ECCV 2026 论文列表

Multi-view human mesh recovery (HMR) is broadly deployed in diverse domains where high accuracy and strong generalization are essential. Existing approaches can be broadly grouped into geometry-based and learning-based methods. However, geometry-based methods (e.g., triangulation) rely on cumbersome camera calibration, while learningbased approaches often generalize poorly to unseen camera con(cid:28)gurations due to the lack of multi-view training data, limiting their performance in real-world scenarios. To enable calibration-free reconstruction that generalizes to arbitrary camera setups, we propose a training-free framework that leverages pretrained single-view HMR models as strong priors, eliminating the need for multi-view training data. Our method (cid:28)rst constructs a robust and consistent multi-view initialization from single-view predictions, and then re(cid:28)nes it via test-time optimization guided by multi-view consistency and anatomical constraints. Extensive experiments demonstrate state-of-the-art performance on standard benchmarks, surpassing multi-view models trained with explicit multiview supervision. • •

Event cameras provide several unique advantages over standard frame-based sensors, including high temporal resolution, low latency, and robustness to extreme lighting. However, existing learningbased approaches for event processing are typically confined to narrow, task-specific silos and lack the ability to generalize across modalities. We address this gap with REALM, a cross-modal framework that learns an RGBand Event-Aligned Latent Manifold by projecting event representations into the pretrained latent space of RGB foundation models. Instead of task-specific training, we leverage low-rank adaptation (LoRA) to bridge the modality gap, effectively unlocking the geometric and semantic priors of frozen RGB backbones for asynchronous event streams. We demonstrate that REALM effectively maps events into the ViT-based foundation latent space. Our method performs downstream tasks, such as depth estimation and semantic segmentation, by simply transferring linear heads trained on the RGB teacher. Most significantly, REALM enables the direct, zero-shot application of complex, frozen image-trained decoders, such as MASt3R, to raw event data. We demonstrate stateof-the-art performance in wide-baseline feature matching, significantly outperforming specialized architectures. Code and models are available at https://papers.starslab.ca/realm/.

Vision language action models (VLAs) have advanced general purpose robotic grasping and manipulation by enabling robots to interpret visual observations and natural language instructions to generate executable action sequences. However, existing VLAs often implicitly assume gripper invariance, despite grasping strategies being inherently embodiment-dependent. Different gripper types, such as parallel-jaw and suction, usually require distinct interaction strategies to achieve the same grasping objective. Moreover, current datasets for VLAs predominantly rely on parallel-jaw grippers, limiting gripper-aware learning. To address this gap, we introduce MiGA, a multi-gripper-aware dataset spanning five distinct gripper types across multiple robots with 103,000 demonstrations, explicitly capturing strategy divergence under shared task objectives. We further propose GVLA, which combines a new multi-gripper tokenizer with adapter-based policy routing. Our new gripper encoding induces structured embedding information that balances parameter sharing and strategy differentiation, while layer-wise probing confirms meaningful gripper-conditioned representations for VLAs. Intensive experiments in both simulation and real-world robots show that our GVLA outperforms the current baselines across evaluated settings. Our method also improves zero-shot generalization or few-shot adaptation to new objects or unseen tasks, and enable more efficient gripper adaptation.

4D volumetric video via Gaussian Splatting (GS) enables free viewpoint control of real-world scenes but does not enable motion editing. One challenge is achieving part-based control in multi-object scenes; prior works focus on single objects, assume rigid parts, or require manual annotations. MotionSplicer is a template-free and annotation-free method that automatically discovers 3D parts in GS. To bridge the semanticto-motion domain gap, it extracts initial parts from temporal difference images and resolves multi-view inconsistencies via an adaptive 3D unifyand-split process. For robust tracking, it optimizes a time-invariant feature grid with temporal refinement to learn spatially and temporally coherent skinning weights. Experiments show that MotionSplicer can be applied more generally to objects and scenes than state-of-the-art methods, and enables creative motion edits like isolation, ghosting, and echo.

Video Question Grounding (VideoQG) requires models toanswer questions and localize supporting temporal evidence. Despiteprogress in video-language models, they remain susceptible to variousbiases. Prior debiasing methods primarily focus on dataset-level correla-tions, while easily overlooking intra-video temporal bias, where visuallysimilar segments differ substantially in their causal relevance to answer-ing the question. To address this challenge, we present Wavelet-basedIntra-video Causal Intervention (WICI), a framework designed to dis-entangle causal temporal evidence from redundant visual contexts. OurWICI comprises two key components: (1) an Intra-video Causal Interven-tion module, which utilizes counterfactual reasoning to suppress irrele-vant intra-video biases, and (2) Wavelet-based Dynamic Modeling, whichdecomposes contextual variations between visually similar frames to cap-ture fine-grained temporal cues. Extensive experiments on two VideoQGbenchmarks demonstrate that the proposed WICI significantly improvesquestion grounding accuracy and yields more robust, faithful questionreasoning.

Upgrading embedding models typically requires expensivedatabase re-indexing, as new query embeddings are incompatible withexisting database embeddings. While Backward Compatible Training(BCT) mitigates this by enforcing compatibility during training, ex-isting approaches often require updating the backbone model. This isimpractical because of significant training cost, the risk of performanceregression, and limited access to proprietary model weights. We intro-duce Multi-modal Knowledge Preserving Adapter (MKP-Adapter),the first adapter-only BCT approach for Multi-Modal Large LanguageModels (MLLMs) that requires no backbone updates. We identified thatthe primary challenge in adapter-only BCT is preserving the knowledgeof the new embeddings while enforcing backward compatibility. Hence,we propose a multi-level preservation loss that maintains the geometricstructure of the embedding spaces throughout BCT. Furthermore, a focalre-weighting strategy is integrated to prioritize learning from challeng-ing samples. Experiments demonstrate that our method achieves strongbackward compatibility across diverse multi-modal benchmarks (image,text, visual document, and video retrieval tasks) and model types. No-tably, MKP-Adapter is trained solely on pre-extracted embeddings andrequires only negligible additional latency relative to the original back-bone forward pass, highlighting its efficiency.

Prompt learning adapts vision-language models, such as CLIP,by adjusting a small set of context tokens. However, under few-shot su-pervision, even moderate label noise can disrupt prompt optimization. Toaddress this issue, we propose TecoPrompt, a closed-loop robust prompt-learning framework that revisits optimal transport (OT) pseudo-labelingfrom a temporal perspective. TecoPrompt employs an entropic OT planin the CLIP semantic space to obtain globally consistent label candidates.It verifies the reliability of these candidates by examining trajectorystability: a noisy label is only rewritten if the OT candidate remainsunchanged within a K-epoch temporal stability window and passes a con-fidence gate based on Exponential Moving Average (EMA). This approachhelps reduce confirmation bias. The rewritten labels are then integratedback into prompt training using a tri-group objective that includes threeloss functions aligned with clean, mid, and noisy subsets. Experimentson seven datasets with synthetic symmetric and asymmetric noise, aswell as Food101N, demonstrate significant performance improvements.For example, on the OxfordPets dataset, with 50% asymmetric noise,TecoPrompt achieves an accuracy of 0.843, up from 0.775.

Polarization cues benefit applications such as material de-tection and de-reflection, yet acquiring them typically requires dedicatedhardware. This motivates us to estimate the linear polarization from asingle RGB image. However, the task is inherently ill-posed, with theAngle of Polarization (AoP) becoming particularly unstable in weak-polarization regions, where the polarimetric signal is overwhelmed bynoise, leading to erratic angle estimates. To address these limitations, wepropose GenPolar, a Stokes-informed diffusion framework grounded inthe Mueller formalism from an intensity observation. Specifically, Gen-Polar predicts channel-wise linear Stokes components (S1 , S2 ) from inten-sity S0 , from which degree of linear polarization (DoLP) and AoP are an-alytically derived; AoP is further supervised with an observability-awareloss. In addition, to enable efficient and high-fidelity inference, we adopta two-stage training strategy. Firstly, a multi-step conditional diffusionmodel is trained with a physics-based loss. Subsequently, we distill it intoa one-step generator, which further supports stable Low-Rank Adapta-tion (LoRA) of the VAE encoder to mitigate domain-specific autoen-coding bias. Extensive experiments across rotating-polarizer, division-of-focal-plane, and hybrid datasets demonstrate that GenPolar achievesstate-of-the-art performance in both DoLP fidelity and AoP stability.Crucially, these improvements translate to significant and consistent gainsin downstream applications, including material detection and de-reflection.

Prevailing 2D-centric visuomotor policies exhibit a pronounceddeficiency in novel view generalization, as their reliance on static observa-tions hinders consistent action mapping across unseen views. In response,we introduce GenSplat, a feed-forward 3D Gaussian Splatting frameworkthat facilitates view-generalized policy learning through novel view ren-dering. GenSplat employs a permutation-equivariant architecture to re-construct high-fidelity 3D scenes from sparse, uncalibrated inputs in asingle forward pass. To ensure structural integrity, we design a 3D-priordistillation strategy that regularizes the 3DGS optimization, preventingthe geometric collapse typical of purely photometric supervision. By ren-dering diverse synthetic views from these stable 3D representations, wesystematically augment the observational manifold during training. Thisaugmentation forces the policy to ground its decisions in underlying 3Dstructures, thereby ensuring robust execution under severe spatial per-turbations where baselines severely degrade.

3D visual grounding aims to locate objects based on naturallanguage descriptions in 3D scenes. Existing supervised methods are lim-ited by generalization and recent zero-shot methods typically rely on apredefined Object Lookup Table (OLT) to query Visual Language Mod-els (VLMs) for reasoning about object locations via a single step ground-ing, which limits the applications in scenarios with undefined targets andcomplex queries. To address these problems, we present OpenGround, anovel zero-shot framework for open-world 3D visual grounding that re-mains compatible with recent zero-shot methods. OpenGround integratesTask-Chain Planning to decompose a query into a plan of context-to-target sub-goals for progressive grounding, and Context-Guided Percep-tion to perceive novel objects online under context guidance from thetask chain. We also propose a new dataset named OpenTarget, whichcontains over 7000 object-description pairs to mimic open-world eval-uation. Extensive experiments demonstrate that OpenGround achievescompetitive performance on Nr3D, state-of-the-art on ScanRefer, anddelivers a substantial 17.6% improvement on OpenTarget.

Precisely manipulating objects in a single photograph (trans-lation, rotation, scaling) while obeying 3D physical constraints remainsunsolved for diffusion-based editors. Current 2D methods lack spatialawareness and produce perspective violations. Forcing structural proxiesinto the latent space also disrupts variance homogeneity, and the result-ing self-attention leakage leads to ghosting and background blur. Thecore difficulty is asymmetric: the relocated object must follow a rigidgeometry, yet the uncovered background needs freedom to synthesizeplausible content. We present GeoEdit, a training-free Lift-Manipulate-Render-Denoise pipeline that satisfies both constraints. We decouplescene and object in 3D, align them through point correspondence, andrender a geometry-aligned proxy with a structural depth map. A Dual-Branch Denoising stage then refines this proxy: a video diffusion back-bone preserves object identity, while 3D constraints are injected into theforeground within a narrow denoising window at matching noise vari-ance (variance-homogeneous injection). The background denoises freely.Because the injected signal matches the native latent statistics, self-attention stays undisturbed. We also introduce GeoEditBench, a pose-aware benchmark covering object translation, object rotation, and cam-era movement with pose-aware evaluation metrics. Experiments confirmconsistent gains in geometric accuracy, identity fidelity, and backgroundquality, validated by automatic metrics and human studies. Code anddata are publicly available at https://github.com/Heey731/GeoEdit.

Active 3D Gaussian Splatting methods select views usingfactorised per-pixel metrics, which cannot represent relationships be-tween surfaces. Incompleteness that arises from unverified geometric re-lationships therefore goes undetected. We present ActiveStructure,which lifts information gain from pixels to scene-level relational topol-ogy through three innovations: (1) a Plane Scene Graph whose topolog-ical health, captured through node connectivity, edge completeness, andangular consistency, quantifies a form of structural completeness thatno pixel-level criterion can reach; (2) a gain decomposition that splitsthe value of a viewpoint into a rendering-fidelity term and a structural-integrity term that can be optimised independently; (3) informationalpolarity reversal, which lets scaffold planes switch from exploration at-tractors to suppressors and provides state-driven phase control tied tostructural objectives. Experiments on Replica and Matterport3D demon-strate state-of-the-art reconstruction accuracy and rendering quality.

Concept erasure methods aim to remove specific unsafe tar-get concepts in diffusion models while preserving image generation utility.To address the vulnerability that erased concepts can be easily recoveredunder adversarial attacks, adversarial concept erasure methods integrateadversarial optimization into the concept erasure process. However, exist-ing adversarial concept erasure methods face a trade-off between robust-ness and computational cost. We attribute this to adversarial optimiza-tion techniques that use random samples to approximate the adversarialobjective function. Adversarial optimization that uses a small numberof samples fails to produce adversarial embeddings that accurately cap-ture the target concept space. To mitigate this limitation, we proposeSemantic-Guided Adversarial Optimization, which uses a single sampleto produce adversarial embeddings that better capture the target con-cept space. We also propose Semantic-Guided Concept Erasure, whichautomatically maps the target concept to a semantically similar surro-gate. Extensive experiments on not-safe-for-work content, artistic styles,and object-related concepts demonstrate that our method, S-GRACE(Semantic-Guided Robust Adversarial Concept Erasure) achieves state-of-the-art erasure robustness and superior image generation utility, withsignificantly lower computational cost than existing methods. Our codeis available at https://github.com/Qhong-522/S-GRACE.

Semi-supervised learning for LiDAR semantic segmentationoften suffers from error propagation and confirmation bias caused bynoisy pseudo-labels. To tackle this chronic issue, we introduce RePL, anovel framework that enhances pseudo-label quality by identifying andcorrecting potential errors in pseudo-labels through masked reconstruc-tion, along with a dedicated training strategy. We also provide a theoret-ical analysis demonstrating the condition under which the pseudo-labelrefinement is beneficial, and empirically confirm that the condition ismild and clearly met by RePL. Extensive evaluations on the nuScenes-lidarseg and SemanticKITTI datasets show that RePL improves pseudo-label quality substantially, and in consequence, achieves the state of theart in semi-supervised LiDAR semantic segmentation.

3D Gaussian Splatting (3DGS) enables real-time novel viewsynthesis for static scenes. Extending it to dynamic scenes via deforma-tion fields has recently attracted significant attention, particularly fordynamic scene reconstruction and distractor-free reconstruction. How-ever, existing deformation networks lack explicit motion awareness: theyneither capture long-term motion intensity nor exploit short-term tempo-ral coherence, leading to inaccurate foreground deformation and pseudo-static residuals in the background. We present MVFusion-GS, a methodthat enhances deformation networks with two complementary motion-aware mechanisms. The Motion-Variance Guided Refinement ag-gregates per-Gaussian deformation statistics across time to estimate mo-tion variance and uses it to guide dynamic-static separation during defor-mation prediction. The MotionFormer Temporal Attention moduleapplies Transformer self-attention over neighboring timesteps to modellocal motion dependencies and improve temporal consistency. Extensiveexperiments on both dynamic scene reconstruction and distractor-free re-construction benchmarks demonstrate state-of-the-art performance, show-ing that explicit motion awareness improves both foreground motionmodeling and static background reconstruction.

All-in-One medical image restoration (MedIR) aims to ad-dress diverse tasks across modalities and degradation types using a singleuniversal model. Existing methods typically prioritize modeling inter-task heterogeneity (e.g., distinct data distributions and degradation types).However, they largely neglect the inherent homogeneity present in med-ical images, such as widely shared anatomical structures within andacross modalities, which can be leveraged to ease model training andimprove generalization. To this end, we propose UniH3 , a novel frame-work that Unifies Hierarchical Homogeneity and Heterogeneity for all-in-one medical image restoration. Specifically, to comprehensively exploithomogeneity, we introduce a Hierarchical Homogeneity Memory (H2 M)module that progressively distills intra- and inter-task homogeneity pri-ors from high-quality images during training, and adaptively retrieves themost relevant priors tailored to the input for guided restoration. Theseretrieved priors are then injected into the restoration pipeline via an ef-ficient Homogeneity-Guided Attention (HGA) mechanism. Furthermore,to comprehensively address heterogeneity, we design a Hierarchical Het-erogeneity Balancer (H2 B) that mitigates both inter- and intra-task con-flicts during optimization, facilitating balanced and effective multi-tasklearning. Extensive experiments on two large-scale benchmarks—MedIR-2D-500K and MedIR-3D-3K—demonstrate that UniH3 achieves state-of-the-art performance on both all-in-one and single-task medical imagerestoration. We hope this work establishes a strong benchmark and ad-vances the development of general-purpose medical image restorationmodels. Code is available at https://github.com/Yaziwel/UniH3.

Reference-based inpainting utilizes the information in thereference image to inpaint the missing region in the target image, aimingto generate the fact object behind the mask rather than a reasonable one.The key challenge is how to accurately locate the region in the referenceimage and maximally utilize it. We proposed UNet-Twice, a simple yetnovel framework. UNet-Twice uses the same UNet two times, combinedwith a well-constructed data pair to build a close connection betweenthe target and reference image. The two passes fuse spatially comple-mentary features generated from the target and the reference image,which provides intuitively explicit guidance for image generation. Ourproposed framework is simple and easy to extend to a multi-referencesetting. Extensive experiments demonstrate that our proposed frame-work outperforms existing reference-based inpainting methods in bothsingle-reference and multi-reference settings.

We study open-vocabulary 3D indoor layout generation, whichsynthesizes diverse and physically plausible indoor scenes from unla-beled 3D assets given free-form language instructions. Recent text-guidedlayout generation methods leverage large language models (LLMs) andvision-language models (VLMs) to synthesize structured scenes directlyfrom text descriptions. However, most existing approaches model inter-asset relations implicitly or rely on local pairwise constraints during lo-cal optimization. Such formulations are misaligned with the global andhighly non-convex feasible layout space, often producing locally plausiblebut globally inconsistent or physically infeasible scenes. To address theselimitations, we introduce a graph-based intermediate representation thatdecouples semantic coherence and physical feasibility, and propose ahybrid search-and-refinement strategy to generate indoor layouts withglobal semantic consistency and physical feasibility. First, we proposeGlobal Semantic Verification (GSV), which represents scenes as struc-tured scene graphs and enforces semantic constraints through rule-basedgraph verification. This explicit structural validation prunes contradic-tory configurations and produces a globally consistent semantic scaffoldfor scene generation. Second, we introduce Global Physical FeasibilitySearch (GPFS), a hybrid optimization framework that combines evo-lutionary search for global exploration with gradient-based refinementfor local exploitation. GPFS reduces dependence on VLM-proposed ini-tialization and improves robustness in highly non-convex and discon-tinuous feasible spaces. Together, GSV and GPFS shift layout genera-tion from local relational modeling and initialization-sensitive optimiza-tion toward globally consistent reasoning and exploration. Experimentsdemonstrate that our method achieves state-of-the-art performance onopen-vocabulary 3D indoor layout generation, improving both semanticconsistency and physical plausibility.

Intrinsic image decomposition aims to estimate physicallybased rendering (PBR) parameters such as albedo, roughness, and metal-licity from images. While recent methods achieve strong single-view pre-dictions, applying them independently to multiple views of the samescene often yields inconsistent estimates, limiting their use in down-stream applications such as editable neural scenes and 3D reconstruc-tion. Video-based models can improve cross-frame consistency but re-quire dense, ordered sequences and substantial compute, limiting theirapplicability to sparse, unordered image collections. We propose Geo-ID, a novel test-time framework that repurposes pretrained single-viewintrinsic predictors to produce cross-view consistent decompositions bycoupling independent per-view predictions through sparse geometric cor-respondences that form uncertainty-aware consensus targets. Geo-ID ismodel-agnostic, requires no retraining or inverse rendering, and appliesdirectly to off-the-shelf intrinsic predictors. Experiments on syntheticbenchmarks and real-world scenes demonstrate substantial improvementsin cross-view intrinsic consistency as the number of views increases, whilemaintaining comparable single-view decomposition performance. We fur-ther show that the resulting consistent intrinsics enable coherent appear-ance editing and relighting in downstream neural scene representations.