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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

Geometry-Anchored Transport Framework for Exemplar-Free Class-Incremental Learning

Hongye Xu, Bartosz Krawczyk

Exemplar-free class-incremental learning (EFCIL) requires stable decision boundaries within a shifting feature space. While maintaining class-conditional Gaussian statistics provides a principled classification strategy, these parametric summaries remain sensitive to anisotropic representation drift. Existing methods often transport these statistics across tasks using a decoupled, post-hoc paradigm: optimizing a backbone without explicit geometric constraints can distort the legacy manifold, limiting the precision of retroactive alignment. In this paper, we formulate feature transport as an endogenous training constraint rather than a separate post-task step, presenting the Geometry-Anchored Transport Framework. First, we derive an Analytic Geometric Anchor via Mahalanobis-aligned regression to mitigate macroscopic anisotropic drift. Second, we introduce a Topology-Aware Evolution objective that regularizes localized manifold degradation while calibrating a residual network against the analytic prior. By coupling manifold evolution with transport constraints during the primary training phase, our framework mitigates evaluation errors without requiring decoupled fine-tuning. Experiments across CIFAR-100, TinyImageNet, and ImageNet-100 demonstrate that the proposed framework consistently improves upon existing post-hoc alternatives under strict exemplar-free constraints. The code is available at https://github.com/HXuSz11/GATF_ECCV2026.

ECCV 2026

RegHead: Non-Humanoid Head Blendshapes via Feed-Forward Registration

Jiahao Luo, Hao Zhang, Jianqi Chen, Yijie He, Jiaxu Zou, Michael Vasilkovsky, Sergei Korolev, Sergey Tulyakov, Chaoyang Wang, Peter Wonka, James Davis, Jian Wang

We present RegHead, a framework for constructing semanticblendshape sets for animatable non-humanoid head avatars. With a fixedexpression vocabulary, semantic blendshapes provide a low-dimensionaland interpretable animation interface and support cross-identity retar-geting. Building such blendshape sets remains expensive because (i)expression-consistent supervision is scarce, (ii) generated 4D assets typ-ically lack correspondence, and (iii) facial motion is highly localized.We propose (1) a large-scale dataset of non-humanoid identities pairedwith a shared expression vocabulary, obtained by expanding a smallartist-rigged library via fine-tuned image editing; (2) a dense stochasticanchor motion representation tailored to localized facial deformations;and (3) a fast feed-forward registration model that converts unregis-tered expression meshes into a corresponded blendshape basis by pre-dicting anchor-based deformations from the neutral shape. Experimentsshow that our approach produces higher-fidelity expression meshes thanbaselines, while running orders of magnitude faster than optimization.We further demonstrate real-time retargeting from human face trackingsignals to non-humanoid characters, capturing both head pose and lo-calized facial motions. Our project page is available at https://snap-research.github.io/RegHead/.

ECCV 2026

Monocular Avatar Reconstruction via Cascaded Diffusion Priors and UV-Space Differentiable Shading

Hong Li, Minqi Meng, Yanjun Liang, Chongjie Ye, Houyuan Chen, Weiqing Xiao, Xianda Guo, Guojun Lei, Xuhui Liu, Chaojie Yang, Yanlun Peng, Hao Zhao, Baochang Zhang

Reconstructing high-fidelity, relightable 3D avatars from asingle in-the-wild image is a challenging ill-posed problem, primarilyhindered by the scarcity of high-quality PBR data and the complex-ity of disentangling illumination from intrinsic materials. In this paper,we present a data-efficient framework that leverages the robust priorsof a unified pre-trained diffusion backbone to sequentially address tex-ture completion, delighting, and material decomposition. Unlike exist-ing methods that rely on fragmented pipelines or extensive proprietarydatasets, we utilize cascaded Low-Rank Adaptations (LoRAs) to adaptthe strong generative prior of the diffusion model for each sub-task inUV space. Specifically, we first employ an Inpainting LoRA to completemissing UV textures caused by occlusion, leveraging the model’s seman-tic understanding to generate semantically and photometrically coher-ent details. Subsequently, a Light-Homogenization LoRA and a novelCross-Intrinsic Attention mechanism are introduced to remove baked-inlighting and collaboratively synthesize pixel-aligned PBR maps (Albedo,Normal, Roughness, Specular, and Displacement). To ensure physicalplausibility, we impose a UV-space differentiable BRDF shading lossduring the decomposition stage, forcing the generative process to adhereto the rendering equation without the artifacts typical of rasterization-based supervision. Extensive experiments demonstrate that our method,trained on fewer than 100 real 3D scans, generates comprehensive, 4K-resolution PBR assets with superior realism and generalization comparedto state-of-the-art methods, and all training code and model weights willbe released upon acceptance.

ECCV 2026

GraphVid: Interactive Graph-Controllable Video Generation

Vedant Shah, Onkar Susladkar, Tushar Prakash, Kiet Nguyen, Tianjiao (Joey) Yu, Adheesh Juvekar, Muntasir Wahed, Ismini Lourentzou

Controllable video generation remains challenging due tothe di"culty of specifying precise multi-object interactions using textprompts or motion-control inputs that primarily constrain pixel move-ment. In practice, trajectory-based control often requires users to drawaccurate tracks for multiple objects, which scales poorly with scene com-plexity and becomes ambiguous under occlusion or overlap. To enableflexible yet precise multi-subject control, we introduce GraphVid, agraph-conditioned image-to-video generation model that enables inter-active control through structured interaction graphs. We further cu-rate GraphVid-Bench, a large-scale interaction-centric video datasetwith structured relational annotations to enable training of interaction-aware video generation models. Despite using substantially less trainingdata and fewer trainable parameters than prior motion-control methods,GraphVid delivers strong controllability and video quality. Comparedwith Motion-I2V, GraphVid reduces FID by up to 39.9% and FVD by37.6%, while improving PSNR (9.87→15.98) and SSIM (0.38→0.61). Ourresults highlight the potential of structured semantic interfaces as a pow-erful paradigm for controllable video generation.PLAN Lab https://plan-lab.github.io/graphvid

ECCV 2026

HAT-4D: Lifting Monocular Video for 4D Multi-Object Interactions via Human-Agent Collaboration

Jiaxin LI, Yuxiang Wu, Zhenkai Zhang, Xinrui Shi, wang haoyuan, Yichen Zhao, Su Linxiang, Chenyang chenyang, Mingyu Zhang, Yifan Ding, Boran Wen, Li Zhang, Ruiyang Liu, Yong-Lu Li

Extracting dynamic 4D object interactions from massive, inthe-wild monocular videos offers a highly efficient data collection pathway for scaling Embodied AI and training VLAs. However, existing monocular 4D reconstruction methods primarily focus on isolated objects, often failing under the severe occlusions and complex dynamics inherent in multi-object interactions. To bridge this gap, we propose HAT- 4D, the first agentic framework designed to reconstruct the 3D geometry, temporal dynamics, and physical interactions of multiple objects from a single video. By integrating VLMs with a multi-level human-in-the-loop feedback mechanism, HAT-4D efficiently resolves depth ambiguities and interaction-induced occlusions during 3D generation and 4D propagation, yielding physically plausible assets without relying on expensive multicamera rigs. As a scalable data engine, HAT-4D facilitates the creation of MVOIK-4D, an open-world benchmark for monocular 4D interaction reconstruction, accompanied by a novel multi-dimensional evaluation protocol focused on physical plausibility and temporal consistency. Extensive experiments demonstrate that HAT-4D achieves SOTA performance on most evaluation metrics, while maintaining competitive semantic alignment. Ablation studies show that introducing a small amount of human feedback improves interaction reconstruction. Moreover, the data produced by HAT-4D effectively improves baseline performance when used for finetuning. Our data and code are available at project webpage.

ECCV 2026

Retrieving and Refining Winning Noise Tickets for Diffusion-Based Motion Generation

Sakuya Ota, Qing Yu, Kent Fujiwara, Satoshi Ikehata, Ikuro Sato

Diffusion-based text-to-motion models synthesize realistic hu-man motions but often exhibit semantic drift from the input text. Mo-tion is inherently temporal, especially in compositional and long-durationsequences that require semantic consistency across multiple action seg-ments and smooth kinematic transitions throughout the trajectory. Weposit that the initial noise is central to this consistency: within the Gaus-sian noise space, certain instances, i.e. winning noise tickets, carry latentstructure that biases denoising toward particular motion semantics, evenunder null prompts. We propose WInning Noise Retrieval and Optimiza-tion (WINRO), a training-free, model-agnostic framework that improvestext–motion alignment by selecting and refining such tickets before diffu-sion sampling. WINRO maps random noises to motion features generatedunder null prompts, retrieves the best-aligned noise for a given text, andrefines it via a KL-regularized objective that reduces the residual seman-tic gap while preserving the Gaussian prior. An optional LoRA-basedadapter amortizes this refinement into a single forward pass. WINROconsistently improves text–motion fidelity across different base models,MDM and MotionLCM, on HumanML3D without retraining, improvestemporal robustness on the MTT benchmark, and generalizes to appli-cations such as motion stylization and spatial constraint satisfaction.

ECCV 2026

Monocular Models are Strong Learners for Multi-View Human Mesh Recovery

Haoyu Xie, Shengkai Xu, Cheng Guo, Muhammad Saleem, Wenhan Wu, Chen Chen, Ahmed Helmy, Pu Wang, Hongfei Xue

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. • •

ECCV 2026

REALM: An RGB and Event Aligned Latent Manifold for Cross-Modal Perception

Vincenzo Polizzi, David Lindell, Jonathan Kelly

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/.

ECCV 2026

Gripper-aware Vision Language Action Models

Hanyi Zhang, Zihong Luo, Tianyu Li, Khang Nguyen, Basu Hela, Shreyas Kumar, Ngoc Tran, Feng Dai, Charith Munasinghe, Jorge Queralta, Giovanni Toffetti, Khoa Vo, Ngan Le, Ravi Prakash, Quan Vuong, Tung Ta, Long Hu, Anh Nguyen, Baoru Huang

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.

ECCV 2026

MotionSplicer: Part-Based Motion Editing for 4D Volumetric Videos

Chaerin Min, Praccho Muna-McQuay, Tao Lu, James Tompkin, Srinath Sridhar

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.

ECCV 2026

Wavelet-based Intra-video Counterfactual Reasoning for Video Question Grounding

Jiasheng Yuan, Wei Wei

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.

ECCV 2026

Multi-modal Knowledge Preserving Adapter for Embedding Backward Compatibility

Jaeseok Byun, Gukyeong Kwon, Han-Kai Hsu, Meher Gitika Karumuri, Zhikang Zhang, Hao Yang, Davide Modolo

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.

ECCV 2026

TecoPrompt: Temporal-Conservative Prompt Learning for Vision-Language Models

zeyi shao, Haowen Hua, Jiaxin Zhang, John See, Zeyd Boukhers, Cong Yang

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.

ECCV 2026

Stokes-Informed Diffusion for Robust Linear Polarization Estimation

Yidong Luo, Chenggong Li, Yuchao Feng, Boxin Shi, Junchao Zhang, Xin Yuan

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.

ECCV 2026

Efficient Camera Pose Augmentation for View Generalization in Robotic Policy Learning

Sen Wang, Huaiyi Dong, Jingyi Tian, lijiayi lijiayi, Zhuo Yang, Tongtong Cao, Anlin Chen, Shuang Wu, Sanping Zhou, Le Wang

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.

ECCV 2026

OpenGround: Planning-based Online Perception for Open-World 3D Visual Grounding

Wenyuan Huang, Zhenyu Zhang, Zhao Wang, Zhou Wei, Ting Huang, Fang Zhao, Jian Yang

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.

ECCV 2026

GeoEdit: Geometry-Aware Object Editing via Dual-Branch Denoising

Yi He, Jiangming Wang, Xinyu Wang, Mark Fong, Songchun Zhang, Yuxuan Xue, Hai-Tao Zheng, Yue Ma

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.

ECCV 2026

ActiveStructure: Plane Scene Graph-Guided Active 3D Gaussian Splatting

Yingzhao Li, Yan Li, Yanjie Liu, Hanyu Zhou, Lijun Zhao, Gim Hee Lee

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.

ECCV 2026

Rethinking Robust Adversarial Concept Erasure in Diffusion Models

Qinghong Yin, Yu Tian, Heming Yang, Xiang Chen, Xianlin Zhang, Yue Ming, Xueming Li, Yue Zhang

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.

ECCV 2026

RePL: Pseudo-label Refinement for Semi-supervised LiDAR Semantic Segmentation

Donghyeon Kwon, Taegyu Park, Suha Kwak

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.

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