ECCV 2026 Papers

Understanding person-level bi-manual interactions requiresnot only detecting hands, but also identifying which two hands belongto the same person and what each hand interacts with. Existing hand–object interaction methods are mostly hand-centric: they treat each handas an independent instance, which can lead to ambiguous ownership inmulti-person scenes.We propose a person-centric formulation in which a single query pre-dicts a structured output for one person, including the human box,body pose, hand boxes and states, and interaction targets. We intro-duce part-aware deformable attention to allocate attention acrosshuman, hand, and pose-specific reference regions, enabling one query tocapture the full person structure. We further unify detection and inter-action reasoning with a hand-to-query relationship matrix, whereeach hand selects its interaction target from the detected query set plusa learnable off token, directly recovering the target’s box and class with-out separate object regression.We build a COCO-based dataset with person-centric bi-manual interac-tion annotations and define structured metrics for evaluating hand statesand complete hand–object tuples. Experiments with a transformer-baseddetector show that our formulation improves person-level bi-manual in-teraction parsing and provides an effective unified framework for jointdetection, pose estimation, and hand reasoning.Project page: https://lgecto-ail-vil.github.io/SingleQuery-BHOI/

Distilling video generation models to extremely low infer-ence budgets (e.g., 2–4 NFEs) is crucial for real-time deployment, yetremains challenging. Trajectory-style consistency distillation often be-comes conservative under complex video dynamics, yielding an over-smoothed appearance and weak motion. Distribution matching distilla-tion (DMD) can recover sharp, mode-seeking samples, but its local train-ing signals do not explicitly regularize how denoising updates composeacross timesteps, making composed rollouts prone to drift. To overcomethis challenge, we propose Self-Consistent Distribution Matching Distil-lation (SC-DMD), which explicitly regularizes the endpoint-consistentcomposition of consecutive denoising updates. For real-time autoregres-sive video generation, we further treat the KV cache as a quality param-eterized condition and propose Cache-Distribution-Aware training. Thistraining scheme applies SC-DMD over multi-step rollouts and introducesa cache-conditioned feature alignment objective that steers low-qualityoutputs toward high-quality references. Across extensive experiments onboth non-autoregressive backbones (e.g., Wan 2.1) and autoregressivereal-time paradigms (e.g., Self Forcing), our method, dubbed Salt, con-sistently improves low-NFE video generation quality while remainingcompatible with diverse KV-cache memory mechanisms. Project page:https://xingtongge.github.io/Salt.

The impact of real-world noise on Open Vocabulary ObjectDetectors (OV-ODs) remains poorly understood due to their architec-tural complexity. We present our comprehensive analysis, Robust Onion,an empirical study that uses controlled synthetic visual degradations topeel OV-ODs layer-by-layer, revealing how, why, and where robustnessdegrades, systematically analyzing feature collapse. Our findings revealthat models with similar vision backbones exhibit comparable robust-ness, driven by similar feature collapse at similar layers, while factorssuch as pretraining strategy, architectural nuances, and caption supervi-sion contribute little. Robustness is primarily governed by the image do-main rather than annotations, explaining the similar robustness impacton COCO and LVIS, and why datasets like ODinW-13 can give an im-pression of inflated robustness due to large, isolated objects. Finally, wevalidate our insights by improving robustness on real-world BDD-100K,WiderFace, and VisDRONE via our lightweight plug-and-play NN &TK0 approach, using 96× fewer trainable parameters than end-to-endtraining. We also explain the prior works’ robustness observations.

Weakly-supervised video scene graph generation (WS-VSGG)aims to parse video content into structured relational triplets withoutbounding box annotations and with only sparse temporal labeling, sig-nificantly reducing annotation costs. Without ground-truth boundingboxes, these methods rely on off-the-shelf detectors to generate objectproposals, yet largely overlook a fundamental discrepancy from fully-supervised pipelines. Fully-supervised detectors implicitly filter out non-interactive objects, while off-the-shelf detectors indiscriminately detectall visible objects, overwhelming relation models with noisy pairs. We ad-dress this by introducing a learnable pair affinity that estimates the like-lihood of interaction between subject–object pairs. Through Pair AffinityLearning and Scoring (PALS), pair affinity is incorporated into inference-time ranking and further integrated into contextual reasoning throughPair Affinity Modulation (PAM), enabling the model to suppress non-interactive pairs and focus on relationally meaningful ones. To providecleaner supervision for pair affinity learning, we further propose Relation-Aware Matching (RAM), which leverages vision-language grounding toresolve class-level ambiguity in pseudo-label generation. Extensive exper-iments on Action Genome demonstrate that our approach consistentlyyields substantial improvements across different baselines and backbones,achieving state-of-the-art WS-VSGG performance.

While existing feed-forward Gaussian splatting models of-fer computational efficiency and can generalize to sparse view settings,their performance is fundamentally constrained by relying on a singleforward pass for inference. We propose ReSplat, a feed-forward recurrentGaussian splatting model that iteratively refines 3D Gaussians withoutexplicitly computing gradients. Our key insight is that the Gaussiansplatting rendering error serves as a rich feedback signal, guiding therecurrent network to learn effective Gaussian updates. This feedbacksignal naturally adapts to unseen data distributions at test time, enablingrobust generalization across datasets, view counts, and image resolutions.To initialize the recurrent process, we introduce a compact reconstructionmodel that operates in a 16× subsampled space, producing 16× fewerGaussians than previous per-pixel Gaussian models. This substantiallyreduces computational overhead and allows for efficient Gaussian updates.Extensive experiments across varying number of input views (2, 8, 16, 32),resolutions (256×256 to 540×960), and datasets (DL3DV, RealEstate10K,and ACID) demonstrate that our method achieves state-of-the-art perfor-mance while significantly reducing the number of Gaussians and improvingthe rendering speed. Our project page is at haofeixu.github.io/resplat.

Personalization models generate images guided by a few subject references, while style transfer methods aim to produce images aligned with a global style derived from a reference image. Recent approaches perform well when the reference image contains a single object, effectively capturing a global style that encompasses all implicit attributes. However, when applied to complex real-world scenes containing multiple objects with distinct attributes, existing methods struggle to selectively control generation using attributes from specific local regions of the reference image. Due to their primarily global conditioning strategies, these methods cannot reliably guide generation using attributes from specific local elements in the reference image. Moreover, existing methods typically employ a single identifier token to capture all details from the reference, resulting in a lack of individual, attribute-level control. Motivated by these limitations, we propose RefDiT, a novel framework for reference-guided image generation. RefDiT takes as input a reference image, a text prompt, and an optional user-provided guidance control. RefDiT employs local region guidance using the attributes of local elements. It constructs an attribute-aware conditioning signal from the reference image by performing attribute-level decomposition of the identifier tokens to train low-rank adapter (LoRA) blocks of a diffusion transformer (DiT)-based generative model. RefDiT leverages multi-modal joint attention to learn the correspondence between identifier tokens and local regions in the reference image, enabling more effective local guidance. RefDiT achieves a local attribute matching score of 0.88, outperforming state-of-the-art methods such as UnZipLoRA (0.54), K-LoRA (0.58), and B-LoRA (0.42). Compared to commercial models like Gemini-Banana (0.74) and GPT-5 (0.84), user studies indicate that participants find RefDiT’s outputs to be of similar quality.

Although Contrastive Language-Image Pretraining (CLIP)has shown promising transfer ability for ophthalmic image analysis, itsopaque decision-making process hinders interpretability, especially inophthalmic settings where probability-only outputs may be difficult forclinicians and patients to trust. Providing visual evidence for predictionscan therefore make models more inspectable and interpretable. How-ever, existing explainability methods often struggle to produce spatiallyconsistent and semantically aligned heatmaps that highlight prediction-relevant anatomical or pathological evidence. To bridge this gap, wepropose OTSMDL, a post-hoc framework for interpretable region selec-tion that couples submodular-inspired scoring with unbalanced optimaltransport. We formulate the identification of salient evidence regions as asubset selection problem. Specifically, submodular-inspired utilities firstconstruct an initial candidate subset and a score-guided source distri-bution, which are then used to initialize the Optimal Transport (OT)process. Subsequently, OT performs global transport-based re-rankingover all candidates, constructing a unified cost matrix to encourage cross-region competition and align candidate superpixels with text prompts.We evaluate OTSMDL on seven ophthalmic datasets spanning color fun-dus photography, OCT, and glaucoma assessment, with additional Ima-geNet experiments for out-of-domain analysis. Under standard post-hocDeletion/Insertion protocols and ground-truth mask localization met-rics, OTSMDL generally outperforms baselines in region-level evidencegrounding, achieving top-1/2 performance in most evaluation settings.

Multimodal Large Language Models (MLLMs) have achievedremarkable progress but still struggle with complex visual reasoningtasks requiring multi-step perception and logical deduction. While ex-plicit visual generation incurs prohibitive computational costs, existinglatent approaches often rely on external experts or lack rigorous cogni-tive logic. In this paper, we introduce ProLaViT (Progressive LatentVisual Thought), a framework empowering MLLMs to perform struc-tured visual derivation in the continuous latent space. Unlike worksdependent on heterogeneous external models, ProLaViT leverages anendogenous self-distillation mechanism, utilizing the model’s own vi-sual encoder to supervise latent thoughts. To facilitate this, we con-struct a scalable programmatic synthesis pipeline enabling the modelto internalize algorithmic precision without inference-time tools. We de-sign two reasoning paradigms: (1) Coarse-to-Fine Causal Chain for spa-tial tasks, guiding attention from global context to local targets. (2)Dialectical Reasoning Chain for logical tasks, incorporating counter-factual thinking for verification. Furthermore, we propose a Distance-Weighted Diversity Loss to impose topology-aware constraints, prevent-ing feature degeneration by enforcing semantic distinctiveness. Exten-sive experiments demonstrate that ProLaViT outperforms baselines onvision-centric benchmarks, achieving superior accuracy and interpretabil-ity with high efficiency.

Sparse Mixture-of-Experts (MoE) offers an attractive pathto scaling discrete diffusion vision–language models (dVLMs), but wefind that naïvely combining MoE with multimodal diffusion training canfail catastrophically. Specifically, MoE-dVLMs suffer from expert selec-tion collapse on visual tokens, where most image tokens are routed toonly a few experts and overall multimodal performance degrades sharply.We trace this failure to an upstream geometric mismatch at the modalityboundary rather than router optimization. Visual embeddings producedby deep Pre-Norm vision encoders have much larger norms than text em-beddings; when injected into a Pre-Norm language tower, the residualdynamics cause attention drowning and representational inertia, yield-ing highly concentrated, low-rank visual representations that are difficultfor the router to discriminate. To resolve this issue, we propose Tower-Align, a lightweight post-projector modality-wise norm alignment thatglobally rescales visual tokens to match text norm statistics while pre-serving their relative structure. TowerAlign stabilizes MoE routing andimproves multimodal accuracy with negligible overhead. On a practicalMoE-dVLM built from LLaDA-MoE-7B-A1B with a SigLIP2-SO400M-Patch14-384 vision encoder, TowerAlign delivers consistent gains acrossdiverse benchmarks and largely closes the gap to dense diffusion baselinesunder matched data budgets.

Slide design requires personalizing both deck themes andpage layouts. Yet, current AI agent-based methods struggle with fine-grained, page-level design. Solely relying on prespecified templates oruser verbose instructions, they fail to capture latent design intents, leav-ing Page-level Slide Personalization (PSP) unresolved. To close this gap,this work formulates PSP as an inverse planning problem. We proposeto learn a design intent without assuming any knowledge of the spe-cific executing tools (e.g., PowerPoint, Beamer) being used. However,relinquishing control over these tools makes the problem intractable tooptimize end-to-end. To overcome this, we propose Spire, a principledframework to solve PSP approximately. By intentionally corrupting thevisual structures of clean slides, Spire creates a verifiable task to de-noise the corruption, whereby two agents learn to collaboratively refineexecutable designs via reinforcement learning (RL). We present a proofthat structural denoising is a consistent surrogate for PSP, and that themulti-agent formulation strictly reduces policy gradient variance in RL.Extensive experiments demonstrate the superiority of Spire.

Real-world object removal is challenging due to two key dif-ficulties: the target object’s non-local effects (e.g., shadows and reflec-tions) that are hard to model, and the fact that user-provided masksare often inaccurate or incomplete. With billions of parameters and tensof denoising steps, diffusion-based models achieve this goal at the ex-pense of massive computational cost, limiting their use in interactiveapplications and edge devices. To solve this problem, we present OSOR(One-Step Object Removal), which achieves efficient, effect-aware, andmask-robust object removal at the same time. Concretely, OSOR in-troduces (1) an occupancy-guided discriminator for precise boundarysupervision, enabling stable single-step diffusion training, (2) an alphahead, which leverages the knowledge in diffusion models to predict cor-rect removal regions with minimal overhead, handling imperfect masks,and (3) a semantic-anchored verification pipeline (SAVP) that filtersnoisy instruction-based triplets to produce effect-aware supervision atscale. Using SAVP, we curate CORNE with 280K verified removal pairsand further annotate AnimeEraseBench and TextEraseBench to evalu-ate model performance in more complex tasks. Experiments show thatOSOR surpasses strong multi-step diffusion baselines in perceptual qual-ity while achieving 4× to 30× faster inference. Code and resources areavailable at https://github.com/Zhouqm-Git/osor.

Label noise is a major challenge in large-scale supervised face recognition, where weak or automatic annotations often introduce errors that mislead training. To address this, we propose a noise robust framework that performs noise detection and sample selection directly in cosine-similarity space. We observe that the non-target cosine similarities of clean samples share a highly consistent distribution profile with the target similarities of unfitted mislabeled samples. This phenomenon can be explained from a backpropagation perspective and provides a cue for monitoring label noise during training. Based on this observation, we formulate noise detection as boundary estimation in similarity space. Specifically, we track the upper bound of high-confidence clean nontarget similarities to determine the filtering threshold, without requiring prior knowledge of the noise rate or auxiliary networks. We further introduce a progressive rule during early training, where the threshold gradually increases from the estimated noise lower bound to the upper bound. This process discards unreliable samples while retaining hard but clean samples. Extensive experiments on eight synthetic and three real-world noisy datasets demonstrate that our method achieves superior noise detection and state-of-the-art recognition accuracy, with only about 0.3% average measured overhead. The filtered dataset produced by our method is also beneficial for subsequent training. Source code is available at https://github.com/wfl95/DDLN.

Vision-Language Models (VLMs) improve generalization andinterpretability in autonomous driving but suffer from efficiency issuesdue to long visual token sequences, particularly in standard multi-viewsettings. Existing token pruning methods employ fixed pruning rate al-location and static importance metrics, ignoring dynamic inter-view im-portance differences and the evolving information importance during in-ference. Our analysis reveals that multi-view VLMs inherently encodetask-related view priors in deeper layers and exhibit dynamic informa-tion requirements. Motivated by these findings, we propose MVPruner,a two-stage adaptive token pruning method that aligns pruning behaviorwith the model’s dynamic information requirements. The first stage al-locates pruning budgets based on the information diversity of each view,and retains tokens with consistent contribution across stages, ensuringsemantic representational capacity. The second stage allocates budgetsand selects tokens guided by instruction text to guarantee task align-ment. Experimental results on four benchmarks demonstrate the supe-rior performance of our method. For example, DriveMM equipped withMVPruner achieves 87.3% reduction in FLOPs, 4.97× speedup in pre-filling phase while retaining 98.5% accuracy on DriveLM benchmark.

High-fidelity 3D generative modeling increasingly relies onthe latent diffusion paradigm, where the reconstruction quality of theunderlying 3D VAE becomes a primary bottleneck. Existing approacheslargely follow two paradigms: sparse voxel-based representations achievestrong reconstruction quality but incur significant memory and compu-tational overhead, while set-based representations are compact and con-tinuous yet typically lag in fidelity due to latent sparsity and excessiveglobal smoothness. We propose MSVS-VAE, a hierarchical set-based VAEthat closes this fidelity gap without sacrificing compactness. Our keyidea is to progressively densify anchored VecSet latents via hierarchicalpoint-shuffle upsampling, increasing spatial capacity for fine-grained ge-ometry modeling. To efficiently decode from the densified hierarchy, wereplace global cross-attention with AVS-Conv, a geometry-aware localaggregation operator operating within local neighborhoods rather thanthe exhaustive latent set. We further introduce multi-scale query decod-ing to fuse coarse-to-fine latent features, where coarse scales providestable global context, and fine scales refine localized geometry, reducingartifacts from overly local receptive fields. Extensive experiments on Ob-javerse, ABO, and in-the-wild benchmarks demonstrate that MSVS-VAEconsistently outperforms prior set-based and voxel-based VAEs, deliver-ing ∼10× faster decoding than prior set-based methods and ∼10× highercompactness than voxel-based baselines.

Converting static 3D meshes into interactable articulated as-sets is crucial for embodied AI and robotic simulation. However, exist-ing zero-shot pipelines struggle with complex assets due to a criticallack of physical grounding. Specifically, ungrounded Vision-LanguageModels (VLMs) frequently suffer from kinematic hallucinations, whileunconstrained joint estimation inevitably leads to catastrophic meshinter-penetration during physical simulation. To bridge this gap, we pro-pose MotionAnymesh, an automated zero-shot framework that seam-lessly transforms unstructured static meshes into simulation-ready dig-ital twins. Our method features a kinematic-aware part segmentationmodule that grounds VLM reasoning with explicit SP4D physical priors,effectively eradicating kinematic hallucinations. Furthermore, we intro-duce a geometry-physics joint estimation pipeline that combines robusttype-aware initialization with physics-constrained trajectory optimiza-tion to rigorously guarantee collision-free articulation. Extensive experi-ments demonstrate that MotionAnymesh significantly outperforms state-of-the-art baselines in both geometric precision and dynamic physicalexecutability, providing highly reliable assets for downstream applica-tions. Our project page is available at https://xwb0117.github.io/Motionanymesh/

Personalized object detection aims to adapt a general-purposedetector to recognize user-specific instances from only a few examples.Lightweight models often struggle in this setting due to their weak se-mantic priors, while large vision-language models (VLMs) offer strongobject-level understanding but are too computationally demanding forreal-time or on-device applications. We introduce MOCHA (Multi-modalObjects-aware Cross-arcHitecture Alignment), a distillation frameworkthat transfers multimodal region-level knowledge from a frozen VLMteacher into a lightweight vision-only detector. MOCHA extracts fusedvisual and textual teacher’s embeddings and uses them to guide stu-dent training through a dual-objective loss that enforces accurate localalignment and global relational consistency across regions. This processenables efficient transfer of semantics without the need for teacher modi-fications or textual input at inference. MOCHA consistently outperformsprior baselines across four personalized detection benchmarks under strictfew-shot regimes, yielding a +10.1 average improvement, with minimalinference cost.

Generative novel view synthesis faces a fundamental dilemma:geometric priors provide spatial alignment but become sparse and inaccu-rate under view changes, while appearance priors offer visual fidelity butlack geometric correspondence. Existing methods either propagate geo-metric errors throughout generation or suffer from signal conflicts whenfusing both statically. We introduce MoCam, which employs structureddenoising dynamics to orchestrate a coordinated progression from geom-etry to appearance within the diffusion process. MoCam first leveragesgeometric priors in early stages to anchor coarse structures and toleratetheir incompleteness, then switches to appearance priors in later stagesto actively correct geometric errors and refine details. This design natu-rally unifies static and dynamic view synthesis by temporally decouplinggeometric alignment and appearance refinement within the diffusion pro-cess. Experiments demonstrate that MoCam significantly outperformsprior methods, particularly when point clouds contain severe holes ordistortions, achieving robust geometry-appearance disentanglement.

Multimodal Large Language Models (MLLMs) have demon-strated promising spatial reasoning capabilities, while these abilities re-main underexplored in the emerging visual modality of panoramic im-agery. The full 360◦ ×180◦ field of view of panoramas essentially supportscomplex global multi-step reasoning, which is also the fundamental ad-vantage of panoramas in applications such as embodied intelligence. How-ever, existing panoramic benchmarks largely focus on simplistic queriesthat rely on local cues or single-/few-step reasoning, thereby ignoring thefundamental advantage of panoramas and failing to fully exploit theirpotential. To address this gap, we introduce OmniCoT , a panoramicspatial reasoning suite designed to enable MLLMs to use global evi-dence and perform multi-step inference across viewpoints. It includesOmniCoT-B (6.7K data) for evaluation, which measures both answeraccuracy and reasoning quality, OmniCoT-Real (1K data) as a man-ually annotated real-world subset to quantify the Sim-to-Real gap. Fortraining, OmniCoT-T (14.3K data) is purpose-built with structuredstepwise Chain-of-Thought annotations that explicitly link intermediatereasoning steps to panoramic evidence. Based on OmniCoT-T, we intro-duce OmniCoT-R1 and adopt a two-stage training strategy tailoredto the geometrically complex panoramic space, where Supervised Fine-Tuning (SFT) anchors reasoning to panoramic evidence (e.g., bearings,proximity) and GRPO penalizes geometrically incoherent paths to con-solidate global 360◦ spatial consistency. Through OmniCoT, we aim torecalibrate the difficulty of panoramic spatial reasoning to bet-ter align with the intrinsic capabilities of panoramic imagery ,thereby fostering meaningful progress in this research area.

Autoregressive (AR) models deliver high-quality image gen-eration but suffer from severe inference latency due to sequential decod-ing. While speculative decoding (SD) successfully accelerates large lan-guage models, we demonstrate that state-of-the-art methods like EAGLE-3 provide limited gains for visual AR models. We attribute this to theintrinsic nature of visual tokens: unlike text, their probability distribu-tions are flat and non-discriminative, which hinders drafter–target align-ment and causes verification to collapse under sampling. To bridge thisgap, we propose LISA, a Locality-Informed Speculative framework forAutoregressive image generation. LISA overcomes alignment barriers viatwo key components: (i) Locality-Informed Distillation, which alignsthe drafter by prioritizing supervision on structured uncertainty; and(ii) Geometry-Aware Soft Verification, which leverages embeddingproximity and target confidence to enable more permissive yet safe to-ken acceptance. Experiments across T2I and C2I tasks demonstrate thatLISA improves the speed–quality trade-off, achieving up to 4.12× accel-eration and outperforming recent competitive methods.