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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

Together, Then Apart: Balancing Alignment and Distinctiveness for Multimodal Survival Analysis

Wenjing Liu, Qin Ren, Wen Zhang, Yuewei Lin, Chenyu You

Multimodal survival analysis aims to improve cancer prognosis using heterogeneous biomedical data, such as histopathology images and genomic profiles. A common strategy is to align representations across modalities so that shared signals can be captured. However, strong cross-modal alignment can also remove modality-specific evidence that is critical for survival prediction. In this paper, we revisit multimodal survival learning from a simple observation: effective models should first discover shared patterns across modalities, and then preserve modalityspecific signals. This motivates a representation learning principle that we refer to as Together Then Apart. Based on this idea, we propose TTA, a framework that balances cross-modal alignment and representation distinctiveness. TTA first performs prototype-based alignment to capture shared survival-related structures between modalities. It then encourages modality-specific distinctiveness through an anchor-guided contrastive objective. To further account for modality imbalance and noisy correspondences, we model cross-modal interactions using unbalanced optimal transport. We evaluate the proposed approach on multiple TCGA cancer cohorts with paired histopathology and genomic data. TTA consistently improves survival prediction over recent multimodal survival models. Moreover, the learned prototype structures reveal interpretable cross-modal patterns associated with clinical outcomes. Code is available at https://github.com/Y-Research-SBU/TTA.

ECCV 2026

TIR-Agent: Training an Explorative and Efficient Agent for Image Restoration

Yisheng Zhang, Guoli Jia, Haote Hu, Shanxu Zhao, Kaikai Zhao, Long Sun, Xinwei Long, Kai Tian, Che Jiang, Zhaoxiang Liu, Kai Wang, Shiguo Lian, Kaiyan Zhang, Bowen Zhou

Vision-language agents that orchestrate specialized tools forimage restoration (IR) have emerged as a promising method, yet most ex-isting frameworks operate in a training-free manner. They rely on heuris-tic task scheduling and exhaustive tool traversal, resulting in sub-optimalrestoration paths and prohibitive computational cost. We argue that thecore bottleneck lies in the absence of a learned policy to make decision,as a vision-language model cannot efficiently handle degradation-awaretask ordering and tool composition. To this end, we propose TIR-Agent, atrainable image restoration agent that performs a direct tool-calling pol-icy through a two-stage training pipeline of supervised fine-tuning (SFT)followed by reinforcement learning (RL). Two key designs underpin effec-tive RL training: (i) a random perturbation strategy applied to the SFTdata, which broadens the policy’s exploration over task schedules andtool compositions, and (ii) a multi-dimensional adaptive reward mech-anism that dynamically re-weights heterogeneous image quality metricsto mitigate reward hacking. To support high-throughput, asynchronousGPU-based tool invocation during training, we further develop a glob-ally shared model-call pool. Experiments on both in-domain and out-of-domain degradations show that TIR-Agent outperforms 12 baselines,including 6 all-in-one models, 3 training-free agents, and 3 proprietarymodels, and achieves over 2.5× inference speedup by eliminating redun-dant tool executions.

ECCV 2026

Through Van Gogh’s Eyes: Global Style Transfer with Diffusion Model

Jeongha Lee, Yujin Kim, Ghazanfar Ali, Suhyun Kim, Jae-In Hwang

Artistic image synthesis aims to recreate the expressive vi-sual identity of a target artist, yet existing methods often fail to cap-ture an artist’s global style. Conventional style transfer methods trans-fer the style of one or a few reference artworks to a content image ina One-to-One manner, making them effective for artwork-level styliza-tion but limited in representing the broader stylistic distribution of anartist. Text-to-image diffusion models conditioned on artist names, suchas ‘∼ in Van Gogh style’, offer greater flexibility, but they often sufferfrom text-induced bias and reproduce patterns from only a few iconicworks. To address these limitations, we introduce Global Style Transfer(GST), an artistic image synthesis paradigm, in a Many-to-One man-ner,that aggregates multiple artworks from a target artist and transferstheir shared global style to a single content image. For GST, we proposeGlobal Style Guidance (GSG), which learns a residual global style offset∆⟨t in the intermediate feature space, or h-space, of a diffusion modelunder a fixed prompt. By learning artist-level style semantics purely fromvisual statistics, GSG mitigates text-dependent artistic bias. We furtherpropose Content Alignment Guidance (CAG), a training-free perceptualguidance mechanism that preserves the semantic structure of the contentimage while allowing artist-specific geometric deformation. Experimentson WikiArt demonstrate that GST achieves superior stylistic fidelity,content preservation, and output diversity compared to existing styletransfer and diffusion-based artistic synthesis methods.

ECCV 2026

The Telephone Game: Evaluating Semantic Drift in Unified Models

Sabbir Mollah, Rohit Gupta, Swetha Sirnam, Qingyang Liu, Ahnaf Munir, Shah Mubarak

Unified models (UMs) combine visual understanding (I2T)and generation (T2I) in a single framework, alongside broader unimodalcapabilities. We focus on the cross-modal pair T2I and I2T, where cross-consistency—what a model understands, it should be able to generate—is a core promise of unification and a practical necessity in applicationsthat compose both capabilities. Yet, existing benchmarks evaluate themin isolation: FID/GenEval for T2I; MME/MMBench for I2T. We showthat this gap is consequential: models that score competitively on thesebenchmarks can fail severely when their own understanding and gener-ation are composed, progressively losing core entities, attributes, spa-tial relations, and counts, resulting in semantic drift. To quantify drift,we introduce the Semantic Drift Protocol (SDP), inspired by the Tele-phone Game: starting from a caption or image, we iteratively alternateI2T and T2I over multiple generations and measure how faithfully se-mantics are preserved. We propose two complementary metrics: MeanCumulative Drift (MCD), an embedding-based measure of overall con-tent retention across three representation spaces, and Multi-GenerationGenEval (MGG), which extends GenEval’s object-level compliance scor-ing across generations. To stress-test models beyond COCO-style data,we create a benchmark of 400 image-text pairs sampled from NoCapsand DOCCI that emphasizes novel objects and fine-grained descriptions.Applying SDP to seven recent unified models reveals that drift behaviorvaries dramatically and is not predicted by single-pass scores: BAGELretains high semantic fidelity over multiple generations, while VILA-Uand Janus variants collapse within five generations, despite comparableisolated metrics. We identify six recurring failure modes and find thatdegradation is typically catastrophic rather than gradual: once a criticalerror occurs, subsequent generations compound it. Overall, SDP exposesfailure modes that single-pass benchmarks miss, enabling a more faithfulassessment of unified model reliability. Code and benchmark resourcesare available at https://github.com/mollahsabbir/telephone-game-semantic-drift.

ECCV 2026

StreamTalk: Streaming Co-Speech Gesture Generation with Key-Pose Anchoring

Jianfang Li, Xiangyue Zhang, Jiaxu Zhang, Kaixing Yang, Steven Hoi

Real-time co-speech gesture generation requires producing 3D motion clip by clip as speech streams in. Existing methods are fundamentally open-loop: each clip is synthesized conditioned only on past context, with no mechanism to verify or correct trajectory plausibility. Small per-clip errors therefore compound silently, causing the well-known drift problem--motion that gradually deviates from natural distributions over minute-scale horizons. We make a key observation: drift stems not from poor local motion quality--modern diusion models already produce convincing short clipsbut from the absence of forward constraints that tell each clip where it should arrive. Supplying even a single plausible key pose at a clip's tail as a destination anchor is sufficient to suppress drift dramatically. Building on this insight, we propose StreamTalk, a closed-loop streaming framework that introduces a periodic generate-retrieve-refine feedback cycle. At inference, Streaming Pose-Guided Generation (SPG) first produces a coarse clip, retrieves a plausible tail key pose from a speaker-specic motion database, and refines the clip with this anchor before passing it to the next window. To enable the model to exploit such sparse anchors effectively, we introduce Stochastic Anchor Masking (SAM) during training, which independently masks random pose and translation frames so the model learns to inpaint complete motion from partial boundary conditions. A part-aware DiT architecture further disentangles hand, body, and translation streams to prevent global displacement from interfering with local articulation. Extensive experiments on the BEAT2 benchmark demonstrate that StreamTalk achieves state-of-the-art motion quality (FGD), signicantly suppresses long-horizon drift compared to open-loop baselines, and runs in real time at 76 FPSenabling practical minute-scale streaming co-speech gesture generation. Code will be released upon acceptance.

ECCV 2026

StreamSpatial: A Benchmark and Framework for Streaming 3D Visual-Spatial Reasoning

Junlin Xie, Keyang Zhong, Quanlong Zheng, Ruifei Zhang, Kuo Wang, Yanhao Zhang, Haonan Lu, Xiang Wan, Guanbin Li

Embodied agents perceive the world as continuous streams,yet prevailing benchmarks evaluate spatial reasoning offline, presuppos-ing global spatial omniscience and overlooking real-time spatial intelli-gence. We present StreamSpatialBench, a testbed for streaming 3Dvisual-spatial reasoning built on three pillars: fine-grained online tempo-ral perspectives (past / current / future anchored to the query times-tamp), dynamic multi-agent interactions, and comprehensive 3D spatialreasoning. It comprises 5,280 millisecond-grounded QA pairs over 2,003egocentric/scene videos across 10 sub-tasks. Benchmarking 18 represen-tative MLLMs against humans reveals a substantial gap, rooted in threedeficiencies: limited spatiotemporal memory, absent incremental updat-ing, and poor adaptation to dynamic, multi-agent scenes. To close thisgap, we propose Stream3D-Mem, which (i) maintains a bounded mem-ory buffer with selective consolidation via round-decayed compressionto prune redundancy while preserving recent context, and (ii) distillscognitive-map knowledge into compact implicit latent tokens, bypassinglatency-heavy explicit map generation. Together they offer a rigoroustestbed and a practical step toward continuous, human-like 3D spatialintelligence for robotics and AR/VR. Code: https://github.com/OPPO-Mente-Lab/StreamSpatial.

ECCV 2026

SPECSIA: Stylization Dataset for Novel-View Enhancement in Drawing-based 3D Animation

Kyuwon Kim, Sunjae Yoon, Chang D. Yoo

Generating animation from a single 2D drawing is challenging because the output must preserve character appearance while remaining plausible and temporally coherent under motion. Existing drawingbased 3D animation pipelines often use sample-wise 2D refinement to align animated renderings with the input image, but such optimization tends to overfit to the observed view and fails to correct projectioninduced artifacts in novel views. To address this limitation, we introduce SPECSIA-15K, a paired stylization dataset containing 14,980 artifactcorrupted projection/refinement-target pairs from 1,498 3DBiCar characters. We further present DraViE (Drawing-based View Enhancement), a lightweight plug-and-play module trained with data-level priors to remove novel-view artifacts while preserving style and motion plausibility. Experiments show consistent gains in novel-view fidelity and temporal coherence with lower per-character adaptation cost than sample-wise fine-tuning. Project page: https://rbdnjs7830.github.io/SPECSIA/

ECCV 2026

Consistent Video-to-Video Translation via Explicit Correspondences

Gaurav Parmar, Zhengqi Li, Richard Zhang, Jun-Yan Zhu, Srinivasa G. Narasimhan, Eli Shechtman, Yotam Nitzan

Interactive video-to-video applications require real-time gen-eration while maintaining long-range temporal consistency. However, re-cent methods achieve speed by restricting attention to a fixed-lengthcausal window. This limits long-range consistency whenever relevant his-tory falls outside the context window. To address this limitation, we in-troduce vid2vid-long, a correspondence-based approach grounded in asimple observation. Video-to-video translation provides an explicit cross-frame reference: the regions that match in the input video should matchin the output video too. Concretely, we compute patch-level correspon-dences in the input, guiding retrieval of previously generated outputs inthe corresponding regions, and adding them to the context window. Ourexperiments demonstrate that vid2vid-long consistently improves long-range temporal consistency across recent video models while preservinggeneration quality and real-time performance.

ECCV 2026

RobustRDP: Advancing Reaction Diagram Parsing via Synthetic-to-Real Data Scaling and Robustness-Oriented Training

Jianting Tang, Zezhong Wu, Linli Xu

Chemical reaction diagram parsing aims to automaticallyconvert reactions from images into machine-readable formats. Currentleading approaches employ end-to-end generative models to directly de-code reaction diagrams into structured coordinate sequences, performingobject localization and relationship modeling in a unified process. How-ever, two critical bottlenecks remain: severe data scarcity due to thehigh cost of expert annotation, and output instability caused by thecomplex sequential dependencies between chemical reactions, where mi-nor prefix errors can lead to cascading parsing failures. In this paper, wepropose RobustRDP, a Robust Reaction Diagram Parser built on amultimodal large language model (MLLM). Its superior performance pri-marily stems from two advancements: Synthetic-to-Real Data Scal-ing: We develop a layout-driven synthesizer and an efficient annotationplatform to create a large-scale training set, along with a more compre-hensive evaluation benchmark. Robustness-Oriented Training: Wepropose a three-stage progressive training strategy (Pretraining, Multi-Task SFT, DPO). The SFT stage employs two auxiliary tasks includ-ing region-guided reaction parsing and prefix-perturbed reaction parsingto mitigate sequential dependencies between reactions and enhance themodel’s robustness against prior errors. The DPO stage further stabilizesoutputs in failure-prone scenarios. Extensive experiments demonstratethe superior performance of RobustRDP, establishing a solid foundationfor automated reaction diagram parsing. Code and model weights areavailable at https://github.com/jaydetang/RobustRDP.

ECCV 2026

Remembering Across Blocks: Topology-Conditioned Block-Progressive Memory for Skeleton-Based Action Recognition

Seonho Lee, Sang Han, Hyeok Nam, Sung In Cho

Skeleton-based action recognition classifies human actionsfrom sequences of joint coordinates and is widely studied using graph con-volutional networks (GCNs). GCNs typically rely on repeated graph nodefeature aggregation via one-hop message passing to capture long-rangedependencies across entire joint sequences, while progressively refiningrepresentations from multiple perspectives through block-wise learnedtopology. However, this repeated aggregation can often degrade repre-sentations, making the node features in the final embedding increasinglyhomogenized and causing the final embedding to fail to capture local orfine-grained representations formed in early blocks. To address these is-sues, we propose a novel topology-conditioned block-progressive memoryfor skeleton GCNs. Along the GCN block axis, memory stores comple-mentary block-wise representations and fuses the accumulated memorystate into the final embedding, thereby mitigating representation degra-dation caused by repeated aggregation. At each block, a sample-specificmemory state is updated by minimizing a topology-conditioned recon-struction loss, where the reconstruction error quantifies how well theprevious memory explains the current block representation while captur-ing newly emerging, discriminative cues. We support two inference-timemodes: memory-free, which uses memory only during training, and anoptional memory-enabled mode, which also applies the same memoryupdate rule at test time for modest gains. Both variants achieve state-of-the-art results on large-scale benchmarks, including NTU RGB+D,NTU RGB+D 120, and Kinetics-Skeleton.

ECCV 2026

Reconstructing Dense Depth of Dark Scenes with Sparse LiDAR, Noisy Events, and Blurry RGB

Jianbo Cao, Yuqi Han, Siming Zheng, Bo Wang, Tong Guo, Jinli Suo

Due to the sparsity of LiDAR measurements, RGB-assisteddense depth reconstruction is widely adopted to provide structural pri-ors for autonomous driving. However, the inherent sensitivity of RGBimaging to illumination conditions makes dense depth reconstruction un-der low-light scenarios still a critical challenge. Specifically, under long-exposure imaging, motion blur in low-light RGB frames significantlydegrades the accuracy of depth reconstruction. To address this issue,we exploit the high-temporal-resolution motion cues captured by eventcameras and propose Event-guided Restoration and Upsampling Network(ERU-Net), a unified framework that tightly couples event-guided featurerestoration with depth completion. The Event-guided Feature Restora-tion (EFR) module combines implicit neural representation (INR) andself-recursive optimization to remove motion blur from long-exposure in-puts and recover artifact-free features. These features serve as structuralpriors for the Restoration-Aware Depth Upsampling (RDU) module, en-abling accurate completion of sparse LiDAR measurements with finegeometric details. Extensive experiments on challenging synthetic andreal-world captured datasets demonstrate that ERU-Net significantlyoutperforms state-of-the-art approaches, producing accurate and tem-porally consistent dense depth sequences in severe low-light conditions.Our code will be available at https://github.com/qzm777/Night-RIDE-ECCV2026.

ECCV 2026

RA-SOD: Reliability-Aware RGB-T Salient Object Detection under Modality Degradation

Hongbo Gao, Zhengyu Li, XUERU NIE, Dihao Zhu, Lijun Zhao, Yunke Wang, Chang Xu

RGB–Thermal (RGB-T) salient object detection leveragescomplementary cues from visible and thermal modalities to improve ro-bustness in challenging environments. However, in real-world scenarios,the reliability of each modality is inherently unstable: RGB images de-grade under low illumination, motion blur, and noise, while thermal im-agery often suffers from contrast compression and sensor artifacts. Suchdegradation introduces unreliable perceptual evidence that can misleadcross-modal fusion and significantly deteriorate detection performance.To address this challenge, we propose RA-SOD, a reliability-aware RGB-T salient object detection framework that explicitly models modality re-liability and integrates it into feature learning and cross-modal fusion.First, we introduce a reliability-conditioned representation that adap-tively compensates degraded modality features while preserving struc-tural cues. Second, an uncertainty-guided dual-stream refinement strat-egy progressively corrects cross-modal representations while suppress-ing unreliable evidence. Finally, we propose a pixel-wise modality com-petition mechanism that dynamically selects modality cues accordingto spatial reliability for fine-grained fusion. Extensive experiments onfour benchmarks (VT821, VT1000, VT5000, and VT-IMAG) demon-strate that RA-SOD achieves state-of-the-art performance and exhibitsstrong robustness under severe modality degradation. Code and modelsare available at https://github.com/zaoxienian/RA-SOD.

ECCV 2026

QuantV2X: A Fully Quantized Multi-Agent System for Cooperative Perception

Seth Zhao, Huizhi Zhang, Zhaowei Li, Juntong Peng, Anthony Chui, Zewei Zhou, Zonglin Zonglin, Hao Xiang, Zhiyu Huang, Fujia Wang, Ran Tian, Chenfeng Xu, Bolei Zhou, Jiaqi Ma

Cooperative perception through Vehicle-to-Everything (V2X) communication offers significant potential for enhancing vehicle perception by mitigating occlusions and expanding the field of view. However, past research has predominantly focused on improving accuracy metrics without addressing the crucial system-level considerations of efficiency, latency, and real-world deployability. Noticeably, most existing systems rely on full-precision models, which incur high computational and transmission costs, making them impractical for real-time operation in resource-constrained environments. In this paper, we introduce QuantV2X, the first fully quantized multi-agent system designed specifically for efficient and scalable deployment of multi-modal, multi-agent V2X cooperative perception. QuantV2X introduces a unified end-to-end quantization strategy across both neural network models and transmitted message representations that simultaneously reduces computational load and transmission bandwidth. Remarkably, despite operating under low-bit constraints, QuantV2X achieves accuracy comparable to full-precision systems. More importantly, when evaluated under deployment-oriented metrics, QuantV2X reduces system-level latency by 3.2× and achieves a +9.5 improvement in mAP30 over full-precision baselines. Furthermore, QuantV2X scales more effectively, enabling larger and more capable models to fit within strict memory budgets. These results highlight the viability of a fully quantized multi-agent intermediate fusion system for real-world deployment. The system will be publicly released to promote research in this field: https://github.com/ucla-mobility/QuantV2X.

ECCV 2026

PixVOD: Pixel-Distributed Direct Visual Odometry and Depth Estimation

Shinjeong Kim, Ignacio Alzugaray, Callum Rhodes, Paul Kelly, Andrew Davison

Images composed of 2D pixel arrays are the standard inputto computer vision algorithms, yet many underlying computations can bedistributed across pixels. Transmitting raw, redundant, and noisy pixeldata off the sensor remains inefficient, motivating a shift toward focal-plane sensor-processors that perform a significant part of the computa-tion directly within each pixel. We envision pixels synthesizing higher-level signals locally, reducing downstream load, and providing richer in-puts for higher-level vision tasks.We propose a fully parallelizable form of visual odometry and depthestimation across pixels, where sensor-processors exchange informationthrough Gaussian Belief Propagation (GBP) to achieve consensus aboutcamera motion and infer depth from per-pixel photometric observationsand a surface normal prior. To maintain geometric stability during op-timization, we introduce a keyframe-like anchoring mechanism that reg-ulates the effective baseline between frames, enabling consistent mo-tion and depth updates. Our method is evaluated on realistic datasets,demonstrating the feasibility of GBP-based pixel-level distributed odom-etry and depth estimation with keyframe anchoring on-sensor. ProjectPage: https://www.shinjeongkim.com/pixvod/

ECCV 2026

PhysRAG: Enhancing Physics-Awareness in Video Generation via Retrieval-Augmented Generation

Kexu Cheng, Zicheng Liu, Mingju Gao, Chunhe Song, Hao Tang

Developing physically aware video generation models remainsa significant challenge due to the difficulty in capturing diverse physicalphenomena, such as thermal dynamics, mechanics, and optics. In thiswork, we introduce PhysRAG, a novel pipeline that enhances physicalawareness in video generation through Retrieval-Augmented Generation(RAG). To address the issue of limited high-quality data, we design atwo-stage data filtering pipeline based on the WISA-80K dataset, result-ing in a curated set of 7K high-quality videos for training. Furthermore,we construct a physical video database and develop a mechanism to injectphysical knowledge into a video diffusion model using learnable queries.Our method achieves state-of-the-art performance in both visual qualityand physical rule compliance, surpassing existing models in benchmarkssuch as PhyGenBench and VBench. We conduct extensive ablation stud-ies to validate the effectiveness of our key components, including the datafiltering pipeline, RAG mechanism, and method for physical informationextraction. To facilitate future research, our code, data, and models areprepared for release at https://github.com/sediment1024/PhysRAG.

ECCV 2026

Partial Skeleton Visibility for Action Recognition: A Constrained Field-of-View Approach

Yingjie Dai, Tianyang Xu, Yanglin Deng, Xiao-Jun Wu, Josef Kittler

Skeleton-based action recognition has achieved remarkablesuccess by exploiting joint coordinates and their topological connec-tions, yet prevailing methods overwhelmingly assume complete and cleanskeleton inputs. In real-world deployments, such as egocentric vision,crowded surveillance, wearable devices, or edge robotics, limited field-of-view (FoV) frequently causes substantial joint visibility dropout, leadingto severe performance degradation that existing models are largely un-prepared to handle. To bridge this critical yet underexplored gap, weintroduce PartialVisGraph, a novel hypergraph framework tailored forrobust skeleton action recognition under constrained FoV. We first con-struct highly expressive hypergraphs by introducing learnable virtualhyperedges that form a soft incidence matrix, capturing flexible high-order dependencies beyond conventional pairwise graphs. We then pro-pose the Single-Head Sample-Adaptive Transformer, which adaptivelyaggregates joint features onto hyperedges while explicitly incorporatinga visibility prior. This prior selectively gates information flow, preventingoccluded or out-of-view joints from corrupting reliable feature propaga-tion. We further establish rigorous evaluation protocols with realisticFoV simulation benchmarks on NTU RGB+D 60 and 120. Extensive ex-periments demonstrate that PartialVisGraph consistently achieves state-of-the-art accuracy under partial visibility, with gains of up to 68.8% onsubsets with severe FoV restrictions compared to recent strong base-lines, while remaining superior on full-visibility settings. Our approachoffers a principled and practical pathway toward deployable skeleton-based action understanding in unconstrained environments. Resourcesrelated to the constrained FoV setting used in this work are available at:https://github.com/yaa1haa1/PartialVisGraph.

ECCV 2026

Taming Text-to-Sounding Video Generation via Advanced Modality Condition and Interaction

Kaisi Guan, Xihua Wang, Zhengfeng Lai, Xin Cheng, Peng Zhang, Xiaojiang Liu, Ruihua Song, Meng Cao

This study focuses on Text-to-Sounding-Video (T2SV) gen-eration, which aims to generate a video with synchronized audio fromtext, with both modalities aligned to the text conditions. Despite progressin joint audio-video training, two critical challenges remain: (1) text con-ditioning is a bottleneck—shared captions (TV = TA ) trigger modalinterference, while a gap persists between dense training captions andconcise inference user prompts, and (2) the optimal fusion mechanismfor cross-modal feature interaction remains unclear. To address the firstchallenge, we first propose the Cross-Referential Rewriter (CRR) cap-tion framework, a dual-agent pipeline where a Semantic Checker extractsgrounded Semantic Anchors and a Cross-Modal Rewriter generates dis-entangled caption pairs (TV and TA ), eliminating modal interference and1 →Equal contribution. Work done during an internship at Apple.2 †Project lead.3 ‡Corresponding author.bridging the training-inference gap via prompt expansion. For the sec-ond, we introduce BridgeDiT, a dual-tower di!usion transformer thatemploys Dual Cross-Attention (DCA) as a bidirectional bridge betweenvideo and audio streams, which we show through systematic compari-son to be the optimal fusion strategy for the dual-tower paradigm. Ex-tensive experiments on three benchmarks, supported by human evalu-ations, demonstrate state-of-the-art results on most metrics. Compre-hensive ablation studies further validate each component and o!er keyinsights for future T2SV systems. The codes and models are available athttps://bridgedit-t2sv.github.io/.

ECCV 2026

One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models

Xiaohao Xu, Feng Xue, Xiang Li, Haowei Li, Shusheng Yang, Tianyi Zhang, Matthew Johnson-Roberson, Xiaonan Huang

A faithful 3D world representation should account for layeredgeometry, where a single camera ray may contain multiple visible and ge-ometrically valid surfaces. Monocular depth estimation, however, reducesthis structure to one scalar depth per pixel. Transparent scenes makethis ambiguity measurable: the same ray can pass through foregroundglass and observe the background, turning the supervised target into aconvention of annotation, data, and training rather than a scene-intrinsictruth. A learned predictor exposes this convention as its depth-layerpreference. We introduce MultiDepth-3k (MD-3k), a sparse two-layer or-dinal benchmark for measuring depth-layer preference and multi-layerspatial relationship accuracy (ML-SRA). On MD-3k, leading depth foun-dation models exhibit diverse layer preferences under standard RGBinput, showing that the same layered geometry can be resolved differentlyacross models. We further find that Laplacian Visual Prompting (LVP),a training-free spectral input transformation, can substantially changethe reported layer for certain frozen models. The strongest RGB/LVPpair, DAv2-L, reaches 75.5% ML-SRA. These results suggest that depthfoundation models may express complementary geometric hypotheses thatstandard RGB inference leaves unexpressed. We invite the community torethink depth supervision and evaluation through an ambiguity-awarelens, where multiple valid 3D interpretations are treated as geometricstructure to be measured, preserved, and expressed.

ECCV 2026

OmniPoint: Universal Monocular Metric Pointcloud from Any Camera

Botao Ye, Marc Pollefeys, Ming-Hsuan Yang, Abhijit Kundu

Recovering metric 3D geometry from monocular images is a fundamental computer vision task, yet current methods remain heavily fragmented by fixed camera model assumptions and inflexible input schemes. We present OmniPoint, a unified framework designed to generalize metric reconstruction across diverse imaging sensors, including pinhole, fisheye, and equirectangular projections, while accommodating varying geometric priors. To overcome projection rigidity, OmniPoint abandons conventional planar depth regression. It instead adopts a decoupled ray and distance representation alongside a decoupled training objective, explicitly separating the camera projection model from the scene structure. To address the severe scarcity of training data for alternative cameras, we introduce a bidirectional augmentation strategy that explicitly bridges labeled perspective data and unlabeled omnidirectional domains in 3D space. Furthermore, to seamlessly integrate optional inputs like camera intrinsics or sparse depth without destabilizing the network through feature distribution shifts, we propose a robust information injection mechanism. This mechanism utilizes learnable input state embeddings to resolve architectural ambiguity and applies vectorized Gaussian smoothing to densify irregular measurements. Extensive experiments demonstrate that OmniPoint achieves state-of-the-art zeroshot performance across multiple benchmarks, establishing a robust new standard for unified monocular 3D reconstruction.

ECCV 2026

Neutralizing Token Aggregation via Information Augmentation for Efficient Test-Time Adaptation

Yizhe Xiong, Zihan Zhou, Yiwen Liang, Hui Chen, Zijia Lin, Xinhao Xu, Tianxiang Hao, Fan Zhang, Jungong Han, Guiguang Ding

Test-Time Adaptation (TTA) has emerged as an effectivesolution for adapting Vision Transformers (ViT) to distribution shiftswithout additional training data. However, existing TTA methods oftenincur substantial computational overhead, limiting their applicability inresource-constrained real-world scenarios. To reduce inference cost, plug-and-play token aggregation methods merge redundant tokens in ViTsto lower computation demands. Albeit efficient, it suffers from signif-icant performance degradation when directly integrated with existingTTA methods. We formalize this problem as Efficient Test-Time Adap-tation (ETTA), seeking to preserve the adaptation capability of TTAwhile reducing inference latency. In this paper, we start by providingan analysis showing that token aggregation inherently leads to informa-tion loss, which cannot be fully mitigated by conventional norm-tuning-based TTA methods. Guided by this insight, we propose to NeutralizeToken Aggregation via Information Augmentation (NAVIA). Specif-ically, we propose an information augmentation mechanism that em-ploys an input-level [CLS] embedding augmentation to compensate fordomain-level information loss, and a feature-level [CLS] bias augmen-tation for fine-grained layerwise information compensation. Theoreticalanalysis provides a principled motivation that augmenting a global infor-mation carrier before aggregation can improve the learnable-informationupper bound, with entropy minimization serving as a practical surro-gate objective. Extensive experiments across various out-of-distributionbenchmarks and model backbones demonstrate that NAVIA outperformsall competing methods. Notably, NAVIA improves performance by upto 3.1% while achieving 14% to 26% wall-clock speedup over the TTAstate-of-the-art, effectively addressing the ETTA challenge. We provideour official implementation at https://github.com/Bostoncake/NAVIA.

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