ECCV 2026 论文列表

As primary consumers in the marine food chain, zooplanktonplay a crucial role in maintaining marine ecological balance. However, theSegment Anything Model (SAM) exhibits limited performance in micro-scopic image instance segmentation due to its lack of zooplankton-specificdomain knowledge. To address these challenges, we propose a novel in-stance segmentation model based on SAM and wavelet transform (ZMIS-SAM), effectively tackling issues such as inaccurate classification, dis-continuous segmentation of slender appendages, and incomplete bound-ary segmentation. Our framework incorporates three core innovations:ZM-ViT enhances SAM’s capability to model zooplankton morphologyand image intensity distributions through two lightweight adapters, theNeighboring Feature Aggregation Module (NFAM) improves continu-ous segmentation of semi-transparent slender appendages by integrat-ing general-purpose and domain-specific features, and the Wavelet-basedMulti-scale Multi-directional Feature Enhancement (WM2FE) moduleeffectively recovers high-frequency details to refine boundary segmenta-tion completeness. Extensive experiments demonstrate that ZMIS-SAMachieves state-of-the-art instance segmentation performance on the zoo-plankton dataset and exhibits strong generalization capability acrossmultiple public cross-domain datasets. Code: ZMIS-SAM.

3D Gaussian Splatting (3DGS) has achieved remarkable suc-cess in real-time novel view synthesis, yet it suffers from severe overfit-ting under sparse-view settings due to insufficient geometric constraints.While recent methods introduce monocular depth priors to mitigatethis, they inherently struggle with scale ambiguity and cross-view in-consistency, leading to defective geometry. In this paper, we proposeStereoGS, a novel sparse-view 3DGS framework that integrates stereopriors to establish reliable binocular consistency. Unlike scale-agnosticmonocular constraints, StereoGS introduces a Stereo Depth Regulariza-tion by constructing virtual stereo pairs during optimization and lever-aging a foundation stereo model to enforce absolute scale and binocular-consistent structures. To further suppress overfitting and eliminate re-dundant primitives, we design a Gradient-Aware Opacity Decay strat-egy that dynamically penalizes Gaussians based on their relative opacitygradient magnitudes. Combined with a Consistency-Aware Dense Ini-tialization using zero-shot multi-view depth estimation, StereoGS effec-tively anchors primitives to accurate scene surfaces. Extensive experi-ments on LLFF, DTU, Mip-NeRF360, and Blender datasets demonstratethat StereoGS achieves state-of-the-art performance in sparse-view set-tings without incurring any additional inference overhead. Project Page:https://stringerywh00.github.io/StereoGS_project_page/

Video generation has recently made striking visual progress,but maintaining coherent object motion and interactions remains dif-ficult. We trace two practical bottlenecks: (i) human-provided motionhints (e.g., small 2D maps) often collapse to too few effective tokens afterencoding, weakening guidance; and (ii) Current architectures inevitablybias toward pixel-level texture reconstruction over complex structuralmotion, yielding videos that are photorealistic but physically incoherent.We present STANCE, an image-to-video framework that addresses bothissues with two simple components. First, we introduce Instance Cues,a pixel-aligned control signal that turns sparse, user-editable hints into adense 2.5D (camera-relative) motion field by averaging per-instance flowand augmenting with monocular depth over the instance mask. Thisreduces depth ambiguity compared to 2D drag/arrow inputs while re-maining easy to user. Second, we preserve the salience of these cuesin the latent token space with Dense RoPE, which tags a small setof initial-frame motion tokens with spatially-addressable rotary embed-dings. Finally, rather than treating appearance and structure as looselycoupled tasks, STANCE integrates an auxiliary stream directly withinthe same Diffusion Transformer (DiT). By sharing identical latent tokensand Dense RoPE, this auxiliary stream acts as a "geometry witness" thatheavily penalizes geometric drift.

Lifelong Person Re-Identification (LReID) aims to train aunified person retrieval model from a non-stationary data stream. Ex-isting LReID methods mainly focus on scenarios where the clothingof each person is consistent. Recently, the Cloth-Hybrid LReID (CH-LReID) where cloth-consistent and cloth-changing data alternately oc-cur, has emerged as a more practical and challenging scenario. Due to theconflict between clothing-relevant and clothing-irrelevant knowledge, thewell-known catastrophic forgetting problem is significantly exacerbatedin this task. To address this issue, we propose a SubDistribution-awareCOllaborative Knowledge REinforcing (SCORE) framework, where ourkey idea is explicitly modeling the intra-identity diversity to continu-ally consolidate distinct cloth-consistent and cloth-changing knowledge.Specifically, an Adaptive SubDistribution Modeling mechanism is devel-oped, where a set of distributional subprototypes is assigned to eachidentity to capture the intra-identity diversity, improving the compati-bility between cloth-consistent and cloth-changing knowledge. Then, aDistributional Knowledge Reinforcement scheme is introduced, wherethe knowledge of old distributional subprototypes is retained in the newones by a collaborative aligning mechanism. Extensive experiments showthat our SCORE achieves the state-of-the-art performance. Our code isavailable at https://github.com/zhoujiahuan1991/ECCV2026-SCORE.

The representation of 3D clothed humans as standardized 2DUV texture and displacement maps over an underlying body model haslong been studied. This compact representation is enticing as it enablespretrained image networks to process, generate, and edit 3D avatars, butis only useful if scans are accurately aligned and brought into correspon-dence via high-fidelity registration. This prerequisite has never been met,which we argue explains the limited quality of prior UV-based methodsfor clothed humans. Despite its significance, no public method produceshigh-fidelity SMPL(−X)+D registrations with UV texture from arbitraryclothed scans. We present AvaImg, a multi-stage optimization pipeline, toclose this gap: it enforces body-inside-clothing constraint via signed wind-ing numbers, made viable by a three-level efficiency cascade (∼10× run-time reduced, ∼95% storage saved), and recovers fine surface detail usingcoarse-to-fine displacement optimization. AvaImg outperforms all base-lines in body fitting, shape estimation, and surface registration acrosssix datasets, yielding textured registrations near-indistinguishable fromscans (PSNR=34.48dB). For validation of AvaImg’s Avatar-as-Image rep-resentation as imminently compatible with image foundation models, weauto-encode our UV maps via the frozen FLUX VAE. This achieves only0.76mm added Chamfer error relative to scan and shows that the result-ing maps lie within natural-image distributions, supporting the use of 2Dgenerative priors for 3D avatar generation. Code, data, and Singularitycontainers will be publicly released.

Recent advancements in unified multimodal understandingand visual generation (or multimodal generation) models have been hin-dered by their quadratic computational complexity and dependence onlarge-scale training data. We present OmniMamba, the first linear-architecture-based multimodal generation model that generates both text and imagesthrough a unified next-token prediction paradigm. The model fully lever-ages Mamba-2’s high computational and memory efficiency, extending itscapabilities from text generation to multimodal generation. To addressthe data inefficiency of existing unified models, we propose two key inno-vations: (1) decoupled vocabularies to guide modality-specific generation,and (2) task-specific LoRA for parameter-efficient adaptation. Further-more, we introduce a decoupled two-stage training strategy to mitigatedata imbalance between two tasks. Equipped with these techniques, Om-niMamba achieves competitive performance with JanusFlow while sur-passing Show-o across benchmarks, despite being trained on merely 2Mimage-text pairs, which is 1,000 times fewer than Show-o. Notably, Om-niMamba stands out with outstanding inference efficiency, achieving upto a 119.2× speedup and 63% GPU memory reduction for long-sequencegeneration compared to Transformer-based counterparts. Code and mod-els are released at https://github.com/hustvl/OmniMamba.

Recent advancements in audio-video joint generation mod-els have demonstrated impressive capabilities in content creation. How-ever, generating high-fidelity human-centric videos in complex, real-worldphysical scenes remains a significant challenge. We identify that the rootcause lies in the structural deficiencies of existing datasets across threedimensions: limited global scene and camera diversity, sparse interac-tion modeling, and insufficient individual attribute alignment. To bridgethese gaps, we present OmniHuman, a large-scale, multi-scene datasetdesigned for fine-grained human modeling. OmniHuman provides a hier-archical annotation covering video-level scenes, frame-level interactions,and individual-level attributes. To facilitate this, we develop a fully au-tomated pipeline for high-quality data collection and multi-modal an-notation. Complementary to the dataset, we establish the OmniHumanBenchmark (OHBench), a three-level evaluation system that providesa scientific diagnosis for human-centric audio-video synthesis. Crucially,OHBench introduces metrics that are highly consistent with human per-ception, filling the gaps in existing benchmarks by providing a compre-hensive diagnosis across global scenes, relational interactions, and in-dividual attributes. Experiments show that fine-tuning on only 20% ofOmniHuman significantly boosts performance, validating its effective-ness in advancing complex scenario modeling. The dataset is available athttps://huggingface.co/datasets/julia527/OmniHuman

Large Vision-Language Models (VLMs) show immense po-tential in autonomous driving but struggle with precise spatial ground-ing, often hallucinating when forced to regress continuous image coordi-nates. To overcome this, we introduce a "Less is More" object-centricprompting strategy that fundamentally decouples spatial localizationfrom logical reasoning. By overlaying detected bounding boxes and dis-crete IDs directly onto multi-view images, the VLM only predicts dis-crete IDs, which are deterministically mapped back to precise coordi-nates via an offline lookup table. This model-agnostic approach trans-forms error-prone regression into reliable discrete matching without re-quiring structural modifications. Extensive evaluations on the DriveLM-nuScenes dataset demonstrate striking improvements in comprehensivedriving reasoning and spatial grounding. Notably, our method booststhe Accuracy and Match scores of the DriveLM-agent by absolute mar-gins of 8.26% and 10.39%, respectively. Furthermore, when applied tostronger models, it achieves impressive increases of 11.85% and 20.02%in Accuracy and Match for Qwen2.5-VL, as well as 8.75% and 19.88% forInternVL-3.5. These consistent gains definitively prove the strong plug-and-play generalizability of our paradigm across varying architectures.

Understanding and generating 3D objects as compositionsof meaningful parts is fundamental to human perception and reasoning.However, most text-to-3D methods overlook the semantic and functionalstructure of parts. While recent part-aware approaches introduce de-composition, they remain largely geometry-focused, lacking semanticgrounding and failing to model how parts align with textual descriptionsor their inter-part relations. We propose DreamPartGen, a frameworkfor semantically grounded, part-aware text-to-3D generation. Dream-PartGen introduces Duplex Part Latents (DPLs) that jointly modeleach part’s geometry and appearance, and Relational Semantic Latents(RSLs) that capture inter-part dependencies derived from language. Asynchronized co-denoising process enforces mutual geometric and seman-tic consistency, enabling coherent, interpretable, and text-aligned 3Dsynthesis. Across multiple benchmarks, DreamPartGen delivers state-of-the-art performance in geometric fidelity (→60% Chamfer Distance) andtext–shape alignment (↑↓20% CLIP/ULIP), while producing composi-tionally consistent and controllable parts.PLAN Lab https://plan-lab.github.io/dreampartgen

Camouflaged object detection (COD) segments objects thatintentionally blend with the background, so predictions depend on subtletexture and boundary cues. COD is often needed under tight on-devicememory and latency budgets, making low-bit inference highly desirable.However, COD is unusually hard to quantize aggressively. We studypost-training W4A4 quantization of Transformer-based COD and finda task-specific cliff: heavy-tailed background tokens dominate a sharedactivation range, inflating the step size and pushing weak-but-structuredboundary cues into the zero bin. This exposes a token-local bottleneck—remove cross-token range domination and bound the zero-bin mass under4-bit activations. To address this, we introduce COD-TDQ, a COD-aware Token-group Dual-constraint activation Quantization method.COD-TDQ addresses this token-local bottleneck with two coupled steps:Direct-Sum Token-Group (DSTG) assigns token-group scales to sup-press cross-token range domination, and Dual-Constraint Range Projection(DCRP) projects each token-group clip range to keep the step-to-dispersionratio and the zero-bin mass bounded. Across four COD benchmarksand two baseline models (CFRN and ESCNet), COD-TDQ consistentlyachieves an Sα score more than 0.12 higher than that of the state-of-the-art quantization method without retraining. The code is available athttps://github.com/MCG-NKU/nku-model-compre.

Flow matching (FM) models generate data by learning a ve-locity field that transforms samples from a simple latent distribution,typically an isotropic Gaussian, to the data distribution. However, thegeometric mismatch between a unimodal, full-dimensional Gaussian anda multimodal, approximately low-dimensional data distribution leads toa complex velocity field that is costly to learn at training time andto integrate at inference time. In this paper, we propose ParsimoniousFlow Matching (PFM), which adopts a mixture of Gaussians (MoG)as the latent distribution whose geometry better aligns with the data.We identify key design choices that enable efficient FM, including anoptimal-transport data-latent coupling, MoG estimation via k-means,and eigenvalue regularization of the per-mode covariances. Theoretically,when both data and latent distributions are MoGs, we show that gra-dient descent for FM with affine velocity fields converges faster whenthe corresponding modes of the latent and data are more similar andthe per-mode covariances are well-conditioned. Further, for data thatfollows a separated MoG, we prove that replacing the isotropic Gaus-sian latent with a MoG in FM training accelerates gradient descentconvergence and lowers the initial loss. On CIFAR-10 and ImageNet(32 × 32), PFM achieves the same generation quality as the baselinein fewer training iterations, and produces higher-quality samples for thesame number of function evaluations at inference time. Code is available:https://github.com/tianjiaoding/pfm.

Conversational talking face generation has recently attractedincreasing attention, aiming to synthesize interactive talking videos wherecharacters speak, listen, and respond dynamically to each other. Thistask presents three core challenges: 1) Flexibility: enabling multi-rounddialogues with an arbitrary number of participants; 2) Naturalness: main-taining coherent motion and appropriate non-verbal feedback through-out the interaction; and 3) Efficiency: achieving real-time generation andlow computation overhead for long-term continuous online conversation.Despite recent advances, existing methods still fall short in balancingall three requirements. To bridge this gap, we introduce InterTalk, anovel and efficient framework designed for highly interactive conver-sational talking face generation. Built upon a motion-based architec-ture, InterTalk supports real-time conversation synthesis. Our methodachieves strong flexibility by explicitly modeling multi-round conversa-tional dynamics among each participant, eliminating constraints on theirnumbers. To enhance interactivity, we incorporate motion feedback frommultiple participants and introduce an iterative generation strategy formore natural behaviors. Besides, we disentangle motion into several fa-cial components, enabling targeted refinements for natural response suchas precise lip-sync and realistic eye-blinking. Finally, we construct a newmulti-person conversational dataset and enrich it with 3D face-baseddata augmentation. Extensive experiments demonstrate that InterTalkachieves superior interaction quality while maintaining real-time perfor-mance at 30 FPS.

Vision-Language-Action (VLA) models enable robots to per-form manipulation tasks directly from natural language instructions andare increasingly viewed as a foundation for generalist robotic policies.However, their reliability under Out-Of-Distribution (OOD) instructionsremains underexplored. In this paper, we reveal a critical failure modein which VLA policies continue executing visually plausible actions evenwhen the language instruction contradicts the scene. We refer to this phe-nomenon as linguistic blindness, where VLA policies prioritize visualpriors over instruction semantics during action generation. To systemati-cally analyze this issue, we introduce ICBench, a diagnostic benchmarkconstructed from the LIBERO dataset that probes language–action cou-pling by injecting controlled OOD instruction contradictions while keep-ing the visual environment unchanged. Evaluations on three represen-tative VLA architectures, including π0 , π0.5 , and OpenVLA-OFT, showthat these models frequently succeed at tasks despite logically impossi-ble instructions, revealing a strong visual bias in action generation. Tomitigate this issue, we propose Instruction-Guided Attention Re-calibration (IGAR), a train-free inference-time mechanism that rebal-ances attention distributions to restore the influence of language instruc-tions. IGAR operates without retraining or architectural modificationand can be directly applied to existing VLA models. Experiments across30 LIBERO tasks demonstrate that IGAR substantially reduces erro-neous execution under OOD contradictory instructions while preservingbaseline task performance. We additionally validate the approach on areal Franka robotic arm, where IGAR effectively prevents manipulationtriggered by inconsistent instructions.

3D Gaussian Splatting has revolutionized novel view synthe-sis with real-time rendering and photorealistic quality. However, standard3DGS methods fail in aerial scenes due to sparse anisotropic UAV captures.Two critical issues emerge: incomplete triangulated points in texturelessregions that destabilizes Gaussian initialization and view-aligned degen-eracies where Gaussians overfit limited observations rather than respecttrue geometry. We present StratoSplat, a framework that exploits the in-herent layered structure of aerial scenes for robust sparse-view 3DGS. Ourkey insight is that buildings, terrain, and roads naturally stratify alonggravity, providing strong geometric priors to regularize this problem. Weaddress point sparsity through layered memory guided initialization thatenforces multi-view consistency. To prevent optimization degeneracies, weintroduce neural multi-plane Gaussians where virtual primitives anchor tolearned parallel planes for geometric regularization. Extensive experimentsdemonstrate state-of-the-art performance, surpassing the best baseline byover 3dB. Code available at https://github.com/keloee/StratoSplat.

Animal Re-Identification (Animal ReID) is crucial for eco-logical monitoring in the wild, yet remains challenging due to the fine-grained and diverse visual features of animals, including intricate tex-tures, distinctive patterns, and various pose or form representations.While current Animal ReID methods mostly rely on holistic global fea-tures, they frequently overfit to the surrounding environment rather thancapturing localised, identity-relevant cues. Existing pattern-based meth-ods primarily focus on global generalised signals that are sensitive tobackground interventions and often fail to highlight identity evidencefor individual discrimination. Additionally, these methods are mainlyconstrained to the visual space only and lack pattern-based semanticinteractions from the multimodal space. To address these challenges,we introduce a Region-Aware Multimodal Interleaving (RAMI) frame-work, which formulates Animal ReID as a visual-textual semantic in-teraction over informative, localised regions. Specifically, RAMI projectssegmentation-derived region tokens into a shared token space and inter-leaves them with semantic tokens within a Transformer-based module tofacilitate dense cross-modal interactions. Given the lack of region-leveldata, we design a simple pipeline to produce biologically representativeregions from segmented animals. To the best of our knowledge, we are thefirst to formulate multimodal interleaving tailored for fine-grained dis-criminative tasks in Vision-Language Models (VLMs). Experiments showthat RAMI outperforms state-of-the-art methods across both benchmarkand in-the-wild datasets, with an average mAP gain of up to 18.7%. Ourcode is available at https://github.com/ML-4-SocialGood/RAMI.git.

Reconstructing articulated 3D objects from a single imagerequires jointly inferring object geometry, part structure, and motionparameters from limited visual evidence. A key difficulty lies in theentanglement between motion cues and object structure, which makesdirect articulation regression unstable. Existing methods address thischallenge through multi-view supervision, retrieval-based assembly, orauxiliary video generation, often sacrificing scalability or efficiency. Wepresent MonoArt, a unified framework grounded in progressive struc-tural reasoning. Rather than predicting articulation directly from im-age features, MonoArt progressively transforms visual observations intocanonical geometry, structured part representations, and motion-awareembeddings within a single architecture. This structured reasoning pro-cess enables stable and interpretable articulation inference without ex-ternal motion templates or multi-stage pipelines. Extensive experimentson PartNet-Mobility demonstrate that MonoArt achieves state-of-the-artperformance in both reconstruction accuracy and inference speed. Theframework further generalizes to robotic manipulation and articulatedscene reconstruction.

Reaction diagram parsing (RxnDP) is critical for extractingchemical synthesis information from literature. Although recent Vision-Language Models (VLMs) have emerged as a promising paradigm toautomate this complex visual reasoning task, their application is funda-mentally bottlenecked by the inability to align visual chemical entitieswith pre-trained knowledge, alongside the inherent discrepancy betweentoken-level training and reaction-level evaluation. To address these dualchallenges, this work enhances VLM-based RxnDP from two comple-mentary perspectives: prompting representation and learning paradigms.First, we propose Identifier as Visual Prompting (IdtVP), whichleverages naturally occurring molecule identifiers (e.g., bold numeralslike 1a) to activate the chemical knowledge acquired during VLM pre-training. IdtVP enables powerful zero-shot and out-of-distribution ca-pabilities, outperforming existing prompting strategies. Second, to fur-ther optimize performance within fine-tuning paradigms, we introduceRe3 -DAPO, a reinforcement learning algorithm that leverages verifi-able rewards to directly optimize reaction-level metrics, thereby achiev-ing consistent gains over standard supervised fine-tuning. Additionally,we release the ScannedRxn benchmark, comprising scanned historicalreaction diagrams with real-world artifacts, to rigorously assess model ro-bustness and out-of-distribution ability. Our contributions advance theaccuracy and generalization of VLM-based reaction diagram parsing.

Evaluation of autonomous vehicle (AV) planners in safety-critical closed-loop simulation is essential for real-world deployment. How-ever, generating controllable safety-critical scenarios remains challeng-ing. Existing approaches use soft guidance that provides only probabilis-tic preferences and cannot guarantee the satisfaction of geometric andseverity constraints associated with specific collision types. We introduceCollision-Constrained Flow Matching (CCFM), a novel framework thatguarantees precise collision control through hard physical constraints.CCFM consists of three key components: (i) a heuristic collision selectorthat optimally identifies an adversarial agent and collision type via com-posite scoring; (ii) structured hard constraints that explicitly define fourcollision types (rear-end, side, cut-in, head-on) through contact point,heading, and severity requirements; and (iii) a collision-constrained flowmatching sampler that enforces the constraints via Gauss-Newton man-ifold projection. CCFM achieves collision rate up to 46.4% on nuScenesand 83.1% on nuPlan, significantly outperforming baselines while pre-serving realistic driving behavior. By enabling controllable collision char-acteristics in safety-critical scenario generation, CCFM provides a reli-able foundation for AV safety evaluation and sim-to-real crash data gen-eration. The code and implementation details are available at https://github.com/KELISBU/CCFM.

Feed-forward 3D reconstruction provides an efficient paradigmfor scene modeling from image sequences. Scaling these models to largemonocular scenarios are constrained by excessive GPU memory foot-print, degraded local geometry, and long-term trajectory drift. Exist-ing chunk-based optimization strategies provide limited geometric con-straints and fail to maintain global consistency over extended trajecto-ries. We present a unified framework for stable and scalable feed-forward3D reconstruction from long monocular sequences. Our approach buildson coarse-to-fine trajectory alignment augmented by lightweight geomet-ric prior injection. Distilling monocular geometric cues into the feed-forward backbone via LoRA adaptation improves depth accuracy on finestructures while preserving inference efficiency. We introduce a hybrid-weight sparse ray-field optimization that leverages high-frequency geo-metric features to guide local point-cloud refinement and enforce con-sistent inter-frame ray constraints. Unlike prior chunk-based methods,this establishes strong cross-frame geometric coupling while maintainingscalability. Finally, an efficient trajectory stitching strategy with jointray-error optimization explicitly reduces accumulated drift. Extensiveexperiments show that our approach achieves competitive trajectory ac-curacy compared with representative SLAM systems, while maintainingglobally consistent 3D reconstruction in large-scale scenarios.