ECCV 2026 论文列表

Designing functional and aesthetically coherent floor plansrequires exploring a vast space of possible room arrangements, a taskthat quickly becomes overwhelming for human designers. In this pa-per, we propose GRE-Diff, a controllable and interactive diffusion-basedframework that automates the creation and editing of apartment floorplans under user-specified constraints. By combining AI-generated sug-gestions with real-time, human-in-the-loop editing, the system enablesusers to specify room types, room counts, boundary shapes, and editingoperations through LLM-parsed instructions or GUI-based interaction.It then generates a diverse set of plausible and well-structured designs forrefinement. At the core of our approach is Gaussian Room Embedding(GRE), a continuous latent representation that models each room as aspatial Gaussian distribution capturing its location and extent. Exten-sive experiments on the RPLAN dataset show that GRE-Diff produceshigh-quality, constraint-aware, and editable polygonal layouts, offeringa practical step toward bridging AI-driven automation and human cre-ativity in spatial design.

Reinforcement Learning from Verifiable Rewards (RLVR)has substantially enhanced the reasoning capabilities of large languagemodels in abstract reasoning tasks. However, its application to LargeVision-Language Models (LVLMs) remains constrained by a structuralrepresentational bottleneck. Existing approaches generally lack explicitmodeling and effective utilization of visual information, preventing vi-sual representations from being tightly coupled with the reinforcementlearning optimization process and thereby limiting further improvementsin multimodal reasoning performance. To address this limitation, wepropose KAWHI (Key-Region Aligned Weighted Harmonic Incentive),a plug-and-play reward reweighting mechanism that explicitly incorpo-rates structured visual information into uniform reward policy optimiza-tion methods (e.g., GRPO and GSPO). The method adaptively localizessemantically salient regions through hierarchical geometric aggregation,identifies vision-critical attention heads via structured attribution, andperforms paragraph-level credit reallocation to align spatial visual ev-idence with semantically decisive reasoning steps. Extensive empiricalevaluations on diverse reasoning benchmarks substantiate KAWHI as ageneral-purpose enhancement module, consistently improving the perfor-mance of various uniform reward optimization methods. Code is availableat https://github.com/kawhiiiileo/KAWHI.

Probabilistic point cloud registration has attracted increasing attention due to its robustness to noise, outliers and occlusions. However, existing methods often suffer from high computational cost and neglect the role of informative priors. In this paper, we propose a new probabilistic registration method based on graph signal processing (GSP), called graph coherent point drift (GraphCPD). Specifically, we design a high-pass graph filter to extract high-frequency components, which are theoretically proven to be invariant under rigid transformations. These components are combined with point coordinates and normals to form a high-dimensional graph signal. We construct a local graph based on the graph signal and use the graph Laplacian model for registration. Compared with the classical Gaussian mixture models (GMMs), graph Laplacian models provide more discriminative geometric representations and enhance the model’s ability to capture graph structure. Furthermore, we exploit the invariance of high-frequency components to define prior probabilities, significantly reducing the corresponding search space and improving the speed of registration. Experimental results demonstrate that our method improves runtime efficiency over most existing probabilistic methods, while maintaining competitive registration accuracy, especially on large-scale point clouds. The source code is available at https://github.com/YCLai1997/GraphCPD.

Blood oxygen saturation (SpO2 ) is a vital indicator of respi-ratory and cardiovascular health, increasingly targeted by camera-based,contactless monitoring systems. Existing video-based approaches typi-cally rely on predefined frequency filtering to separate pulsatile (AC)and baseline (DC) components. Such spectral decomposition propagatesband-limited noise and assumes signal stationarity, limiting robustnessunder motion, illumination variation, and subject diversity.We propose FUSE, a filter-free, physics-guided framework that learnsAC and DC representations via structured differential constraints ratherthan static spectral filtering. The pulsatile component is regularized as aband-limited harmonic field through a second-order ordinary differentialequation, while spatial coherence is enforced by modeling it as a trans-ported density field under an advection-based partial differential con-straint. This coupled wave-transport formulation enforces physiologicallyconsistent spatiotemporal dynamics without explicit frequency-domain

Partial label learning in the instance-dependent setting (ID-PLL) poses significant challenges, as candidate labels are highly corre-lated with sample features, and strong semantic overlap among candi-dates often leads to error-prone disambiguation. Meanwhile, the infor-mative dark knowledge inherent in non-candidate labels remains largelyunderexplored. To address these issues, we propose DKDP, a dual dis-tillation method that extracts and transfers more stable and reliableknowledge from the teacher model. Specifically, we first use constrainedprojection to structurally rectify the teacher’s prediction distribution,enforcing the prior that the minimum value of candidate labels exceedsthe maximum value of non-candidate labels. On this basis, we guide thestudent model to fit the corrected teacher outputs. We further derive theminimum modification required to satisfy the prior, which theoreticallyguarantees the interpretability of the rectification process. Moreover, weintroduce class prototypes to model the distribution of fine-grained se-mantic relations, extending reliable knowledge distillation from the out-put space to the embedding space. This yields a stable, separable geomet-ric structure in the feature space. Extensive experiments on benchmarkdatasets demonstrate the effectiveness of our proposed method.

Multi-Exposure Fusion (MEF) effectively extends dynamicrange, but practical deployment is hindered by motion-induced ghostingand the scarcity of high-quality dynamic benchmarks. Current bench-marks largely neglect dynamic scenes and lack reliable ground truth,making it difficult to handle the complexity of real-world motions. In re-sponse, we introduce ExpoMotion, a large-scale benchmark designed toevaluate deghosting capabilities. Comprising 1,738 sequences and 10,909images across diverse environments, it covers a wide range of motionsand provides high-fidelity GTs constructed through an expert-guided ac-quisition pipeline. To tackle the complex dynamics and extreme con-ditions captured in this benchmark, we propose the Householder Or-thogonal Projection network (HOP), which revisits MEF deghostingfrom a mathematical perspective via Householder transformation, de-coupling multi-frame alignment into exposure pre-alignment and ghostfiltering. Specifically, the Global Priors Illumination Alignment (GPIA)module first rectifies drastic dynamic range discrepancies by utilizingglobal statistics for exposure harmonization. Regarding ghost removal,our Householder Orthogonal Attention (HOA) models artifacts as or-thogonal perturbations. By employing a dynamic Householder reflector,HOA effectively projects ghosts out of the feature manifold while pre-serving high-frequency details. Experiments demonstrate that our Ex-poMotion dataset enables superior generalization and artifact-free detailrestoration, while also validating the effectiveness and efficiency of theHOP method. The dataset and code are available at https://github.com/Leo-LiuYao/ExpoMotion.

Understanding fine-grained eating behaviors in unconstraineddaily-life videos is essential for dietary monitoring and behavioral healthassessment, yet existing video-language benchmarks lack the temporalgranularity and domain-specific grounding required to rigorously evalu-ate models in this health-critical domain. We introduce EatVid-Bench,a large-scale, multi-dimensional benchmark designed to advance fine-grained eating behavior understanding. Our dataset comprises nearly700 real-world eating sessions (around 3,000 minutes) annotated througha novel three-tier automated pipeline that integrates 12 complementarysignals, including body pose, food detection, bite events, and facial ex-pressions, with each annotation traceable to its source signal for fullinterpretability and verifiability. Building on this foundation, we con-struct a question-answering benchmark spanning seven capability di-mensions, three difficulty levels, and five question types, with rigorousprovenance tracking throughout. Comprehensive evaluation of state-of-the-art Video-LLMs exposes a substantial perception–reasoning gap, par-ticularly in sub-second temporal grounding and cross-frame counting. Tofurther validate the effectiveness of EatVid-Bench, we propose a domain-adapted fine-tuning strategy to provide a strong open-source baselineby leveraging structured training annotations as explicit reasoning su-pervision. Code and benchmark are publicly available at GitHub andHuggingFace.

The slow iterative sampling nature remains a major bottle-neck for the practical deployment of diffusion and flow-based genera-tive models. While consistency models (CMs) represent a state-of-the-art distillation-based approach for efficient generation, their large-scaleapplication is still limited by two key issues: training instability andinflexible sampling. Existing methods seek to mitigate these problemsthrough architectural adjustments or regularized objectives, yet over-look the critical reliance on trajectory selection. In this work, we firstconduct an analysis on these two limitations: training instability orig-inates from loss divergence induced by unstable self-supervised term,whereas sampling inflexibility arises from error accumulation. Based onthese insights and analysis, we propose the Dual-End ConsistencyModel (DE-CM) that selects vital sub-trajectory clusters to achievestable and effective training. DE-CM decomposes the PF-ODE trajec-tory and selects three critical sub-trajectories as optimization targets.Specifically, our approach leverages continuous-time CMs objectives toachieve few-step distillation and utilizes flow matching as a boundaryregularizer to stabilize the training process. Furthermore, we propose anovel noise-to-noisy (N2N) mapping that can map noise to any point,thereby alleviating the error accumulation in the first step. Extensiveexperimental results show the effectiveness of our method: it achieves astate-of-the-art FID score of 1.70 in one-step generation on the ImageNet256×256 dataset, outperforming existing CM-based one-step approaches.

Safe L2/L3 driving automation requires anticipating human-in-the-loop reactions during shared-control transitions. While most driv-ing world models forecast the external environment, in-cabin intelligenceremains strictly recognition-oriented and lacks multi-step rollout capa-bilities for driver dynamics. We introduce Driver-WM, a driver-centriclatent world model that rolls out in-cabin dynamics causally conditionedon out-cabin traffic context. This formulation unifies physical kinemat-ics forecasting with auxiliary behavioral and emotional semantic recog-nition. Operating in a compact latent space constructed from frozenvision-language features, Driver-WM adopts a dual-stream architectureto separately encode external traffic and internal driver states. Thesestreams are directionally coupled via a gated causal injection mechanism,which uses a learned vector gate to modulate external contextual per-turbations while strictly enforcing temporal causality. Experiments onAIDE show robust long-horizon forecasting on reactive high-motion clips,improved driver/traffic semantic alignment, and controlled interventionsthat expose the external-to-internal mechanism.

End-to-end autonomous driving increasingly relies on gener-ative planners, broadly classified into continuous diffusion policies anddiscrete token-based vision-language-action models (VLAs). While diffu-sion policies exploit parallel iterative noise prediction to efficiently gener-ate accurate trajectories, they suffer from cross-modal misalignment, lim-ited training efficiency, and poor generalization. Conversely, token-basedVLAs generate discrete trajectory tokens autoregressively, offering bettergeneralization but being constrained by inefficient causal reasoning, cu-mulative error, and irreversible decoding. To overcome these limitations,we propose DriveFine, a vision-language-action model that exploresmasked diffusion LLMs for trajectory planning, which significantly im-proves the efficiency, flexibility, and adaptability of trajectory generation.To address the inherent irreversibility of token decoding and improve thetrajectory quality, we introduce a plug-and-play block Mixture-of-Expert(block MoE) module, which seamlessly injects the refinement capa-bility into the dLLM at minimal cost. Through explicit expert routingand gradient isolation, DriveFine decouples generation and refinement,preventing cross-task interference. We further devise a tailored hybridreinforcement learning strategy to facilitate the effective exploration ofthe refinement expert and further explore the performance ceiling. Ex-tensive experiments on the NAVSIM v1, v2, and Navhard benchmarksdemonstrate that DriveFine exhibits strong performance, robustness andefficiency. The code will be released.

Despite the success of recent novel view synthesis methods,they tend to struggle in sparse-view settings. This poor generalizationto unseen viewpoints is an inherent challenge when training with lim-ited data. To address this, we investigate the relationship between losssharpness and generalization in novel view synthesis—an underexploreddirection. Interestingly, while pursuing flatter minima is widely knownto improve generalization in deep learning, reducing loss sharpness is notalways beneficial in novel view synthesis. We demonstrate that this dif-ference arises because high-detail regions inherently require a sharp losslandscape for accurate reconstruction, whereas low-detail regions benefitfrom a flat loss landscape for improving generalization. Based on this in-sight, we introduce structure-aware sharpness, defined within structure-adaptive neighborhoods, and propose to adaptively adjust the sharpnessregularization weight according to the local image structure. This strat-egy encourages flatter minima for generalization while preserving the losssharpness necessary to reconstruct fine details. Across various datasetsand configurations, our strategy consistently improves a wide range ofbaselines. Code is available at https://bbangsik13.github.io/FASR.

We present DASAM3D, a framework that fuses the complementary strengths of SAM3D and Depth Anything 3 (DA3) for objectaware 3D scene reconstruction. A fundamental appearance–geometry trade-off exists: SAM3D yields geometrically coherent per-object Gaussian primitives but with unrealistic appearance, while DA3 delivers photorealistic 3DGS reconstructions yet lacks object-level geometric reasoning. Our key insight is to use SAM3D exclusively as a 3D geometric layout provider, guiding DA3 geometry refinement while preserving its photorealistic textures. DASAM3D proceeds through three differentiable stages—(1) Global Alignment Optimization, (2) Per-Object Alignment Refinement, and (3) Layout-Based Geometry Optimization—and inherits DA3’s flexibility for posed or pose-free inputs at any scale. Experiments on DL3DV-10K and MipNeRF-360 show consistent gains in reconstruction accuracy and novel view synthesis; a user-preference study confirms DASAM3D’s object renderings are preferred over SAM3D’s in visual fidelity.

Recent advances in world models have greatly enhanced in-teractive environment simulation. Existing methods mainly fall into two⋆ Equal contribution. † Corresponding author.categories: (1) static world generation models, which construct 3D en-vironments without active agents, and (2) controllable-entity models,which allow a single entity to perform limited actions in an otherwise un-controllable environment. In this work, we introduce CustomX, leverag-ing the realism and structural grounding of static world generation whileextending controllable-entity models to support user-specified characterscapable of performing open-ended actions. Users can provide a 3DGSscene and a character, then use natural language to direct the characterto perform diverse behaviors, ranging from basic locomotion to object-centric interactions, while freely exploring the environment. CustomXsynthesizes temporally coherent video clips that preserve visual fidelitywith the provided scene and character, formulated as a conditional au-toregressive video generation problem. Built upon a pre-trained videogenerator, our training strategy significantly enhances motion dynamicswhile maintaining generalization across actions and characters. Our eval-uation covers a broad range of aspects, including visual quality, characterconsistency, action controllability, and long-horizon coherence.

Consistency distillation has significantly accelerated diffusion-model inference, but its sampling dynamics remain underexplored. Wereveal an asymmetry: although Logit-Normal sampling priors work well forstandard iterative generation, consistency distillation exhibits a differentdifficulty profile (e.g., U-shaped), with optimization bottlenecks concen-trated at the boundary stages rather than intermediate steps. To addressthe limitations of static sampling under evolving learning demands, wepropose Curvature-Adaptive Consistency Flow Matching (CACFM). Byformulating distillation as a dynamic decision process, CACFM usesa lightweight reinforcement learning agent to probe Probability FlowODE trajectories and construct an efficiency-oriented curriculum thatprioritizes critical regions without manual scheduling. Combined withFlow-adapted DMD and adversarial consistency objectives, our RL-basedscheduler achieves state-of-the-art results on large-scale models suchas FLUX and SDXL, mitigating structural deformities and preservinghigh-frequency details in extreme few-step regimes.

Training data for class-conditional image synthesis often ex-hibit a long-tailed distribution with limited amount of images for tailclasses. Such an imbalance causes mode collapse and reduces the diver-sity of synthesized images for tail classes. For class-conditional di!usionmodels trained with imbalanced data, we aim to improve the diversityand fidelity of tail class images without compromising the quality of headclass images. We propose contrastive conditional-unconditional align-ment (CCUA), which comprises two synergistic loss functions. Our firstloss is an Alignment Loss (AL) that aligns class-conditional generationwith unconditional generation at large timesteps. Alignment loss makesthe denoising process insensitive to class conditions for the initial steps,which enriches tail classes through knowledge sharing from head classes.Secondly, we diversify unconditional generation via an UnsupervisedContrastive Loss (UCL) to increase the distance/dissimilarity amongsynthetic images. We combine the two losses to implicitly diversify condi-tional generation. Our framework is easy to implement as demonstratedon both U-Net based architecture and Di!usion Transformer. Our methodoutperforms vanilla denoising di!usion probabilistic models, score-baseddi!usion model, and alternative contrastive methods for class-imbalancedimage generation across various datasets, in particular ImageNet-LT with256→256 resolution.

Human demonstrations provide strong priors for robot ma-nipulation, yet it is non-trivial to transfer them to execute on real robotsdue to the kinematic gap. In dexterous manipulation, it remains chal-lenging to track long-horizon, contact-rich sequences even in simulators:a reference-tracking policy must keep objects on their target trajectorieswhile preserving demonstrated joint motion and contact timing. Exist-ing approaches often rely on hand-crafted reward tuning that requireper-sequence tuning and break under limited interaction budgets. We in-troduce ConTrack, a reinforcement learning (RL) framework that scaleswith tracking data. ConTrack treats object tracking as a constraint andallocates remaining control authority to motion fidelity, which allows itto adapt task–style trade-offs online using a dual-variable update. In ad-dition, ConTrack also stabilizes long-horizon learning with an adaptivemid-trajectory reset library that reuses policy-reachable simulator states.Our qualitative and quantitative results in simulation tracking and realrobot demonstrate that ConTrack improves success and object pose ac-curacy significantly over prior arts while preserving joint and contactfidelity. Website: https://www.lyt0112.com/projects/ConTrack.

Few-shot anomaly detection remains challenging due to lim-ited training data. Multi-modal anomaly detection (MAD) offers a viablesolution, leveraging 3D geometric cues to enrich 2D RGB representationsand compensate for this scarcity. However, existing MAD methods applyspatially uniform feature processing, conflating stable macroscopic struc-tures with high-frequency localized defect signals, exacerbating cross-modal misalignment and inflating false-positive rates. To overcome this,we present CMDS-AD, a Cross-Modal Dual-Stream Anomaly Detectionframework. A LoRA-guided diffusion model generates diverse RGB sam-ples to mitigate extreme data scarcity. For 3D normal augmentation, weemploy a pre-trained diffusion model as a normal estimator. Crucially,this estimator inherently acts as a non-linear low-pass filter, directly ex-tracting low-frequency normal representations from RGB inputs. Thisestablishes an auxiliary estimated stream of purely low-frequency infor-mation, anchoring robust structural templates and assisting the uncom-pressed real stream, containing coupled high- and low-frequency compo-nents, to precisely isolate micro-defects. A Coordinate-Aware Hierarchi-cal Feature Mapper adaptively aligns cross-modal semantics, while a mul-tiplicative scoring mechanism filters modality-specific noise. Under theextreme 1-shot setting, CMDS-AD achieves absolute performance gainsof 5.7% (I-AUROC) and 2.0% (AUPRO) on MVTec 3D-AD, along-side 7.7% and 5.6% improvements on EyeCandies, establishing a newstate-of-the-art. Code is available at Junhaocai27/CMDS-AD.

Mainstream 3D asset retrieval relies predominantly on category-level matching, overlooking intra-class variations in geometry, style, andtexture critical for real-world applications. To address this, we redefineretrieval from binary categorical matching to intra-class graded relevanceestimation, treating relevance as a continuous variable to capture fine-grained semantic nuances. We propose a tri-modal encoder incorporatinga Semantic Conditioned Interaction module for cross-modal fusion and aQuery-Guided Mixture-of-Experts module for dynamic modality weight-ing. This design explicitly decouples intra-class variations and preventssemantic collapse via a re-parameterization mechanism. Furthermore,we introduce an automated, scalable benchmark generation frameworkdriven by a Multimodal Large Language Model to synthesize high-qualitysoft relevance labels, proposing a new benchmark: INGRE. Extensive ex-periments demonstrate our method achieves state-of-the-art results onINGRE while maintaining superior performance and robustness on stan-dard classification tasks.

Stereotype biases in Large Multimodal Models (LMMs) per-petuate harmful societal prejudices, undermining the fairness and equityof AI applications. As LMMs grow increasingly influential, addressingand mitigating inherent biases related to stereotypes, harmful generations,and ambiguous assumptions in real-world scenarios has become essential.However, existing datasets evaluating stereotype biases in LMMs oftenlack diversity, rely on synthetic images, and often have single-actor im-ages, leaving a gap in bias evaluation for real-world visual contexts. Toaddress the gap in bias evaluation using real images, we introduce theBBQ-Vision (BBQ-V), the most comprehensive framework for assessingstereotype biases across nine diverse categories and 50 sub-categories withreal and multi-actor images. BBQ-V benchmark contains 14,144 image-question pairs and rigorously evaluates LMMs through carefully curated,visually grounded scenarios, challenging them to reason accurately aboutvisual stereotypes. It offers a robust evaluation framework featuring real-world visual samples, image variations, and open-ended question formats.BBQ-V enables a precise and nuanced assessment of a model’s reasoningcapabilities across varying levels of difficulty. Through rigorous testingof 19 state-of-the-art open-source (general-purpose and reasoning) andclosed-source LMMs, we highlight that these top-performing models areoften biased on several social stereotypes, and demonstrate that the think-ing models induce more bias in the reasoning chains. This benchmarkrepresents a significant step toward fostering fairness in AI systems andreducing harmful biases, laying the groundwork for equitable and sociallyresponsible LMMs. Dataset and evaluation code are available here.