fundamentally self-limiting and prone to reward overfittingand mode collapse due to its myopic guidance. In this paper, we pro-pose a new framework, namely DiffusionCompass, that breaks such lim-itation by strategically integrating off-policy guidance. DiffusionCom-pass establishes a dual learning objective: the model continues to ex-plore reward maximization on-policy, while simultaneously being an-chored by off-policy examples that define a stable quality benchmark.That is achieved through a novel integration of several key techniques:an off-policy objective with distribution-aware filtering to adaptively em-phasize relevant anchors, a perceptual anchored reward mechanism tomitigate reward hacking, and a reward renormalization strategy usingoff-policy samples for a stable quality baseline. The dual regimes com-plement each other: off-policy guidance prevents reward overfitting andcollapse, and on-policy exploration ensures the model does not merelyimitate a static dataset. Experiments across text-to-image/video gener-ation demonstrate that DiffusionCompass achieves superior reward per-formance while maintaining high sample diversity, shaping a robust andeffective path for fine-tuning generative models. Code is available athttps://github.com/HiDream-ai/DiffusionCompass.

ECCV 2026

OmniForcing: Unleashing Real-time Joint Audio-Visual Generation

Yaofeng Su, Yuming Li, Zeyue Xue, Jie Huang, Siming Fu, Haoran Li, Haoyang Huang, Nan Duan

Recent joint audio-visual diffusion models achieve remark-able generation quality but suffer from high latency due to their bidirec-tional attention dependencies, hindering real-time applications. We pro-pose OmniForcing, the first framework to distill an offline, dual-streambidirectional diffusion model into a high-fidelity streaming autoregressivegenerator. However, naively applying causal distillation to such dual-stream architectures triggers severe training instability, due to the ex-treme temporal asymmetry between modalities and the resulting tokensparsity. We address the inherent information density gap by introducingan Asymmetric Block-Causal Alignment with a zero-truncation GlobalPrefix that prevents multi-modal synchronization drift. The gradient ex-plosion caused by extreme audio token sparsity during the causal shift isfurther resolved through an Audio Sink Token mechanism equipped withan Identity RoPE constraint. Finally, a Joint Self-Forcing Distillationparadigm enables the model to dynamically self-correct cumulative cross-modal errors from exposure bias during long rollouts. Empowered by amodality-independent rolling KV-cache inference scheme, OmniForcingachieves state-of-the-art streaming generation at ∼25 FPS on a singleGPU, maintaining multi-modal synchronization and visual quality on parwith the bidirectional teacher. Project Page: https://omniforcing.com.

ECCV 2026

MVGS: Multi-view Regulated Gaussian Splatting for Novel View Synthesis

Xiaobiao Du, Yida Wang, Xin Yu

Recent works in novel view synthesis, e.g., Neural RadianceField (NeRF) and 3D Gaussian Splatting (3DGS), have significantly ad-vanced rendering quality and efficiency. However, existing Gaussian-basednovel view synthesis methods typically follow a single-view optimizationparadigm. We observed that this optimization paradigm suffers fromunstable gradients, leading to suboptimal rendering quality. To tackle thisissue, we present a novel multi-view regulated Gaussian Splatting (MVGS)that fully leverages a multi-view coherent (MVC) constraint throughoutthe optimization process. Specifically, our proposed MVC enhances 3DGaussian multi-view consistency and thus ensures smoother gradient up-dates. Furthermore, since single-scale training usually leads to suboptimalsolutions, we propose a cross-intrinsic guidance scheme in a coarse-to-fine manner to improve the convergence of multi-view optimization in3DGS. In particular, by incorporating more multi-view images at the lowresolution, we can optimize 3D Gaussians with more comprehensive per-spectives. Then, finer-scale Gaussians are initialized by coarsely estimatedones instead of optimizing full-scale 3D Gaussians from scratch. Moreover,we found that 3D Gaussians usually struggle to fit 2D training views withminimal overlap. Thus, we propose a novel multi-view cross-ray densifica-tion strategy, where 3D Gaussians are dynamically split to accommodatedrastic viewpoint variations in the multi-view optimization process. Inthis way, the multi-view consistency can be further improved. Notably,our proposed MVGS method is a plug-and-play optimizer. Extensiveexperiments across various tasks demonstrate that our proposed MVGSimproves existing Gaussian-based methods and achieves state-of-the-artperformance. Project Page: https://xiaobiaodu.github.io/mvgs-project/

ECCV 2026

Multi-scale Mixture of World Models for Embodied Agents in Evolving Environments

Jinwoo Jang, Daniel Rho, Sihyung Yoon, Hyunsuk Cho, Honguk Woo

Embodied agents operating in the real world require multi-scale reasoning and knowledge adaptation as conditions change. We iden-tify two challenges in applying Mixture of Experts (MoE) to this setting:routing lacks an explicit notion of scale, preventing targeted updates atspecific scales, and a uniform update policy cannot accommodate thedifferent rates at which knowledge at each scale becomes outdated. Wepresent MuSix, a framework that addresses both challenges throughscale-aware world model mixture and evolution. A two-stage routingmechanism first maps experiential distance, a measure of situational nov-elty inspired by Construal Level Theory, to a weight over continuous scalespace via a meta-router, then selects world models within the identifiedscale. For adaptation, scale-dependent forgetting rates allow low-scaleknowledge to refresh rapidly while high-scale abstractions persist, andgated inter-scale transfer maintains coherence across the hierarchy. Ex-periments on EmbodiedBench and HAZARD show that MuSix improvesover state-of-the-art baselines.

ECCV 2026

MGM-Omni: Scaling Omni LLMs to Personalized Long-Horizon Speech

Chengyao Wang, Zhisheng Zhong, Bohao PENG, Senqiao Yang, Yuqi Liu, Haokun GUI, Bin Xia, Jingyao Li, Bei Yu, Jiaya Jia

We present MGM-Omni, an Omni MLLM for omni-modalunderstanding and expressive, long-horizon speech generation. MGM-Omni adopts a “brain–mouth” design with a dual-track, token-based ar-chitecture that cleanly decouples multimodal reasoning from real-timespeech generation. This design enables efficient cross-modal interactionand low-latency, streaming speech generation. For multimodal under-standing, a unified training strategy coupled with a dual audio encoderdesign enables long-form omni modal perception across diverse acous-tic conditions. For speech generation, a chunk-based parallel decodingscheme narrows the text–speech token-rate gap, accelerating inferenceand supporting streaming zero-shot voice cloning with stable timbre overextended durations. Compared to concurrent work, MGM-Omni achievesthese capabilities with markedly data-efficient training. Extensive exper-iments demonstrate that MGM-Omni outperforms existing open sourcemodels in preserving timbre identity across extended sequences, produc-ing natural and context-aware speech, and achieving superior long-formaudio and omnimodal understanding. MGM-Omni establishes an effi-cient, end-to-end paradigm for omnimodal understanding and control-lable, personalized long-horizon speech generation. Code and models areavailable at https://github.com/dvlab-research/MGM-Omni.

ECCV 2026

Mapping the Concept Landscape: Structural Perception of Global Distributions for Transparent Data Pruning

Dongyue Wu, Tao MA

Existing data pruning methods predominantly rely on high-dimensional feature embeddings to measure sample importance. How-ever, these compressed vectors often obscure fine-grained semantic inter-actions, leading to suboptimal coverage of rare semantic concepts in thepruned subsets. In this paper, we propose Mapping the Concept Land-scape (MCL), a novel structural perception framework for transparentdata pruning. Instead of abstract embeddings, we represent each image-caption pair as an explicit sample-level graph comprising entities, events,and attributes. By integrating these individual graphs into a comprehen-sive dataset-level graph, we characterize the global distribution of seman-tic concepts and quantify their rarity across the entire corpus. Basedon this structured perception, we develop a greedy concept-coveragemaximization algorithm that iteratively selects samples to maximize themarginal gain of high-value, under-represented concepts. Experimentalresults on various benchmarks demonstrate that our method not onlyachieves superior pruning efficiency compared to state-of-the-art meth-ods but also provides a transparent and interpretable audit trail for theselection process.

ECCV 2026

LaGen: Towards Autoregressive LiDAR Scene Generation

Sizhuo Zhou, Xiaosong Jia, Fanrui Zhang, Junjie Li, Juyong Zhang, Yukang Feng, Jianwen Sun, Songbur Wong, Junqi You, Junchi Yan

Generative world models for autonomous driving (AD) are ofgreat value in applications such as data augmentation, closed-loop sim-ulation, and safety-critical scenario evaluation. Unlike the widely stud-ied image modality, in this work we explore generative world modelsfor LiDAR data. Existing generation methods for LiDAR predominantlyfocus on single frame generation or lack the capacity for interactive sim-ulation, while existing prediction approaches require multiple frames ofhistorical input and can only deterministically predict multiple framesat once. Both paradigms fail to support long-horizon interactive gen-eration. To this end, we introduce LaGen, which, to the best of ourknowledge is the first autoregressive framework capable of generatinglong-horizon LiDAR scenes in a frame-by-frame, interactive manner. La-Gen is able to take a single-frame input as a starting point and effectivelyutilize bounding box information as conditions to generate high-fidelity4D scene. In addition, we introduce a scene decoupling estimation moduleto enhance the model’s interactive generation capability for object-levelcontent, as well as a noise modulation module to mitigate error accumu-lation during long-horizon generation. We extensively evaluate LaGen’sperformance in controlled data generation and long-horizon scene genera-tion on the nuScenes dataset. The experimental results demonstrate thatLaGen achieves state-of-the-art performance, especially on later frames.The code is publicly available at: https://github.com/szzhou88/LaGen.

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