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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

MVFusion-GS: Motion-Variance Guided Temporal Attention for High-Quality Dynamic Gaussian Splatting

Jianwei Hu, Tingxuan Huang, Hengyu Zhou, Ningna Wang, Xiaohu Guo, Jinshan Lai, Bin Wang

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.

ECCV 2026

UniH3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration

Zhiwen Yang, Jiayin Li, Chengyu Liu, Hui Zhang, Bingzheng Wei, Yan Xu

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.

ECCV 2026

UNet-Twice: A Simple Structured Reference-based Inpainting Framework

Junda Lu, zhiqiao xu, Houkun Wu, Wei Cui, Bo Huang, Mingyang Chen, Bing Li

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.

ECCV 2026

Global Graph-Validated Optimization for VLM-based 3D Indoor Scene Generation

Jialu Huang, Yingxuan You, Fei Wang, Zheng Dang

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.

ECCV 2026

Geo-ID: Test-Time Geometric Consensus for Cross-View Consistent Intrinsics

Alara Dirik, Stefanos Zafeiriou

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.

ECCV 2026

Auto-Prompting: Layer-Specific Prompt Fusion Discovery via Differentiable Search

Xi Xiao, Xingjian Li, Yunbei Zhang, Cheng Han, Tianming Liu, Tianyang Wang, Runmin Jiang, Jihun Hamm, Xiao Wang, Min Xu

Visual prompt tuning has emerged as a parameter-e!cientfine-tuning approach for adapting large-scale Vision Transformers (ViTs)to downstream tasks. As its learnable prompts are applied in input andfeature spaces, prior to jointly going through attention in transformerlayers, the most commonly used scheme for fusing image and prompttokens is concatenation or addition. In this paper, we aim to study afundamental yet essential problem in visual prompt tuning: whethera single fusion scheme tends to yield better results, and whether thatwould be beneficial to develop a hybrid fusion scheme. To this end,we formulate the task as a bi-level optimization problem, and solve itleveraging di"erentiable architecture search. In this context, the learnableprompts and their fusion schemes are jointly optimized. To enrich thesearch space in the architecture search, we propose two additional fusionschemes, namely, a!ne transformation and cross-attention, in additionto concatenation and addition. Extensive experiments on 34 datasetsspanning VTAB-1k, FGVC, and HTA show consistent gains over prompt-tuning baselines. With a frozen ViT backbone, our method delivers afavorable accuracy–latency–parameter trade-o" compared with VPT-Deep and recent variants. Our findings reveal that how prompts fusewith image tokens plays a significant role in visual prompt tuning, anda hybrid fusion fashion can more e"ectively leverage layer semantics ofViTs, contributing a novel perspective for visual prompt-tuning research.

ECCV 2026

Adversarial Score Distillation for Stable One-Step Diffusion in Real-World Image Super-Resolution

Wei Zhu, Kai Zhang, Yu Zheng, Lei Luo, Jian Yang

In recent years, super-resolution (SR) methods leveragingpre-trained text-to-image diffusion models have shown impressive po-tential. However, their deployment in real-world applications remainsconstrained by the computational overhead of numerous inference steps.Recent one-step approaches, such as OSEDiff and TSD-SR, mitigate thisissue via score distillation techniques. However, their discriminators areoptimized solely with the diffusion loss, resulting in weak and unstablegradients for the generator, making it difficult to preserve fine details andstructural fidelity. To tackle these limitations, we introduce ASDSR, anovel one-step distillation framework tailored for real-world image super-resolution. Our approach enhances Adversarial Score Distillation (ASD)by adopting a WGAN-based formulation with a stabilized penalty termthat utilizes the full discriminator loss to improve the distillation pro-cess. Furthermore, we incorporate CLIP-derived semantic priors to alignreconstructed images with their high-quality counterparts in the fea-ture space, effectively guiding the generator toward perceptual realism.Extensive experiments on multiple real-world benchmarks demonstratethat ASDSR achieves superior performance compared to state-of-the-artmethods, offering a practical and efficient solution for real-world imagerestoration.

ECCV 2026

MMEarth-Bench: Global Model Adaptation via Multimodal Test-Time Training

Lucia Gordon, Serge Belongie, Christian Igel, Nico Lang

Recent research in geospatial machine learning demonstratesthat models pretrained with self-supervised learning on Earth observa-tion data can perform well on downstream tasks with limited labeleddata. However, most benchmark datasets have few data modalities andpoor global representation, limiting the ability to evaluate multimodalpretrained models at global scales. In order to fill this gap, we introduceMMEarth-Bench, a collection of five new environmental tasks with 12modalities, globally distributed data, and both random and geographictest splits. We benchmark a diverse set of pretrained models and findthat while (multimodal) pretraining tends to improve model robustnessin limited data settings, geographic generalization abilities remain poor.Moreover, a simple randomly initialized multimodal model is competi-tive given enough labeled data. Although data is abundant, models cancurrently only make use of the modalities on which they were pretrained.To solve this problem, we propose using all the modalities available attest time as auxiliary tasks for test-time adaptation. Our model-agnosticmethod for test-time training with multimodal reconstruction (TTT-MMR) can improve performance across all models and tasks on bothtest splits. Furthermore, geographic batching leads to a good trade-off be-tween regularization and specialization during TTT, which is especiallybeneficial for long-tail distributions. Our dataset, code, and visualizationtool are linked on the project page: lgordon99.github.io/mmearth-bench.

ECCV 2026

Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples

Shiyu Wu, Shuyan Li, Jing Li, Jing Liu, Yequan Wang

AI-generated image (AIGI) attribution presents a pressingchallenge that goes beyond mere AIGI detection, aiming to identify thesource model or technique responsible for a synthetic image. However,most previous source attribution methods operate in a closed-set manner,which necessitates retraining to recognize any novel category, preventingadaptation to the rapid evolution of image generation. In this work, wepropose a new paradigm for synthetic image attribution, termed few-shotattribution. This paradigm targets the reliable identification of unseengenerators using only limited samples, making it highly suitable for real-world applications. To facilitate this work, we construct OmniFake, alarge-scale, well-categorized synthetic image dataset that contains 1.17million images from 45 distinct generators. We further introduce Om-niDFA (Omni Detector and Few-shot Attributor), a few-shot attributionbaseline that not only assesses the authenticity of images but also deter-mines their synthesis origins. Experiments demonstrate that OmniDFAexhibits excellent capability in few-shot attribution and achieves state-of-the-art generalization performance in AIGI detection. Our dataset andcode are available at https://github.com/teheperinko541/OmniDFA.

ECCV 2026

Event-based Sparse-view Background-Oriented Schlieren Tomography

Xinyu Zhou, Shihao Hu, Peiqi Duan, Chao Xu, Boxin Shi

Background-oriented schlieren (BOS) tomography reconstructs3D flow density fields from refraction-induced distortions observed fromone or more viewpoints. Capturing high-speed flows with frame-basedBOS typically requires high-speed cameras and bright illumination tocompensate for short exposure times. In this paper, we propose an event-based BOS tomography approach that reconstructs time-varying 4D den-sity fields from event streams. Leveraging the high temporal resolutionand high dynamic range of event cameras, the proposed approach enableshigh-speed airflow reconstruction under ambient lighting. We representthe spatiotemporal density field as a neural implicit field and render BOSobservations via refractive ray tracing. We utilize physics-informed regu-larization to improve reconstruction under sparse views. Experiments onsimulated and real data in single-view and orthogonal dual-view setupsdemonstrate accurate reconstructions.

ECCV 2026

Training-free Controllable Motion Generation under Heterogeneous Constraints

Xiaofei Hui, Bo Yan, Haoxuan Qu, Hossein Rahmani, Jun Liu

Training-free controllable motion generation has attracted growing interest for enabling flexible constraint enforcement without constraint-specific training. However, existing training-free methods require constraints to be continuous objective-based with differentiable losses, while many real-world requirements are criterion-based and provide only discontinuous, sparse, or even black-box feedback. In this paper, we propose Motion-Inference-as-Control (MIC), the first training-free motion generation framework that handles both continuous objective-based and criterion-based motion constraints under a shared mechanism. The key idea is to cast diffusion-based motion generation as a stochastic control problem. This perspective not only provides principled and practically effective step-wise control laws that support criterion-based constraints without requiring differentiability and naturally accommodate objectivebased constraints as a special case, but also motivates a control-oriented constraint coordination mechanism that adaptively balances and reconciles motion constraints during generation. Experiments across diverse constraint settings demonstrate the effectiveness of our framework.

ECCV 2026

Counting Trees from Satellite Imagery with Noisy Supervision

Dimitri Gominski*, Maurice Mugabowindekwe, Qiue XU, Xiaowei Tong, Martin Brandt, Hieu Le, Rasmus Fensholt, Dimitris Samaras, Loic Landrieu

Counting individual trees is a fundamental task for environ-mental monitoring, yet remains largely unexplored with satellite imagery.At these resolutions, isolated trees may still be identifiable, but crownboundaries become ambiguous in dense forests, making the notion of anindividual tree inherently ill-defined. Moreover, large-scale manual an-notations of individual trees are prohibitively expensive. While scalablesupervision can be derived from airborne LiDAR, the resulting anno-tations are noisy and difficult to exploit effectively. We address thesechallenges by formulating tree counting as a spatial density matchingproblem supervised through Unbalanced Optimal Transport. This formu-lation naturally accommodates both precise localization of isolate treesand robust density estimation in dense forests. We further introduce aself-correction mechanism that leverages transport residuals to progres-sively refine noisy supervision during training. We evaluate our approachon TinyTrees, a new benchmark spanning three continents and threesatellite sensors, comprising over 216 million tree annotations (including639k manually verified instances) across 25 890 km2 . Our method con-sistently outperforms detection-based, regression-based, and transport-based distribution-matching baselines, demonstrating the effectivenessof unbalanced transport and reliability-aware supervision for large-scaletree counting from satellite imagery. Code, data and models are availableat github.com/dgominski/treematch.

ECCV 2026

Coordinate Singularities Break Conformal Coverage for Gaze and Head Pose

Mohammadreza Jamalifard, Yaxiong Lei, Parastoo Azizinezhad, Javier Andreu-Perez

Conformal prediction provides distribution-free reliability guar-antees for vision systems, but these guarantees depend on how predictionerrors are measured in the output space. Many vision tasks produce out-puts on curved spaces (e.g. gaze directions on the sphere or 3D headrotations), yet intermediate prediction heads, residuals, uncertainty es-timates, or conformal scores are often defined in flat coordinate chartssuch as yaw–pitch or Euler angles. We show that this scoring choice intro-duces systematic geometric distortion near coordinate singularities (largepitch angles on the sphere and poses approaching gimbal lock in 3D rota-tions). Across four datasets (ETH-XGaze, Gaze360, BIWI, AFLW2000-3D), slice-conditional coverage at a nominal 90% target drops by 30–50percentage points in these regions, falling to 38.9% on ETH-XGaze and42.0% on Gaze360 at gaze pitch above 70◦ , and to 57.5% on BIWI and55.2% on AFLW2000-3D at head pose pitch above 60◦ near gimbal lock,despite marginal coverage remaining near 90%. We prove that this isstructural. Scalar thresholding changes the size of chart-coordinate pre-diction sets but leaves their distorted axis ratios unchanged. To diagnosethis hidden failure mode, we show that a simple geometric quantity,the Riemannian volume density, strongly correlates with where cover-age collapse occurs. Finally, we show that coordinate-free geodesic scor-ing removes this distortion. It requires no retraining and adds negligiblecomputational cost.

ECCV 2026

Sparsity-Inducing Divergence Losses for Biometric Verification

Dimitrios Koutsianos, Ladislav Mosner, Yannis Panagakis, Themos Stafylakis

Performance in face and speaker verification is largely drivenby margin-penalty softmax losses such as CosFace and ArcFace. Recentlyintroduced α-divergence loss functions offer a compelling alternative, par-ticularly due to their ability to induce sparse solutions (when α > 1).However, standard geometric margins are designed for the softmax func-tion and do not naturally extend to this generalized probabilistic frame-work. In this paper we propose Q-Margin, a novel α-divergence lossthat introduces a principled probabilistic margin. Unlike conventionalmethods that apply geometric penalties to the logits (unnormalized log-likelihoods), Q-Margin encodes the margin penalty directly into the ref-erence measure (prior probabilities). This formulation naturally encour-ages discriminative embeddings while preserving the beneficial sparsityproperties of the α-divergence. We demonstrate that Q-Margin achievescompetitive or superior performance on the challenging IJB-B and IJB-C face verification benchmarks and similarly strong results in speakerverification on VoxCeleb. Crucially, against ArcFace and CosFace base-lines trained under an identical recipe, Q-Margin consistently improvesat low False Acceptance Rates (FARs), a capability critical for practicalhigh-security applications. Finally, the extreme sparsity of the Q-Marginposteriors enables exact and memory-efficient training, offering a scalablesolution for datasets with millions of identities.

ECCV 2026

Sink-Token-Aware Pruning for Fine-Grained Video Understanding in Efficient Video LLMs

Kibum Kim, Jiwan Kim, Kyle Min, Yueqi Wang, Jinyoung Moon, Julian McAuley, Chanyoung Park

Video Large Language Models (Video LLMs) incur high in-ference latency due to a large number of visual tokens provided to LLMs.To address this, training-free visual token pruning has emerged as asolution to reduce computational costs; however, existing methods areprimarily validated on Multiple-Choice Question Answering (MCQA)benchmarks, where coarse-grained cues often suffice. In this work, wereveal that these methods suffer a sharp performance collapse on fine-grained understanding tasks requiring precise visual grounding, such ashallucination evaluation. To explore this gap, we conduct a systematicanalysis and identify sink tokens–semantically uninformative tokens thatattract excessive attention–as a key obstacle to fine-grained video un-derstanding. When these sink tokens survive pruning, they distort themodel’s visual evidence and hinder fine-grained understanding. Moti-vated by these insights, we propose Sink-Token-aware Pruning (SToP),a simple yet effective plug-and-play method that introduces a sink scoreto quantify each token’s tendency to behave as a sink and applies thisscore to existing spatial and temporal pruning methods to suppress them,thereby enhancing video understanding. To validate the effectivenessof SToP, we apply it to state-of-the-art pruning methods (VisionZip,FastVid, and Holitom) and evaluate it across diverse benchmarks cover-ing hallucination, open-ended generation, compositional reasoning, andMCQA. Our results demonstrate that SToP significantly boosts perfor-mance, even when pruning up to 90% of visual tokens. Our code is avail-able at https://github.com/rlqja1107/SToP

ECCV 2026

Self-Improving Diffusion Classifiers with Minority Preference Optimization

Hyunsoo Kim, Jungmyung Wi, Soobin Um, Donghyun Kim, Suhyun Kim

Prior studies have demonstrated that diffusion classifiers achieverobust zero-shot classification performance. However, their effectivenessis strongly tied to the pretraining data distribution: they perform well inmajority, high-density regions of the data manifold, but are significantlyless accurate in minority, low-density regions. Although prior works onminority sampling have focused on generating more minority-like im-ages, what minority sampling fundamentally enables beyond generationremains underexplored. In this paper, we reveal a direct relationshipbetween minority sampling in generation and the perception capabil-ity of diffusion classifiers. Specifically, we show that enhancing minoritysampling broadens the coverage of underrepresented regions on the datamanifold, thereby improving diffusion-based recognition. To exploit thisconnection, we propose Self-Improving Diffusion Classifiers with Minor-ity Preference Optimization (MiPO), which fine-tunes a pretrained dif-fusion model using minority preference rewards. Using only arbitrarycaption data, MiPO generates candidate samples, rewards those thatbetter cover minority regions, and optimizes the model with LoRA andGroup Relative Policy Optimization, without additional image data, ex-ternal foundation models, or external reward models. This enables stable,prompt-adaptive minority sampling and translates low-density genera-tive coverage into improved zero-shot diffusion classification. To sum up,we show that diffusion classifier perception is biased toward majorityregions, demonstrate that this bias can be alleviated through minoritypreference optimization, and evaluate MiPO on five standard datasets.

ECCV 2026

Rethinking Visual Privacy: A Compositional Privacy Risk Framework for Severity Assessment with VLMs

Efthymios Tsaprazlis, Tiantian Feng, Anil Ramakrishna, Sai Karimireddy, Rahul Gupta, Shrikanth Narayanan

Existing visual privacy benchmarks largely treat privacy asa binary property, labeling images as private or non-private based onvisible sensitive content. We argue that privacy is fundamentally compo-sitional. Attributes that are benign in isolation may combine to producesevere privacy violations. We introduce the Compositional Privacy RiskTaxonomy (CPRT), a regulation-aware framework that organizes visualattributes according to standalone identifiability and compositional harmpotential. CPRT defines four graded severity levels and is paired with aninterpretable scoring function that assigns continuous privacy severityscores. We further construct a taxonomy-aligned dataset of 6.7K imagesand derive compositional risk scores. By evaluating frontier and open-weight VLMs we find that frontier models align well with compositionalseverity when provided structured guidance, but systematically underes-timate composition-driven risks. Smaller models struggle to internalizegraded privacy reasoning. To bridge this gap, we introduce a deploy-able 8B SFT model that closely matches frontier-level performance oncompositional privacy assessment. Our dataset and models are publiclyavailable at: https://huggingface.co/collections/timtsapras23/cprt.

ECCV 2026

Rethinking Adversary in Semantic Segmentation: An Out-of-Distribution Perspective

Yuxuan Zhang, Shuchang Wang, Xiaoman Liu, Zhenbo Shi, Zhidong Yu, Wei Song, Wei Yang

Semantic segmentation models remain vulnerable to adversarial perturbations, which can degrade pixel-wise predictions despite being imperceptible. Existing adversarial training methods primarily rely on iterative prediction-driven attacks that push features across decision boundaries between in-distribution (ID) classes while remaining close to the ID manifold. This limits their ability to expose more severe representation failures. In this paper, we propose SegOOD, a novel adversarial attack framework that explicitly encourages out-of-distribution (OOD)- like deviations in latent feature space. SegOOD introduces a Feature Wasserstein Separation objective to increase distributional discrepancy between adversarial features and ID semantic prototypes, along with a Weighted K-Nearest-Neighbor Separation objective to enforce local feature-level deviation. These objectives are efficiently implemented using prototype aggregation and feature-aware superpixel clustering. Furthermore, we integrate SegOOD with conventional prediction-driven attacks during adversarial training, improving robustness against both existing ID-style and our OOD-like perturbations. Extensive experiments demonstrate state-of-the-art robustness while maintaining competitive clean performance and no additional inference cost.

ECCV 2026

InfiniteDance: Scalable 3D Dance Generation Towards in-the-wild Generalization

Ronghui Li, Zhongyuan Hu, Li Siyao, Youliang Zhang, Haozhe Xie, Mingyuan Zhang, Jie Guo, Xiu Li, Ziwei Liu

Although existing 3D dance generation methods performwell in controlled scenarios, they often struggle to generalize in the wild.When conditioned on unseen music, existing methods often produce un-structured or physically implausible dance, largely due to limited music-to-dance data and restricted model capacity. This work aims to pushthe frontier of generalizable 3D dance generation by scaling up bothdata and model design. 1) On the data side, we develop a fully auto-mated pipeline that reconstructs high-fidelity 3D dance motions frommonocular videos. To eliminate the physical artifacts prevalent in exist-ing reconstruction methods, we introduce a Foot Restoration DiffusionModel (FRDM) guided by foot-contact and geometric constraints thatenforce physical plausibility while preserving kinematic smoothness andexpressiveness, resulting in a diverse, high-quality multimodal 3D dancedataset totaling 100.69 hours. 2) On model design, we propose Choreo-graphic LLaMA (ChoreoLLaMA), a scalable LLaMA-based architecture.To enhance robustness under unfamiliar music conditions, we integratea retrieval-augmented generation (RAG) module that injects referencedance as a prompt. Additionally, we design a slow/fast-cadence Mixture-of-Experts (MoE) module that enables ChoreoLLaMA to smoothly adaptmotion rhythms across varying music tempos. Extensive experimentsacross diverse dance genres show that our approach surpasses existingmethods in both qualitative and quantitative evaluations, marking a steptoward scalable, real-world 3D dance generation.

ECCV 2026

Don’t Starve the Boundaries: Boundary-Constrained Label Propagation for Weakly Supervised 3D Segmentation

Shuwei Wu, Shuo Jin, Zhijin He, Siyue Yu, ENG LIM, Qiufeng Wang, Jimin XIAO

Although fully supervised methods have substantially ad-vanced segmentation in boundary areas of point clouds, effective weaklysupervised approaches remain scarce. This is primarily because limitedsupervision rarely reaches boundary regions, leaving them lacking reli-able supervision. We propose a novel 2D-assisted pseudo-label propaga-tion paradigm that does not rely on the model’s own predictions or anyexternal foundation models, yet is able to generate high-purity pseudo-labels. Compared with SAM-based 2D-3D projection, our pseudo-labelsare purer and more uniformly distributed. Even under 1 pt/obj settingon S3DIS, our initial offline propagation achieves >94.4% accuracy (≈93k pts per scene). We decomposed the pseudo-labels generation pro-cess from the main network, and applied a divide-and-conquer strategy:supervision from interior pseudo-labels serves to stabilize the representa-tion of core class regions, while boundary pseudo-labels are leveraged toenhance boundary robustness. This design reduces the confirmation biasinherent in classic online labeling and alleviates the lack of boundary su-pervision in existing weakly supervised models. Experiments show thatour method outperforms existing state-of-the-art methods. Our codeswill be released at: https://github.com/paul-swu/Bound3D.

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