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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

Seen2Scene: Completing Realistic 3D Scenes with Visibility-Guided Flow

Quan Meng, Yujin Chen, Lei Li, Matthias Niessner, Angela Dai

We present Seen2Scene, the first flow matching-based approach that trains directly on incomplete, real-world 3D scans for scene completion and generation. Unlike prior methods that rely on complete and hence synthetic 3D data, our approach introduces visibility-guided flow matching, which explicitly masks out unknown regions in real scans, enabling effective learning from real-world, partial observations. We represent 3D scenes using truncated signed distance field (TSDF) volumes encoded in sparse grids and employ a sparse transformer to efficiently model complex scene structures while masking unknown regions. We employ 3D layout boxes as an input conditioning signal, and our approach is flexibly adapted to various other inputs such as text or partial scans. By learning directly from real-world, incomplete 3D scans, Seen2Scene enables realistic 3D scene completion for complex, cluttered real environments. Experiments demonstrate that our model produces coherent, complete, and realistic 3D scenes, outperforming baselines in completion accuracy and generation quality.

ECCV 2026

Data Circuit Breaker: Identifying Training, Test, and Generated Data in Image Generative Models

Bihe Zhao, Michel Meintz, Juangui Xu, Franziska Boenisch, Adam Dziedzic

As generative models increasingly produce samples that are indistinguishable from human-created content, it becomes difficult to determine whether a given data point was part of a model’s natural training set or was generated by the model itself, especially when models memorize and reproduce training data. We formalize this challenge as Member vs Generated Inference (MGI): given a sample and a target generative model, infer whether the sample is a true training member or a generated output of that model. Focusing on image generation, we show that existing membership inference methods systematically misclassify generated samples as training members, while attributionbased methods often misclassify true members as generated. This failure arises because both approaches rely on likelihood-related signals that are similarly elevated for training examples and for the model’s own outputs. To address MGI, we propose Data Circuit Breaker (DCB), a three-stage method that combines complementary signals from a generative model’s autoencoder and latent generator to distinguish training members from generated samples. Across multiple generative models, including image autoregressive and diffusion models, DCB consistently addresses the shortcomings of membership inference and attribution methods, remains effective even when models reproduce near-duplicates of training samples, and generalizes to challenging model derivative settings in which new models are trained on generated data. Our code is available at https: //github.com/sprintml/MemberGeneratedInference.

ECCV 2026

MoBa-GS: Learning a Spatially-Varying Motion Basis over a Dynamic Canonical Space for 4D Reconstruction

Guan Yuan Tan, Arghya Pal, Sailaja Rajanala, Raphaël Phan, Chee-Ming Ting

Faithfully capturing the intricate relationship between motion and geometry in dynamic scenes is essential for 4D reconstruction. Recent state-of-the-art methods rely on monolithic deformation networks or global time-basis factorizations that struggle to represent complex, non-rigid topological changes. We propose MoBa-GS, a framework that resolves this entanglement by introducing a structural inversion: a spatially-factorized motion field coupled with adaptive geometric optimization. First, our model learns a low-frequency dynamic canonical space to represent coarse scene motion. Next, it decomposes complex, non-rigid motion into a Spatially-Varying Motion Basis of local kinematics, predicted from the canonical geometry, which is then linearly combined using dynamic blending weights. This formulation directly acts as an implicit neural scaffold, recovering both geometry and motion from random initialization, thereby removing the dependency on SfM point cloud priors. This design is further augmented with motion-guided densification and positional annealing to reduce geometry overfitting. Extensive experiments show that our framework surpasses prior state-of-theart methods in reconstruction fidelity. Enabled by time-invariant caching, MoBa-GS requires an order-of-magnitude shorter training time (<18 minutes), a compact storage (∼ 11 MB), and achieves real-time rendering speeds (>163 FPS). Our work establishes a new foundation for highfidelity, efficient 4D representations without relying on explicit geometric priors. Code is available at https://github.com/tgy1221/MoBa-GS.

ECCV 2026

Trajectory-Level Continuous Action Representation for Robotic Manipulation

Tong Yang, Jingkai Jia, Yuecheng Xu, Xueyao Chen, Chi Zhang, Wenqiang Zhang

We propose CAT, a trajectory-level continuous action representation framework for robotic manipulation. Existing visuomotor systems often entangle action representation with control frequency or rely on fixed temporal parameterizations. This leads to representational redundancy at high sampling rates and limits the modeling of critical motion. CAT instead encodes action trajectories within a fixed realtime interval into a set of continuous latent tokens. To ensure temporal consistency across varying control frequencies, we further incorporate a frequency-aware positional encoding that establishs a shared temporal coordinate system. Trajectory-level regularization further stabilizes the latent representation. This approach prevents representation growth with timestep density and avoids reliance on predefined temporal parameterizations. Extensive system-level evaluations on LIBERO, MimicGen, and real-world long-horizon manipulation tasks demonstrate that CATbased policies consistently outperform both competitive VQ-based and continuous visuomotor baselines under matched training settings. Across various model backbones and control frequencies, CAT consistently improves success rates. These results highlight the advantages of trajectorylevel continuous action modeling for scalable robotic manipulation across varying control rates.

ECCV 2026

DiffusionVL: Translating Any Autoregressive Models into Diffusion Vision Language Models

Lunbin Zeng, Jingfeng Yao, Bencheng Liao, Hongyuan Tao, Wenyu Liu, Xinggang Wang

Diffusion-based decoding has recently emerged as an appealing alternative to autoregressive (AR) generation, offering the potential to update multiple tokens in parallel and reduce latency. However, diffusion vision language models (dVLMs) still lag significantly behind mainstream autoregressive vision language models. This is due to the scarcity and weaker performance of base diffusion language models (dLLMs) compared with their autoregressive counterparts. This raises a natural question: Can we build high-performing dVLMs directly from existing powerful AR models, without relying on dLLMs? We propose DiffusionVL, a family of dVLMs obtained by translating pretrained AR models into the diffusion paradigm via an efficient diffusion finetuning procedure that changes the training objective and decoding process while keeping the backbone architecture intact. Through an efficient diffusion finetuning strategy, we successfully adapt AR pretrained models into the diffusion paradigm. This approach yields two key observations: (1) The paradigm shift from AR-based multimodal models to diffusion is remarkably effective. (2) Direct conversion of an AR language model to a dVLM is also feasible, achieving performance comparable to that of the same AR model finetuned with standard autoregressive visual instruction tuning. To enable practical open-ended generation, we further integrate block decoding, which supports arbitrarylength outputs and KV-cache reuse for faster inference. Our experiments demonstrate that despite training with less than 5% of the data required by prior methods, DiffusionVL achieves a comprehensive performance improvement, with a 34.4% gain on the MMMU-Pro (vision) benchmark and 37.5% gain on the MME (Cog.) benchmark, alongside a 2× inference speedup. The model and code are released at https://github.com/hustvl/DiffusionVL.

ECCV 2026

GhostPoint: Self-Supervised Representation Learning by Hallucinating Occluded LiDAR Structure

Bin Yang, Mohamed Abdelsamad, Miao Zhang, Michael Ulrich, Yakov Miron, Abhinav Valada, Alexandru Paul Condurache

3D object detection from LiDAR point clouds is a core problem in autonomous driving. Recent advances in self-supervised learning (SSL) enable scalable pretraining and transfers well to per-point tasks such as semantic and panoptic segmentation, but transfer to 3D detection remains weaker. We analyze recent SSL methods and find that most objectives are defined only on measured LiDAR returns from visible surfaces, leaving occluded and unobserved regions unconstrained. This visible-surface bias can be sufficient for point-wise prediction, but 3D detection requires robustness to missing structure. To address this gap, we propose GhostPoint, an SSL framework that hallucinates latent features in local neighborhoods around discovered instances, generated via a novel instance voxel dilation. In GhostPoint, an encoder processes observed returns, and an additional predictor infers neighborhood representations from observed context. In addition to standard encoder-level supervision, we introduce a predictor-level supervision scheme on sampled voxels from generated neighborhoods. Specifically, observed (visible/masked) voxels match teacher-encoder targets, while unobserved voxels match teacher-predictor hallucinations. This design encourages the learned representation to explicitly model structure beyond observed returns. Extensive evaluations on nuScenes and Waymo demonstrate that our method achieves state-of-the-art performance, consistently improving downstream 3D detection, especially under sparse scans and limited labels.

ECCV 2026

CVSBench: A Comprehensive Benchmark for Cross-view Spatial Reasoning and Dreaming

Ruixun Liu, Lingyu Zhang, Lanxuan Xue, Kaiyu Li, Bowen Fu, Xiangyong Cao

Humans can effortlessly reason about scenes across different viewpoints, yet it remains unclear whether Vision–Language Models (VLMs) possess similar cross-view spatial abilities. Satellite-street scene pairs, with their complex contexts and extreme viewpoint variations, provide an ideal testbed. Motivated by this, we introduce CVSBench, a large-scale benchmark for evaluating cross-view spatial reasoning through satellite-street pairs. This benchmark supports multiple tasks, including cross-view VQA, cross-view grounding, and viewpoint identification. CVSBench comprises 3,297 cross-view image groups with 9,468 object-level annotations and 40,679 question–answer (QA) pairs, enabling systematic and controlled evaluation of cross-view spatial reasoning. Extensive evaluations reveal that advanced VLMs struggle to maintain object-level and layout consistency under drastic viewpoint changes. To bridge this gap towards human-like spatial cognition, we investigate two categories of approaches: spatially grounded reasoning and the incorporation of cognitive map inputs. Our findings demonstrate that language-only reasoning yields marginal improvements, while incorporating visual spatial imagination via a 3D scene imagination pipeline substantially improves cross-view reasoning. These results highlight the necessity of explicit visual-spatial representations for robust spatial cognition in VLMs. Our data and code are released at https://huggingface.co/datasets/zlyzlyzly/CVSBench.

ECCV 2026

OmniDance: Multimodal Driven Dance Video Generation with Large-scale Internet Data

Kaixing Yang, Jiashu Zhu, Xulong Tang, Ziqiao Peng, Xiangyue Zhang, Chubin Chen, Puwei Wang, Jiahong Wu, Xiangxiang Chu, Hongyan Liu, Jun He

Music-driven dance video generation aims to synthesize expressive human motion that is temporally aligned with music while maintaining high visual fidelity. Despite recent progress, existing methods still struggle to generate dance videos that simultaneously exhibit expressive motion and high visual quality. This limitation primarily arises from two factors: (1) Dataset. The lack of large-scale and high-quality datasets and effective data collection pipelines specifically tailored for dance video generation; and (2) Method. The absence of principled framework-level solutions for effectively integrating music as a complementary conditioning signal into the Video Generation Foundation Models. To address the dataset limitation, we introduce CIPE-Dance, a large-scale Internetsourced dance video dataset, equipped with Choreograph Informed text annotations and constructed via a Progressive Expert pipeline. To the best of our knowledge, CIPE-Dance is the largest dataset for dance video generation to date, comprising 300k high-quality clips (over 400 hours) and covering diverse dancers, environments, and dance genres. To overcome the method limitation, we propose OmniDance, a frameworklevel recipe for integrating music into a TI2V foundation model without sacrificing its original controllability or visual fidelity. Motivated by the complementary roles of text (low-frequency semantics) and music (highfrequency temporal dynamics), OmniDance co-designs a depth-aware specialization model architecture, an anchored easy-to-hard curriculum learning strategy, and modality-specialized time-dependent CFG strategy, achieving unified TI2V/MI2V/MTI2V generation. Extensive experiments on the CIPE-Dance dataset demonstrate that OmniDance achieves state-of-the-art performance across TI2V, MI2V, and MTI2V tasks, while exhibiting robust multimodal integration capability. Project is available at https://github.com/AMAP-ML/OmniDance.

ECCV 2026

AIMold: An Autonomous AI-based Pipeline for Complex Mold Design

Pengyun Qiu, Shuo Wang, Zeyuan Chen, Yihao Zhi, Chongjie Ye, Xiaoguang Han

Injection molding is the cornerstone of mass-producing plastic components. While current algorithms can automate mold design for basic geometries using standard two-piece molds, complex parts featuring undercuts, side holes, or re-entrant features present a significant challenge. These geometries often necessitate auxiliary components beyond the primary upper and lower molds. In practice, designing these intricate assemblies is a laborious process that relies heavily on expert knowledge. Furthermore, the scarcity of public datasets has hindered the development of effective learning-based solutions. To bridge these gaps, we introduce MoldCAD, a curated dataset that pairs complex single-body CAD parts with industry-standard mold assemblies. Each entry includes the upper and lower molds, parting surfaces, demolding orientations, and necessary auxiliary components. The dataset comprises 4,934 CAD models and over 3,850 mold assemblies, totaling more than 23k individual models. Building upon this dataset, we propose a comprehensive pipeline that predicts demolding orientations, identifies auxiliary components, and constructs parting surfaces to derive a complete, manufacturing-ready mold assembly for downstream CAD/CAM workflows. Our results demonstrate a promising path toward fully automated industrial mold design and contribute to the broader advancement of manufacturing-aware CAD generation. Code is available at https://github.com/tb2-sy/AIMold.

ECCV 2026

Bridging the Geometry Mismatch: Frequency-Aware Anisotropic Serialization for Thin-Structure SSMs

Jin Bai, Huiyao Zhang, Qi Wen, Ningyang Li, Shengyang Li, Atta ur Rahman, Xiaolin Tian

The segmentation of thin linear structures is inherently topology-critical, where minor local errors can sever long-range connectivity. While recent State-Space Models (SSMs) offer efficient long-range modeling, their isotropic serialization (e.g., raster scanning) creates a geometry mismatch for anisotropic targets, causing state propagation across rather than along the structure trajectories. To address this, we propose FGOS-Net, a framework based on frequency-geometric disentanglement. We first decompose features into a stable topology carrier and directional high-frequency bands, leveraging the latter to explicitly correct spatial misalignments induced by downsampling. Building on this calibrated topology, we introduce frequency-aligned scanning that elevates serialization to a geometry-conditioned decision, preserving directionconsistent traces. Coupled with an active probing strategy to selectively inject high-frequency details and suppress texture ambiguity, FGOS-Net consistently outperforms strong baselines across four challenging benchmarks. Notably, it achieves 91.3% mIoU and 97.1% clDice on DeepCrack while running at 80 FPS with only 7.87 GFLOPs.

ECCV 2026

Breaking High Confidence: Practical Face Impersonation under High-Security Thresholds

Changjin Kim, Seunghun Paik, Dongsoo Kim, Jae Hong Seo

Face recognition systems (FRSs) are increasingly deployed in critical real-world services for authentication, such as banking applications and airport identity checks, necessitating stringent security configurations. Consequently, the security vulnerabilities of FRSs have garnered significant attention. While existing studies have extensively explored FRS security, prior analyses have primarily focused on mediumsecurity threshold settings, which are not directly applicable to FRSs operating under high-security constraints. In this paper, we propose the first successful impersonation attack against FRSs under high-security threshold settings. Among various threat models, we focus on a practical and challenging scenario: score-based impersonation attacks under strict rate limits. To precisely evaluate the feasibility of such attacks, we provide a principled mathematical analysis characterizing the gaps in each stage of the attack pipeline. Our method significantly enhances impersonation capabilities in score-based attacks, even under elevated decision thresholds. On the LFW benchmark, with a budget of only 100 confidence score queries per identity, our attack achieves an impersonation success rate exceeding 92% against Amazon Rekognition at a confidence score threshold of 99—recommended setting for law enforcement scenarios. We further observe consistently robust performance across multiple open-source FRSs evaluated at similarly stringent decision thresholds.

ECCV 2026

Why Can Accurate Models Be Learned from Inaccurate Annotations?

Chongjie Si, Yidan Cui, Fuchao Yang, Wei Shen

Learning from inaccurate annotations has gained significantattention due to the high cost of precise labeling. However, despite thepresence of erroneous labels, models trained on noisy data often retainthe ability to make accurate predictions. This intriguing phenomenonraises a fundamental yet largely unexplored question: why models canstill extract correct label information from inaccurate annotations re-mains unexplored. In this paper, we conduct a comprehensive investiga-tion into this issue. By analyzing weight matrices from both empiricaland theoretical perspectives, we find that label inaccuracy primarily ac-cumulates noise in lower singular components and subtly perturbs theprincipal subspace. Within a certain range, the principal subspaces ofweights trained on inaccurate labels remain largely aligned with thoselearned from clean labels, preserving essential task-relevant information.We formally prove that the angles of principal subspaces exhibit mini-mal deviation under moderate label inaccuracy, explaining why modelscan still generalize effectively. Building on these insights, we proposeLIP, a lightweight plug-in designed to help classifiers retain principalsubspace information while mitigating noise induced by label inaccu-racy. Extensive experiments on tasks with various inaccuracy conditionsdemonstrate that LIP consistently enhances the performance of existingalgorithms. We hope our findings can offer valuable insights to under-stand of model robustness under inaccurate supervision.

ECCV 2026

EgoMAN: Interaction-Structured Reasoning for Egocentric 3D Hand Trajectory Prediction

Mingfei Chen, Yifan Wang, Zhengqin Li, Homanga Bharadhwaj, Yujin Chen, Chuan Qin, Ziyi Kou, Yuan Tian, Eric Whitmire, Rajinder Sodhi, Hrvoje Benko, Eli Shlizerman, Yue Liu

Our work addresses 3D hand trajectory prediction in egocentric interaction, where future hand motion is inferred from visual observations, past motion, spatial context, and intent. Real-world actions follow stage-aware interaction structures (e.g., approach, manipulate) describing how the hand interacts with objects over time. However, prior works typically treat trajectory prediction as continuous signal regression, decoupling motion from semantic supervision and ignoring interaction structure. Without stage-aware cues to infer intent, models struggle to separate purposeful motion from egocentric noise and generalize across diverse interactions. We therefore present EgoMAN, a unified framework for interaction-structured 3D hand trajectory prediction that models hand motion as stage-aware interactions between the hand and surrounding objects. EgoMAN introduces a novel Trajectory-Token Interface where a small set of tokens encodes interaction stages, temporal progression, and 6DoF pose, enabling interaction stage-aware reasoning to guide efficient long-horizon 3D trajectory generation while preserving physical interpretability. To support this formulation, we construct the EgoMAN dataset with 219K 6DoF trajectories, stage-aware annotations, and 3M semantic, spatial, and motion QA pairs. Experiments show that EgoMAN improves trajectory accuracy, smoothness, and generalization, enabling interaction-structured reasoning for egocentric hand motion prediction for applications in robotics and assistive systems.

ECCV 2026

GeMoE: Gating Entropy is All You Need for Uncertainty-aware Adaptive Routing in MoE-based Large Vision-Language Models

Chaoxiang Cai, Minghe Weng, Jie Li, Yibo Jiang, Longrong Yang, Zequn Qin, Xi Li

With the increase in model parameters and training data, the instruction following and generalization capabilities of Large VisionLanguage Models (LVLMs) have been significantly improved. Based on the Mixture-of-Experts (MoE) architecture, LVLMs expand their parameter capacity while maintaining the inference cost. However, traditional MoE methods employ a Top-k static routing strategy, which fails to account for variations in the input and to adaptively select the number of experts, resulting in suboptimal resource utilization. In this paper, we propose viewing token routing as an information encoding task, framing dynamic routing as a Minimum Description Length (MDL) problem in encoding. By validating the connection between MDL and gating entropy in the MoE scenario, we introduce Gating Entropy-based Uncertaintyaware Adaptive Routing (GeMoE) for MoE. Unlike traditional static or heuristic-based dynamic routing methods, GeMoE explicitly models the trade-off between model complexity and performance. By using gating entropy to assess the complexity of tokens, GeMoE adaptively determines the number of experts each token should engage. On a wide range of backbones and benchmarks, our method achieves 99.5% average performance retention compared to the original static routing, while improving average expert activation sparsity by 36.5%. The code will be publicly available at https://github.com/caichaoxiang/GeMoE.

ECCV 2026

Argus: Metric Panoramic 3D Reconstruction for Indoor Scenes

Xi Li, Linyuan Li, Yan Wu, Tong Rao, Kai Zhang, Xinchen Hui, Cihui Pan

Metric feed-forward 3D reconstruction for panoramic data remains under-explored due to the lack of large-scale panoramic RGBD training data. We present Realsee3D, a hybrid dataset of 10K indoor scenes (1K real, 9K synthetic) with 299K panoramic viewpoints and precise metric annotations, and Argus, a feed-forward network trained on it for metric panoramic 3D reconstruction. In the sparse unordered capture setting of Realsee3D, a poorly chosen coordinate anchor can cause global pose drift. Argus addresses this with a learned covisibility module that selects the geometrically optimal reference view to anchor the metric world frame. To further improve multi-task learning, we decompose the bidirectional pixel-to-world mapping into interpretable sub-steps with per-step supervision and cross-coordinate joint constraints, reinforcing geometric consistency across prediction branches. On the Realsee3D benchmark, Argus achieves state-of-the-art metric performance in camera pose estimation, depth estimation, and point cloud reconstruction. Project page: https://argus-paper.realsee.ai.

ECCV 2026

SignSparK: Efficient Multilingual Sign Language Production via Sparse Keyframe Learning

Jian He Low, Alexandre Symeonidis-Herzig, Maksym Ivashechkin, Ozge Mercanoglu Sincan, Richard Bowden

Sign Language Production (SLP) faces a fundamental tradeoff: direct text-to-pose models suffer from regression-to-the-mean effects, while dictionary-retrieval methods produce disjointed transitions. To resolve this, we propose a novel training paradigm that leverages sparse keyframes to capture the underlying kinematic distribution of human signing. By generating dense motion from discrete anchors, our approach mitigates regression-to-the-mean while ensuring fluid articulation. To achieve this at scale, we introduce FAST, an ultra-efficient sign segmentation model that automatically mines precise temporal boundaries. We then present SignSparK, a Conditional Flow Matching (CFM) framework that utilizes these temporal anchors to synthesize 3D signing sequences. This keyframe-driven formulation also unlocks Keyframe-toPose (KF2P) generation, making precise spatiotemporal editing of signing sequences possible. Furthermore, SignSparK scales across four distinct sign languages, constituting the largest multilingual SLP framework to date, and integrates 3D Gaussian Splatting for photorealistic rendering. Extensive evaluations demonstrate that SignSparK achieves state-of-the-art across diverse SLP tasks and multilingual benchmarks. Our code is available at https://github.com/JianHe0628/SignSparK.

ECCV 2026

Last-Layer-Centric Feature Recombination: Unleashing 3D Geometric Knowledge in DINOv3 for Monocular Depth Estimation

Gongshu Wang, Zhirui Wang, Kan Yang

Monocular depth estimation (MDE) is a fundamental yet inherently ill-posed task. Recent vision foundation models (VFMs), particularly DINO-based transformers, have significantly improved accuracy and generalization for dense prediction. Prior works generally follow a unified paradigm: sampling a fixed set of intermediate transformer layers at uniform intervals to build multi-scale features. This common practice implicitly assumes that geometric information is uniformly distributed across layers, which may underutilize the structural 3D cues encoded in VFMs. In this study, we present a systematic layer-wise analysis of DINOv3, revealing that 3D information is distributed nonuniformly: deeper layers exhibit stronger depth predictability and better capture inter-sample geometric variation. Motivated by this, we introduce a Last-Layer-Centric Feature Recombination (LFR) module to enhance geometric expressiveness. LFR treats the final layer as a geometric anchor and adaptively selects complementary intermediate layers according to a minimal-similarity criterion. Selected features are fused with the last-layer representation via compact linear adapters. Extensive experiments show that LFR module consistently improves MDE accuracy and achieves state-of-the-art performance. Our analysis sheds light on how geometric knowledge is organized within VFMs and offers an efficient strategy for unlocking their potential in dense 3D tasks. Code is released at https://github.com/book-book24/LFR.

ECCV 2026

V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning

Lorenzo Mur-Labadia*, Matthew Muckley, Amir Bar, Mido Assran, Koustuv Sinha, Michael Rabbat, Yann LeCun, Nicolas Ballas, Adrien Bardes

We present V-JEPA 2.1, a family of self-supervised models that learns dense, high-quality, and temporally consistent representations for visual scenes in both images and videos. V-JEPA 2.1 combines four key ingredients: (i) a Dense Predictive Loss, a masking-based objective in which all tokens—visible context and masked tokens alike— contribute to the training loss, encouraging explicit spatial and temporal grounding; (ii) Deep Self-Supervision, which applies the selfsupervised objective hierarchically at multiple intermediate encoder layers to improve representation quality; (iii) Multi-Modal Tokenizers that support unified training over images and videos; and (iv) effective model and data scaling. Empirically, V-JEPA 2.1 achieves state-ofthe-art results on a range of benchmarks: 7.71 mAP on Ego4D for shortterm object-interaction anticipation, 40.8 Recall@5 on EPIC-Kitchens for high-level action anticipation, and a 20% improvement in real-robot grasping success rate over VJEPA-2 AC. The model also achieves stateof-art performance in robotic navigation (5.687 ATE on Tartan Drive), depth estimation (0.307 RMSE on NYUv2 with a linear probe), and global recognition (77.7% on Something-Something-V2).

ECCV 2026

Dynamic Image Prompt Adapter for Scalable Zero-shot Personalized Text-to-Image Generation

Zhizhong Wang, Tianyi Chu, Richard Wang, Nanyang Wang, Kehan Li

Personalized Text-to-Image (PT2I) generation aims to pro-duce customized images based on reference images. A prominent interestpertains to the integration of an image prompt adapter to facilitate zero-shot PT2I without test-time fine-tuning. However, current methods grap-ple with three fundamental challenges: 1. the elusive equilibrium betweenConcept Preservation (CP) and Prompt Following (PF), 2. the difficultyin retaining fine-grained concept details in reference images, and 3. therestricted scalability to extend to multi-subject personalization. To tacklethese challenges, we present Dynamic Image Prompt Adapter (DynaIP),a cutting-edge plugin to enhance the fine-grained concept fidelity, CP·PFbalance, and subject scalability of state-of-the-art T2I multimodal diffu-sion transformers (MM-DiT) for PT2I generation. Our key finding is thatMM-DiT inherently exhibit decoupling learning behavior when injectingZ. Wang and T. Chu—Equal contribution. ∗ Corresponding author.reference image features into its dual branches via cross attentions. Basedon this, we design an innovative Dynamic Decoupling Strategy that re-moves the interference of concept-agnostic information during inference,significantly enhancing the CP·PF balance and further bolstering thescalability of multi-subject compositions. Moreover, we identify the vi-sual encoder as a key factor affecting fine-grained CP and reveal thatthe hierarchical features of commonly used CLIP can capture visual in-formation at diverse granularity levels. Therefore, we introduce a novelHierarchical Mixture-of-Experts Feature Fusion Module to fully leveragethe hierarchical features of CLIP, remarkably elevating the fine-grainedconcept fidelity while also providing flexible control of visual granularity.Extensive experiments across single- and multi-subject PT2I tasks verifythat our DynaIP outperforms existing approaches, while requiring onlysingle-subject training datasets.

ECCV 2026

Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching

Yue Pan, Tao Sun, Liyuan Zhu, Lucas Nunes, Iro Armeni, Jens Behley, Cyrill Stachniss

Point cloud registration aligns multiple unposed point clouds into a common reference frame and is a core step for 3D reconstruction and robot localization when no initial pose guess is available. In this work, we cast point cloud registration as conditional generation: a learned, continuous point-wise velocity field transports noisy points to a registered scene, from which the pose of each view is recovered. Unlike prior methods that perform correspondence matching to estimate pairwise transformations and then optimize a pose graph for multi-view registration, our model directly generates the registered point cloud, yielding both efficiency and point-level global consistency. By scaling the training data and conducting test-time rigidity enforcement, our approach achieves state-of-the-art average performance on existing pairwise registration benchmarks and on our proposed cross-domain multi-view registration benchmark. The superior zero-shot performance on this benchmark demonstrates that our method generalizes across view counts, scene scales, and sensor modalities even with low overlap.

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