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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

UniGP: Taming Diffusion Transformer for Prior-Preserved Unified Generation and Perception

Qin Guo, Hao Luo, Dongxu Yue, Weixuan Jin, Xiao Fu, Fan Wang, Dan Xu

Recent advances in diffusion models have shown impressiveperformance in controllable image generation and dense prediction tasks.However, existing approaches typically treat diffusion-based controllablegeneration and dense prediction as separate tasks, overlooking the po-tential benefits of jointly modeling the heterogeneous distributions. Inthis work, we introduce UniGP, a framework built upon MMDiT, whichunifies controllable generation and dense prediction through simple jointtraining, without the need for complex task-specific designs or losses,while preserving the backbone’s versatile priors. By learning controllablegeneration and prediction under different conditions, our model effectivelycaptures the joint distribution of image-geometry pairs. UniGP is capableof versatile controllable generation, dense prediction, and joint generation.Specifically, the proposed UniGP consists of DUGP and a unified datasettraining strategy. The former, following the principle of Occam’s razor,uses only a copied image branch of MMDiT to model dense distributionsbeyond RGB, while the latter integrates heterogeneous datasets into aunified training framework to jointly model generation and perceptiontasks. Extensive experiments demonstrate that our unified model sur-passes prior unified approaches and performs on par with specializedmethods. Furthermore, we demonstrate that multi-task joint training pro-vides complementary benefits: generative priors enrich perceptual details,while perceptual learning improves structural alignment in generation.Project page: guoqincode.github.io/UniGP.

ECCV 2026

Syn-GRPO: Self-Evolving Data Synthesis for MLLM Perception Reasoning

Qihan Huang, Haofei Zhang, Rong Wei, Yi Wang, Rui Tang, Mingli Song, Jie Song

RL (reinforcement learning) methods (e.g., GRPO) for mul-timodal LLM perception ability has attracted wide research interest ow-ing to its remarkable generalization ability. Nevertheless, existing rein-forcement learning methods still face the problem of low data quality,where data samples cannot elicit diverse responses from MLLMs, thus re-stricting the exploration scope for MLLM reinforcement learning. Somemethods attempt to mitigate this problem by imposing constraints onentropy, but none address it at its root. Therefore, to tackle this problem,this work proposes Syn-GRPO (Synthesis-GRPO), which employs anonline data generator to synthesize high-quality training data with di-verse responses in GRPO training. Specifically, Syn-GRPO consists oftwo components: (1) data server; (2) GRPO workflow. The data serversynthesizes new samples from existing ones using an image generationmodel, featuring a decoupled and asynchronous scheme to achieve highgeneration efficiency. The GRPO workflow provides the data server withthe new image descriptions, and it leverages a diversity reward to su-pervise the MLLM to predict image descriptions for synthesizing sam-ples with diverse responses. Experiment results across three visual per-ception tasks demonstrate that Syn-GRPO improves the data qualityby a large margin, achieving significant superior performance to exist-ing MLLM perception methods, and Syn-GRPO presents promising po-tential for scaling long-term self-evolving RL. Our code is available athttps: // github. com/ hqhQAQ/ Syn-GRPO .

ECCV 2026

StarDojo: Benchmarking Open-Ended Behaviors of Agentic Multimodal LLMs in Production–Living Simulations with Stardew Valley

Weihao Tan, Changjiu Jiang, Yu Duan, Mingcong Lei, Li JiaGeng, Yitian Hong, Xinrun Wang, Bo An

Autonomous agents navigating human society must masterboth production activities and social interactions, yet existing bench-marks rarely evaluate these skills simultaneously. To bridge this gap,we introduce StarDojo, a novel benchmark based on Stardew Valley,designed to assess AI agents in open-ended production–living simulations.In StarDojo, agents are tasked to perform essential livelihood activitiessuch as farming and crafting, while simultaneously engaging in socialinteractions to establish relationships within a vibrant community. Star-Dojo features 1,000 meticulously curated tasks across five key domains:farming, crafting, exploration, combat, and social interactions. Addition-ally, we provide a compact subset of 100 representative tasks for efficientmodel evaluation. The benchmark offers a unified, user-friendly interfacethat eliminates the need for keyboard and mouse control, supports allmajor operating systems, and enables the parallel execution of multipleenvironment instances, making it particularly well-suited for evaluatingthe most capable foundation agents, powered by multimodal large lan-guage models (MLLMs). Extensive evaluations of state-of-the-art MLLMsagents demonstrate substantial limitations, with the best-performingmodel, GPT-4.1, achieving only a 12.7% success rate, primarily due tochallenges in visual understanding, multimodal reasoning and low-levelmanipulation. As a user-friendly environment and benchmark, StarDojoaims to facilitate further research towards robust, open-ended agents incomplex production-living environments.

ECCV 2026

Music-to-Dance Generation via Atomic Movements

Xinhao Cai, Yixuan Sun, Minghang Zheng, Qingchao Chen, Xin Jin, Song-Chun Zhu, Yang Liu

Music-driven dance generation aims to produce human mo-tion that is both rhythmically synchronized and semantically consis-tent with music. While recent neural approaches have achieved impres-sive visual realism, they typically model motion as a continuous signaland neglect its compositional nature, making generated dances struc-turally incoherent and difficult to control. In this work, we introducea structure-aware framework that models choreography as a sequenceof atomic movements—semantically interpretable motion events thatserve as the building blocks of dance. To construct this atomic move-ment vocabulary, we first segment large-scale dance data and clusterthem into atomic movement groups. We then employ a large languagemodel to semantically relabel and refine the clusters, yielding a set ofinterpretable and reusable atomic movements. Based on these atomicmovement annotations, we design a two-stage generation framework thatmirrors the human choreography process. In the atomic movement plan-ning stage, the model predicts the type, duration, and timing of atomicmovements conditioned on the input music, forming a symbolic danceallocation. In the completion stage, a transition-aware generator syn-thesizes smooth and stylistically coherent motion conditioned on theplanned structure. Extensive experiments demonstrate that our methodproduces dances with significantly improved structural coherence, rhyth-mic alignment, and perceptual naturalness compared to existing base-lines, while providing enhanced interpretability and controllable edit-ing through explicit structural representation. The code is available at(A) Real music and choreography exhibits highly structural formation.Repeated patterns Repeated patternsSimilar or symmetrical atomic movements(B) Existing end-to-end works overlook such structural organization.Fig. 1: (A) There exists explicit repeated patterns in music and choreography. (B)Existing methods, usually in an end-to-end form, treat music-to-dance generation as asimple sequence-to-sequence task, neglecting the inner structure.

ECCV 2026

FST-SAM3: Taming SAM~3 with Frequency-Spatio-Temporal Refinement for Video Polyp Segmentation

Guanhao Wu, Guilian Chen, Huisi Wu, Jing Qin

Video polyp segmentation (VPS) is important for automatedcolorectal cancer screening, yet it is a very challenging task due to severecamouflage in mucosal scenes, complex intestinal anatomy, and substan-tial frame-to-frame appearance variations. Although Segment AnythingModel 3 (SAM 3) is able to provide strong representations for segmen-tation tasks, directly applying it to endoscopic videos usually cannotachieve satisfactory performance owing to domain shifts, boundary am-biguities, and unstable temporal propagation. To address these chal-lenges, we propose FST-SAM3, a novel parameter-efficient adaptationframework built on SAM 3 for VPS. FST-SAM3 introduces two inno-vative yet complementary components. First, we propose a Structure-Aware Frequency Enhancement (SAFE) module to strengthen structure-and boundary-sensitive cues under endoscopic appearance. Second, wedevelop a Spatio-Temporal Token Refinement Framework (STRF) torefine temporal information at the token level for more stable cross-frame segmentation. We freeze the SAM 3 backbone and learn only asmall set of additional parameters, keeping the inference characteristicsclose to SAM 3 while improving segmentation performance. We con-ducted extensive experiments on two benchmarking datasets (SUN-SEGand CVC-612) under standardized fully automatic (0-point) and first-frame prompted (1-point at t=0) protocols. Results demonstrate that ourmodel achieves consistent improvements over state-of-the-art approaches,including both task-specific baselines and representative SAM-based ap-proaches. Code is available at https://github.com/GavonW/FST-SAM3.

ECCV 2026

FaCT-GS: Fast and Scalable CT Reconstruction with Gaussian Splatting

Pawel Pieta, Rasmus Juul Pedersen, Sina Borgi, Jakob S. Jørgensen, Jens Wenzel Andreasen, Vedrana Dahl

Gaussian Splatting (GS) has emerged as a dominating tech-nique for image rendering and has quickly been adapted for the X-ray Computed Tomography (CT) reconstruction task. However, despiteits growing popularity, the benefits of GS are typically not substantialenough to motivate a transition from well-established reconstruction al-gorithms. This paper addresses the most significant remaining limita-tions of the GS-based approach by introducing FaCT-GS, a frameworkfor fast and flexible CT reconstruction. Enabled by an in-depth optimiza-tion of the voxelization and rasterization pipelines, our new method issignificantly faster than its predecessors and scales well with projectionand output volume size. Furthermore, the improved voxelization enablesrapid fitting of Gaussians to pre-existing volumes, which can serve as aprior for warm-starting the reconstruction, or simply as an alternative,compressed representation. FaCT-GS is over 4× faster than the State ofthe Art GS CT reconstruction on standard 5122 projections, and over13× faster on 2k projections. Implementation and data available through:https://papieta.github.io/fact-gs/.

ECCV 2026

MMAgent-R2: Learning to Rerank and Reject for Agentic mRAG

Tao Zhang, Ziqi Zhang, Zongyang Ma, Yuxin Yang, Bing Li, Chunfeng Yuan, Kang Rong, Fengyun Rao, Jing Lyu, Weiming Hu

Knowledge-based Visual Question Answering (KB-VQA) re-quires models to retrieve visual entities matching the query image fromlarge-scale encyclopedic knowledge bases and answer related questions.Existing multimodal Retrieval Augmented Generation (mRAG) meth-ods rely on global visual features to match candidate entities, yet whenthe knowledge base contains numerous visually similar entities, the re-triever struggles to distinguish them, populating the candidate set withvisually similar but factually mismatched distractors. Since subsequentprocessing steps such as noise filtering are also confined to this fixedcandidate set, errors from failed retrieval inevitably propagate to the fi-nal answer. To address these challenges, we propose MMAgent-R2 , anagentic mRAG framework that integrates visual reranking and active re-jection as its internal verification mechanism. Visual reranking directlycompares query and candidate images, capturing discriminative detailsbeyond textual descriptions to precisely identify the target entity amongsimilar candidates; active rejection discards unreliable results and re-trieves additional candidates when no confident match is found, movingbeyond the fixed candidate pool. We design a composite reward functionwith step-level verification rewards and achieve joint optimization of ex-ternal retrieval, internal verification, and answer generation via GRPOtraining. Experiments on InfoSeek, E-VQA, and MMhops demonstratethat MMAgent-R2 achieves state-of-the-art performance, with particu-larly notable advantages in challenging retrieval scenarios and complexmulti-image multi-hop reasoning tasks.

ECCV 2026

Frequency Director: Learnable Mixture of Frequency Experts for Unified Concealed Scene Segmentation

Guangqian Guo, Aixi Ren, Xuehui Yu, Pengxu Wei, Yong Guo, Shan Gao

Concealed object segmentation aims to segment objects thatblend into their surroundings, presenting significant challenges due tothe high similarity between objects and backgrounds. Existing methodsrely on task-specific designs and independent training, leading to limitedgeneralization and structural redundancy. Although a unified parameter-shared model for diverse concealed scenarios is highly desirable andpromising, it is hindered by two challenges: large representation gapsacross scenarios and the intrinsic difficulty of concealed targets. From afrequency-domain perspective, we observe that different concealed sce-narios exhibit more discriminative and interpretable spectral character-istics compared to those in the RGB domain. These findings motivate usto approach unified concealed scene segmentation from the perspectiveof frequency modulation. To this end, we propose Frequency Director(FreqDirect), a dynamic frequency adaptation framework that directsspectral representations across diverse concealed scenes. It features amixture of frequency experts to perform adaptive routing over frequencycomponents, capturing heterogeneous task-specific patterns, along witha spatial commonality anchor to complement the spatial and structurecues. These components collectively enable effective and efficient adap-tation to diverse concealed segmentation tasks within a single model.Extensive experiments on eight concealed scenarios demonstrate thatFreqDirect surpasses existing unified models and achieves performancecomparable to, or better than, specialist models.

ECCV 2026

Different Changes Require Different Reasoning: Change-Type-Specialized Experts for Robust Change Captioning

Jiyoung Park, InJae Oh, Jung Uk Kim

Change captioning is the task of generating natural languagedescriptions that explain the changes between a pair of images. Althoughdifferent change types (e.g., color shifts, object additions) exhibit distinctvisual cues and require specialized reasoning processes, existing methodsoften overlook these distinctions. To address this limitation, we proposeMulti-Expert Diagnosis for Image Change (MEDIC), a novel frameworkthat introduces change-type awareness by explicitly modeling changecategories. We build our MEDIC as a memory network to dynamicallyretrieve type-relevant visual patterns conditioned on the input. This de-sign allows each expert to flexibly capture diverse variations within eachchange type and focus on the most informative cues for its designatedchange type. By routing inputs through type-specialized experts andlearning dedicated representations for each change category, MEDICgenerates more precise and type-aware change descriptions. Extensiveexperiments demonstrate that proposed MEDIC consistently outper-forms across diverse and challenging datasets. The code is available athttps://github.com/VisualAIKHU/MEDIC.

ECCV 2026

Dense Reward for Multi-View 3D Reasoning with Global Maps and Local Views

Jiho Choi, Seonho Lee, Seojeong Park, Hyunjung Shim

Multi-view 3D Visual Question Answering (MV3D-VQA) re-quires integrating partial observations into a coherent 3D scene repre-sentation and selecting informative viewpoints for multi-step spatial rea-soning. However, current multimodal LLMs are typically trained withsparse, answer-level supervision, which often yields inconsistent cross-view reasoning and brittle view selection. We present DR-MV3D (DenseReward for MV3D-VQA), a map-grounded learning framework that pro-vides dense, verifiable rewards to supervise the reasoning process. Our ap-proach decomposes MV3D-VQA into (i) allocentric global map construc-tion, (ii) question-conditioned view-trajectory planning, and (iii) egocen-tric grounding for answer prediction. To make intermediate steps learn-able without manual annotations, we introduce two rewards: a global con-sistency reward that aligns the predicted map with geometry-consistentpseudo targets from frozen 3D vision foundation models (e.g., VGGT+ SAM3), and a local trajectory reward that supervises ordered view-point selection. We optimize the full pipeline with trajectory-level pol-icy optimization (GRPO). Experiments on MindCube, VSI-Bench, andBLINK (MV) show that DR-MV3D consistently improves over strongmulti-image baselines, supporting the effectiveness of process-level densesupervision for multi-view 3D reasoning.Code is available at: https://github.com/kaist-cvml/dr-mv3d

ECCV 2026

Why Linear Probing Works: Non-Vacuous Generalization Bounds via Effective Dimension

Dongxin Guo, Jikun Wu, SM Yiu

Linear probing on frozen features is the standard evalua-tion protocol for vision foundation models, yet classical theory for d-dimensional classifiers demands Ω(d) labeled samples, far more thanpractitioners actually need. We resolve this discrepancy with the first non-vacuous PAC-Bayes generalization bounds for linear probing on frozenvision foundation-model features. The key insight is that foundation-modelfeatures do not fill Rd : covariance spectra decay steeply, concentratingvariance in a subspace of effective dimension deff ≪ d. A data-dependentGaussian prior aligned to this spectral structure yields a certificate whosecomplexity is governed by deff and the classification margin γ rather thanthe ambient dimension d. We validate the theory on ImageNet across 12vision encoders spanning discriminative, language-supervised, generative,and predictive pretraining: the bound is non-vacuous for all discrimi-natively pretrained models and ranks among the tightest PAC-Bayescertificates reported for any vision model. Three empirical predictionsfollow: generative pretraining yields markedly less certifiable features thandiscriminative pretraining, deff alone ranks foundation models withoutany labels, and larger models tend to be more certifiable because scalesteepens spectral decay.

ECCV 2026

Video2Reaction: Mapping Video to Audience Reaction Distribution in the Wild

Trang Nguyen, Sidong Zhang, Shiv Shankar, Gauri Jagatap, Deepak Chandran, Andrea Fanelli, Madalina Fiterau

Understanding and forecasting audience reactions to videocontent are crucial for improving content creation, recommendation sys-tems, and media analysis. To enable audience reaction prediction andother content engagement applications, we introduce Video2Reaction,a multimodal dataset that maps short movie segments to a distributionof induced emotions of viewers in the wild, as expressed through socialmedia. Video2Reaction spans more than 10,000 videos and serves asa reliable benchmark as well as a training resource for audience reactionprediction. To enable cost-e!ective continuous annotations as reactionsmay change over time, we develop a two-stage multi-agent pipeline usingonly open-source LLMs, achieving 86% correctness under blind humanverification despite the inherently noisy and subjective nature of the task.We establish the first benchmark for video-to-reaction-distribution pre-diction in the wild and show that pretrained foundation video modelsfail in zero-shot settings, while finetuning transforms them into state-of-the-art predictors capable of modeling both full reaction distribu-tions and dominant responses from video alone. However, the task re-mains challenging: even the strongest methods achieve only 77% Top-3F1 in dominant reaction prediction (LLaVA-Next), highlighting a sub-stantial gap in modeling collective audience reaction. Dataset and codeare available at our project page: https://information-fusion-lab-umass.github.io/video2reaction-bench.github.io/.

ECCV 2026

Tuning Real-World Image Restoration at Inference: A Test-Time Scaling Paradigm for Flow Matching Models

Purui Bai, Junxian Duan, Pin Wang, Jinhua Hao, Ming Sun, Chao Zhou, Huaibo Huang

Although diffusion-based real-world image restoration (Real-IR) has achieved remarkable progress, efficiently leveraging ultra-large-scale pre-trained text-to-image (T2I) models and fully exploiting theirpotential remain significant challenges. To address this issue, we proposeResFlow-Tuner, an image restoration framework based on the state-of-the-art flow matching model, FLUX.1-dev, which integrates unifiedmulti-modal fusion (UMMF) with test-time scaling (TTS) to achieveunprecedented restoration performance. Our approach fully leveragesthe advantages of the Multi-Modal Diffusion Transformer (MM-DiT) ar-chitecture by encoding multi-modal conditions into a unified sequencethat guides the synthesis of high-quality images. Furthermore, we in-troduce a training-free test-time scaling paradigm tailored for imagerestoration. During inference, this technique dynamically steers the de-noising direction through feedback from a reward model (RM), therebyachieving significant performance gains with controllable computationaloverhead. Extensive experiments demonstrate that our method achievesstate-of-the-art performance across multiple standard benchmarks. Thiswork not only validates the powerful capabilities of the flow match-ing model in low-level vision tasks but, more importantly, proposes anovel and efficient inference-time scaling paradigm suitable for largepre-trained models. The source code is publicly available at https://github.com/Rorschach-1010/ResFlow-Tuner.

ECCV 2026

TR-MoE: Temporal Reliability-Aware Mixture-of-Experts for Robust Tracking

Tianle Wang, Xiangyang Yang, Jihua Zhu, Binrui Liu, Yanzhao Li, Shuiwang Li

In visual object tracking, SAM2 stands out in deformation adaptability and distinguishing similar objects due to its pixel-level masks, whereas traditional discriminative trackers have advantages under occlusion and motion blur thanks to global semantic feature matching. To leverage this complementarity, we propose TR-MoE, a Temporal Reliability-aware Mixture-of-Experts framework. Unlike conventional fusion strategies that rely on static or per-frame confidence, we reframe tracker fusion as a time-series modeling problem. Specifically, we introduce a Mamba-based Trajectory Refiner (MTR) that processes the historical sequence of tracker states—including relative deviations and consistency scores—to capture the temporal evolution of error regimes. This enables the model to adaptively allocate fusion weights among experts based on historical temporal context. Furthermore, to achieve precise geometric correction, we design a KAN-enhanced Disentangled Head. Leveraging Kolmogorov–Arnold Networks, this module factorizes the refinement into independent directional offsets, dynamic scale magnitudes, and fusion weights. Extensive experiments show that TR-MoE effectively adapts to varying failure patterns, significantly boosting robustness and establishing new state-of-the-art performance. The source code is available at https://github.com/Avant1v/TR-MoE.

ECCV 2026

PPTArena: A Benchmark for PowerPoint Editing

Michael Ofengenden, Yunze Man, Ziqi Pang, Liang-Yan Gui, Yu-Xiong Wang

We introduce PPTArena, a benchmark for PowerPoint edit-ing that evaluates how agents modify real slides from natural-languageinstructions. Unlike benchmarks that rely on image-PDF renderings ortext-to-slide generation, PPTArena features 100 decks with over 1,300human-curated edits across 2,125 slides, spanning text, charts, anima-tions, and professional master styles. Each edit pairs a ground-truthdeck with a target rubric and is scored by two Vision-Language Model(VLM) judges: one rates instruction following from structural diffs, theother visual quality from slide images. On top of this benchmark, wepresent PPTPilot, a structure-aware agent that plans semantic edit se-quences, routes between programmatic tools and deterministic XMLoperations, and verifies each result in an iterative plan-edit-check loop.PPTPilot outperforms strong VLM-based agents by more than 10 per-centage points on compound, layout-sensitive, and cross-slide edits, withlarge gains in visual fidelity and deck-wide consistency. Despite this, allagents still struggle on long-horizon, document-scale tasks, underscoringhow hard reliable PowerPoint editing remains. We publicly release ourcode: https://github.com/michaelofengend/PPTArena.

ECCV 2026

Human Mesh Modeling for Anny Body

Romain Brégier, Guénolé Fiche*, Matthieu Armando, Laura Bravo-Sánchez, Thomas Lucas, Philippe Weinzaepfel, Gregory Rogez, Fabien Baradel

Parametric body models provide the structural basis formany human-centric tasks, yet existing models often rely on costly 3Dscans and learned shape spaces that are proprietary and demographi-cally narrow. We introduce Anny, a simple, fully differentiable, and scan-free human body model grounded in anthropometric knowledge from theMakeHuman community. Anny defines a continuous, interpretable shapespace, where phenotype parameters (e.g. gender, age, height, weight)control blendshapes spanning a wide range of human forms—across ages(from infants to elders), body types, and proportions. Calibrated usingWHO population statistics, Anny provides realistic and demographicallygrounded human shape variation within a single unified model. We re-lease the Anny body model and its code under the Apache 2.0 license.Thanks to its openness and semantic control, Anny serves as a versa-tile foundation for 3D human modeling—supporting millimeter-accuratescan fitting, controlled synthetic data generation, and Human Mesh Re-covery (HMR). We further introduce Anny-One, a collection of 780kphotorealistic images generated with Anny, showing that despite its sim-plicity, HMR models trained with Anny can match the performance ofthose trained with scan-based body models.

ECCV 2026

AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation

Yuchao Gu, Guian Fang, Yuxin Jiang, Weijia Mao, Song Han, Han Cai, Mike Zheng Shou

Few-step video generation has been significantly advancedby consistency distillation. However, the performance of consistency-distilled models often degrades as more sampling steps are allocatedat test time, limiting their effectiveness for any-step video diffusion.We argue that this limitation arises because consistency distillation re-places the original probability-flow ODE trajectory with a consistency-sampling trajectory, weakening the desirable test-time scaling behaviorof ODE sampling. To address this limitation, we introduce AnyFlow,the first any-step video diffusion distillation framework based on flowmaps. Instead of distilling a model for only a few fixed sampling steps,AnyFlow optimizes the full ODE sampling trajectory. To this end, weshift the distillation target from endpoint consistency mapping (zt → z0 )to flow-map transition learning (zt → zr ) over arbitrary time inter-vals. We further propose Flow Map Backward Simulation, whichdecomposes a full Euler rollout into shortcut flow-map transitions, en-abling efficient on-policy distillation that reduces test-time errors (i.e.,discretization error in few-step sampling and exposure bias in causalgeneration). Extensive experiments across both bidirectional and causalarchitectures, at scales ranging from 1.3B to 14B parameters, demon-strate that AnyFlow achieves performance comparable to or better thanconsistency-based counterparts in the few-step regime, while support-ing flexible and scalable sampling under varying step budgets. Code isreleased at https://github.com/NVLabs/AnyFlow.

ECCV 2026

ReShift: Aha-Moment-Driven Reasoning-Level Backdoor Attacks on Vision–Language Models

Zhihao Dou, Qinjian Zhao, Zhiqiang Gao, Sumon Biswas

Vision–Language Models (VLMs) are increasingly deployedin safety-critical applications, yet remain vulnerable to backdoor attacks.Existing methods primarily manipulate final outputs, often producingreasoning traces that are inconsistent or easily detectable. In this paper,we propose ReShift, the novel aha-moment-driven reasoning-level back-door framework that explicitly redirects the internal chain-of-thought(CoT) trajectory while preserving surface-level coherence. ReShift intro-duces a Poisoned Reasoning-Aware Data Construction (PRDC) pipelineand a Supervised–Reinforcement Joint Optimization (SRJO) strategyto induce stable trigger-conditioned reasoning shifts. We further for-malize Entropy Rebound as a principled signal for characterizing rea-soning redirection and provide theoretical guarantees linking entropygaps to trajectory-level divergence. Extensive experiments demonstratethat ReShift achieves high attack success rates while maintaining clean-task performance and realistic reasoning traces, substantially improv-ing stealthiness against existing defenses. Code can be found at https://github.com/AlbertZhaoCA/ReShift.

ECCV 2026

RainODE: Continuous-Time Precipitation Forecasting with Latent Neural ODEs

Yeeun Seong, Doyi Kim, Minseok Seo, Changick Kim

In precipitation forecasting, not only accuracy but also tem-poral resolution is critical. However, increasing temporal resolution isconstrained by observational limitations and the computational cost ofdense discrete modeling. To overcome this limitation, we reformulate pre-cipitation forecasting as a continuous-time dynamical system and pro-pose RainODE, a framework that models precipitation evolution in la-tent space using a Neural ODE. This formulation enables derivative-consistent temporal dynamics and captures the dominant large-scaleadvective motion of precipitation systems. Nevertheless, a purely de-terministic ODE struggles to represent non-advective intensity changessuch as localized growth, decay, and sub-grid variability, often lead-ing to over-smoothed predictions. To address this issue, we introducea stochastic source modeling module based on a Brownian Bridge for-mulation, which refines residual intensity variations and restores fine-grained structures while preserving advective consistency. By combiningdeterministic continuous dynamics with stochastic refinement, RainODEenables arbitrary-time inference while maintaining sharp predictions. Ex-periments on SEVIR and the newly introduced Radar-based Precipita-tion Integrated Dataset (RAPID) demonstrate consistent improvementsacross multiple temporal intervals and precipitation regimes. The codeis available at https://github.com/SeongYE/RainODE.

ECCV 2026

Scene Generation at Absolute Scale: Utilizing Semantic and Geometric Guidance From Text for Accurate and Interpretable 3D Indoor Scene Generation

Stefan Ainetter, Thomas Deixelberger, Edoardo Dominici, Philipp Drescher, Konstantinos Vardis, Markus Steinberger

We present GuidedSceneGen, a text-to-3D generation frame-work that produces metrically accurate, globally consistent, and semanti-cally interpretable indoor scenes. Unlike prior text-driven methods thatoften suffer from geometric drift or scale ambiguity, our approach main-tains an absolute world coordinate frame throughout the entire generationprocess. Starting from a textual scene description, we predict a global 3Dlayout encoding both semantic and geometric structure, which serves as aguiding proxy for downstream stages. A semantics- and depth-conditionedpanoramic diffusion model then synthesizes 360° imagery aligned with theglobal layout, substantially improving spatial coherence. To explore un-observed regions, we employ a video diffusion model guided by optimizedcamera trajectories that balances coverage and collision avoidance, achiev-ing up to 10× faster sampling compared to exhaustive path exploration.The generated views are fused using 3D Gaussian Splatting, yielding aconsistent and fully navigable 3D scene in absolute scale. GuidedSceneGenenables accurate transfer of object poses and semantic labels from lay-out to reconstruction, and supports progressive scene expansion withoutre-alignment. Quantitative results and a user study demonstrate greater3D consistency and layout plausibility compared to recent text-to-3Dbaselines. Project page: https://d3ixi.github.io/GuidedSceneGen/

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