ECCV 2026 论文列表

Latent diffusion models pair VAEs with diffusion backbones,and the structure of VAE latents strongly influences the difficulty of dif-fusion training. However, existing video VAEs typically focus on recon-struction fidelity, overlooking latent structure. We present a statisticalanalysis of video VAE latent spaces and identify two spectral propertiesessential for diffusion training: a channel-wise eigenspectrum dominatedby a few modes, and a spatio-temporal frequency spectrum biased towardlow frequencies. To induce these properties, we propose two lightweight,backbone-agnostic regularizers: Latent Masked Reconstruction and Lo-cal Correlation Regularization. Experiments show that our Spectral-Structured VAE (SSVAE) achieves a 3× speedup in text-to-video gen-eration convergence and a 10% gain in video reward, outperformingstrong open-source VAEs. Code is available at: https://github.com/zai-org/SSVAE.

We present D-Rex, a person-specific framework for photore-alistic, relightable, expressive, and animatable full-body human avatarswith free-viewpoint rendering. Existing methods for relightable full-bodyavatars rely on explicit 3D intrinsic decomposition with analytic re-flectance models, which require accurate geometry registration and care-ful optimization to capture realistic light transport effects. This tightcoupling of relighting with avatar modeling has hindered expressiveness:to our knowledge, no existing method demonstrates strong facial anima-tion alongside relighting, limiting applicability in telepresence, gaming,and virtual production. We propose to decouple relighting entirely fromavatar modeling by treating it as an image-space post-process: a learnedtranslation from flat-lit, albedo-like renderings to a target HDR illumina-tion. To this end, we leverage the strong generative prior of a pre-trainedvideo diffusion relighting model, fine-tuned via LoRA on paired flat-litand relit frames captured in a light stage. The flat-lit driving framesare produced by an independent expressive full-body avatar frameworktrained under white-light conditions, requiring no modification to sup-port relighting, making D-Rex directly applicable to any white-lightavatar system. We demonstrate that D-Rex enables view- and tempo-rally consistent relighting while faithfully preserving expressive motionand fine-grained facial detail, outperforming physically-based relightableavatar baselines. Project page: https://vcai.mpi-inf.mpg.de/projects/DRex/

Bundle adjustment (BA) remains a critical refinement mod-ule for image-based 3D reconstruction and continues to improve geomet-ric accuracy even in learning-based pipelines. However, in low-parallaxand near-rotational regimes, classical Schur-based Levenberg–Marquardt(LM) often becomes ill-conditioned and yields unreliable pose and cali-bration estimates. We propose Gate-Guided CSS-BA, a solver-side mod-ification of Schur-LM that preserves the classical BA objective and trust-region framework while constraining each update to a geometrically in-formed low-dimensional subspace. By integrating Column Space Search(CSS) with geometry-aware gating, the method stabilizes the Schur-LMupdate without altering the estimation problem. In contrast to keyframeor state-selection approaches, all camera and point parameters remainin the optimization problem; only the update direction is restricted. Themethod serves as a drop-in replacement for existing BA pipelines. Exper-iments on both generic and challenging weak-geometry scenarios showmore stable optimization, improved relative pose accuracy, and compet-itive calibration behavior while maintaining reprojection quality.

Referring Remote Sensing Image Segmentation (RRSIS) hasachieved significant progress through the integration of VLMs and theSegment Anything Model (SAM). However, this progress largely relieson strong pre-trained capabilities, while leaving two fundamental limi-tations insufficiently addressed: (1) Architectural Weak-Coupling, wherethe unidirectional flow forces reliance on coarse VLM prompts and wastesSAM’s pixel-level structural guidance, causing localization drift; and (2)Object-Centric Semantic Bias, where models overemphasize dominantobject semantics while remaining insensitive to spatial reasoning cru-cial for RRSIS. Motivated by these observations, we propose CROSS, atightly integrated paradigm for RRSIS. First, we introduce Linguistic-Guided Cascaded Distillation (LGCD) to bridge the architectural gap,which distills SAM’s geometric affinities as soft regularizers into VLM in-termediate layers, injecting dense structural priors to refine localization.Second, Perspective-Spatial Contrastive Learning (PSCL) imposes cross-anchored constraints by mining mask-filtered deceptive distractors andspatial-linguistic counterfactuals as hard negatives, explicitly shatter-ing semantic shortcuts to enforce genuine logical consistency. Extensiveexperiments on RRSIS benchmarks demonstrate that CROSS achievesstate-of-the-art performance and maintains precise localization even un-der severe spatial description perturbations, standing as a robust newparadigm for RRSIS. https://clarence-cv.github.io/CROSS/.

Realistic motion generation from a single example remainschallenging due to the scarcity of large-scale, high-quality motion datasets,particularly for animals and custom characters. Existing approaches mainlyoperate in temporal or spatial domains, where controllability is imposedthrough joint-level or time-based constraints. However, motion signalsexhibit a natural hierarchical structure across frequency scales: low-frequency components encode the fundamental rhythm and movementpattern, while high-frequency components capture individual details andvariations. This observation suggests an alternative control dimensionbased on frequency-selective motion synthesis. We present CMDer (pro-nounced Commander), a Controllable Mode Decomposition-based SingleMotion Synthesis framework that leverages diffusion models to generatediverse motions from a single motion example. CMDer employs Multi-variate Variational Mode Decomposition (MVMD) to represent motionsequences as intrinsic frequency modes, providing an interpretable andstructured representation for motion synthesis. A lightweight U-Net dif-fusion model then learns to generate these modes, while a Mode En-hancement Block (MEB) improves local motion modeling. By perform-ing diffusion directly in the decomposed motion modes, CMDer enablesfrequency-selective control that allows users to preserve global rhythmicpatterns while varying fine-grained motion details. Experiments demon-strate that CMDer achieves generation quality comparable to state-of-the-art temporal-domain approaches while introducing a new control-lable dimension for motion synthesis. We further showcase diverse appli-cations including mode-based synthesis, cross-modality motion genera-tion, and controllable crowd animation.

Continual learning for text-to-image retrieval is essential foradapting retrieval systems to evolving user interests and emerging con-cepts. While existing methods mitigate forgetting through image-textsimilarity distillation, they overlook a critical real-world scenario: newtextual queries often describe different semantic facets of images frompreviously seen visual domains, a phenomenon we term “unbalancedcross-modal shift”. This shift arises from the inherent polysemy of vi-sual content and is strongly associated with severe forgetting. To studythis phenomenon, we introduce two diagnostic metrics: image domainoverlap measured via principal subspace similarity, and textual querydivergence quantified through image-conditioned query probability. Ourempirical analysis shows that unbalanced cross-modal shift significantlyamplifies forgetting, with existing methods struggling to retain imagesemantics under such conditions. To address this challenge, we proposeCaPCL (Caption-Preserved Continual Learning), which regularizes mod-els to maintain consistent caption generation for previously learned con-tent, thereby preserving multi-faceted image semantics. By leveragingauxiliary captions as semantic anchors, CaPCL delivers more robusttraining signals than contrastive similarity alone. We construct a bench-mark integrating five heterogeneous datasets to enable comprehensiveevaluation. Extensive experiments demonstrate that CaPCL consistentlyoutperforms state-of-the-art methods in both knowledge retention andadaptation.

Establishing reliable correspondences across diverse objectinstances is fundamental to robust 3D understanding and generalizablemanipulation. However, existing methods are often inconsistent acrossgeometries or vulnerable to non-canonicalized poses, making correspon-dence transfer unreliable. To address this, we propose XSemanticFlow,which learns correspondences from pure semantic features. Given per-object semantic feature _x001C_elds, XSemanticFlow applies hierarchical intra-object self-attention and inter-object cross-attention to predict soft cor-respondence maps. By restricting cross-attention strictly to semantic to-kens, XSemanticFlow avoids geometric over_x001C_tting, enabling it to predictgeneralizable correspondences across di_x001B_erent topologies and producealigned feature _x001C_elds. This enables XSemanticFlow to learn e_x001B_ectivelythrough self-supervised cross-pose alignment on large-scale unlabeledshapes, followed by supervised cross-instance _x001C_netuning on a cosegmen-tation dataset containing labeled object pairs from PartNet. We eval-uate our method on both 3D understanding and manipulation tasks.For 3D understanding, XSemanticFlow improves self-segmentation withSE(3) augmentations by +19.1 mIoU and +20.6 accuracy, and cross-instance cosegmentation by +10.9 mIoU and +12.5 accuracy over base-lines, demonstrating stronger transform consistency and semantic align-ment. For manipulation, XSemanticFlow provides a reliable alternativeto recent video-generation-based pipelines for zero-shot manipulation.Rather than relying on synthesized visual plans, we directly transfer con-tact regions and end-e_x001B_ector trajectories from a single reference demon-stration to novel instances, bypassing the brittle video generation failurescommon in contact-critical tasks.

Vision transformers (ViTs) have achieved remarkable gen-eralization across visual domains, yet little is known about how theyinternally represent the structure of the world. To address this gap, weuse Cross-Layer Transcoders (CLTs) to read concept circuits from ViTs:directed graphs whose nodes correspond to sparse, interpretable con-cepts and edges capture concept interactions across layers. Our methodyields two complementary views of model behavior. The global conceptcircuit is input-invariant and can be recovered directly from learnedcross-layer weights, exposing the reusable “world knowledge” encodedin the model. The instance concept circuit is input-dependent and iden-tifies the concepts and pathways actually used for a specific prediction,enabling faithful example-level explanations. We demonstrate the util-ity of concept circuits in three ways: (1) Automatic spurious correla-tion discovery: leveraging the statistics of our global concept circuitsto identify shortcut dependencies within the model. (2) Spurious cor-relation removal: intervening on the instance concept circuit to steerthe model towards correct predictions. Empirical results show that ourmethod outperforms existing counterparts by 11.0% on the Waterbirddataset. (3) Model comparison: contrasting the global concept circuits ofdifferent foundation models (e.g., CLIP vs. DINO) to reveal how super-vision paradigms shape representational structure. Our code is availableat https://github.com/deep-real/VisionCLT

Vision-Language Large Models (VLLMs) are promising forAI-generated image (AIGI) detection because they can produce both aprediction and a natural-language output. However, most existing VLLM-based detectors primarily fine-tune the language side while giving lim-ited attention to low-level visual forensic cues. They also often dependon manually crafted prompts or human-annotated rationales, which lim-its scalability. We present UC-VLM, a unified multi-stage frameworkfor AIGI detection that relies solely on binary supervision. UC-VLMfirst identifies effective instruction variants automatically. It then reusesthe same binary label within a multi-stage training framework: (i) a vi-sual discrimination objective that strengthens sensitivity to non-semanticforensic cues, and (ii) a label-conditioned generation objective that usesthe binary label to supervise textual outputs. This design turns weakbinary supervision into a shared supervision signal for both the visualpathway and the language output. Our key novelty is a unified multi-stage binary-supervised framework that consistently reuses the same au-thenticity labels for visual adaptation and label-conditioned text gener-ation, while leveraging automatically optimized instructions to reduceprompt sensitivity without requiring human-written rationales or hand-crafted prompts. Experiments show that UC-VLM achieves 96.1% aver-age accuracy on GenImage, exceeding the strongest prior result by 4.6%,and obtains 69.6% / 77.9% accuracy on Chameleon under ProGAN /SDV1.4 training, surpassing the best baseline by 11.2% / 15.3%, re-spectively.

History context is central to autoregressive video generation,driving consistency and storytelling for both commercial models andpersonal use cases. For example, personal users, offline workflows, andindividual-scale finetuning need to encode longer video histories undertight compute and memory budgets. We observe that content and identityconsistency is an essential requirement, and that complete, uninterruptedhistory coverage together with content query and interpretation capabil-ities is broadly desired. We present TinyHistory, a lightweight historyembedding learned through two-stage context learning. In the first stage,we pretrain the encoder on large-scale video data with a randomized framequery objective; in the second stage, we repurpose the pretrained encoderwithin an autoregressive video diffusion model to learn content-level con-sistency. As a result, we show that the learned lightweight embeddingsachieve consistency comparable (by VLM, VBench, ELO, etc.) to heavieralternatives, while reducing training overhead and extending the encod-able history length within a given memory budget. We conduct ablationstudies to analyze the influence and trade-offs of each component.

Video Temporal Grounding (VTG) aims to localize specificvideo segments corresponding to natural language queries. While recentLarge Vision-Language Models (LVLMs) employ Reinforcement Learn-ing to generate Chains-of-Thought (CoT), they typically rely solely onoutcome-based supervision. Consequently, this often leads to halluci-nations, where the reasoning process becomes disconnected from thevisual content and the final prediction. Existing attempts to mitigatethis by relying on external supervision from larger models or separatereward models are computationally expensive and prone to rigid pat-terns. To address these challenges, we propose TAR (Temporal Anchor-Constrained Reasoning), a framework that introduces the temporal an-chor (T-anchor) as a transparent and auditable checkpoint mechanism.T-anchor enforces progressive refinement within the CoT, compellingthe model to continuously ground its intermediate thoughts in visual evi-dence and iteratively calibrate temporal predictions, thereby significantlyenhancing the faithfulness and autonomy of the reasoning process and fi-nal accuracy. Furthermore, we introduce a bootstrapping paradigm thatautomatically harvests high-quality CoT data using only a standard 7Bmodel, eliminating the dependency on ultra-large models. Extensive ex-periments demonstrate that TAR achieves state-of-the-art performanceand generates faithful, autonomous, and progressively refined reasoningtraces.

Recent progress in image and video synthesis has inspiredtheir use in advancing 3D scene generation. However, we observe thattext-to-image and -video approaches struggle to maintain scene- andobject-level consistency beyond a limited environment scale without apersistent, explicit geometric representation. We thus present a geometry-first approach that decouples this complex problem of large-scale 3Dscene synthesis into its structural composition, represented as a meshscaffold, and realistic appearance synthesis, which leverages powerful im-age synthesis models conditioned on the mesh scaffold. From an inputtext description, we first construct a mesh capturing the environment’sgeometry (walls, floors, etc.), and then use image synthesis, segmentationand object reconstruction to populate the mesh structure with objects inrealistic layouts. This mesh scaffold is then rendered to condition imagesynthesis, providing a structural backbone for consistent appearance gen-eration. This enables scalable, arbitrarily-sized 3D scenes of high objectrichness and diversity, combining robust 3D consistency with photoreal-istic detail. We believe this marks a significant step toward generatingtruly environment-scale, immersive 3D worlds.

Accurate survival modeling using whole slide images (WSI) is crucial for guiding cancer treatment and improving patient outcomes, yet it remains challenging due to the gigapixel resolution, small data cohorts, and limited annotations. In recent years, pathology foundation models (FM) have shown impressive performance improvements over the previous benchmarks set by the ImageNet-based backbones across a variety of WSI analysis tasks. However, the performance of individual FMs, especially in a secondary analysis task such as survival modeling, varies depending on the dataset, without a clear consensus for a superior FM. With over 25 publicly available pathology FMs so far, in this work, we address the growing challenge of model selection for survival analysis in digital pathology. We propose SurvMILKD, the first multi-teacher knowledge distillation (MKD) framework for WSIbased survival analysis. To efficiently enable MKD with high resolution of the WSIs, we integrate the weakly supervised multiple instance learning (MIL) adapters in our framework. The objective of the framework is to distill the complementary task-specific knowledge from multiple teacher FMs into the student FM, which, during inference, has collective knowledge of all FMs involved during training. Additionally, we introduce the risk-aware distillation loss to distill outcome-specific knowledge from multiple teachers into the student model. Our experiments on eight public datasets (including two external evaluation datasets) demonstrate that the proposed approach outperforms the individual FMs, their ensemble, as well as the prior MKD approaches that we benchmarked on survival analysis for the first time in literature. The codes are available at https://github.com/AIMLab-UBC/SurvMILKD.

We study dynamic Gaussian Splatting from monocular videos.While recent advancements in dynamic Gaussian Splatting offer a promis-ing foundation for modeling dynamic scenes, they often overfit to thetraining views and fail under occlusion or complex scene motion due tothe lack of reliable regularization signals in under-constrained regions.We propose Semantic Motion Graph (SMG), a novel approach that mod-els the Gaussian motion as the semantic motion. Our key insight is thatthe real-world scene motion is often structured by semantic coherence:regions that are spatially close and semantically related tend to exhibitconsistent dynamics. To leverage this prior, we build SMG to modelstructured motion of the scene. The Gaussian motion is driven by the mo-tion of SMG nodes. We further observe that the uncertainty of Gaussianmotion arises from both unreliable off-the-shelf priors and weakly con-strained regions during optimization. SMG addresses this by using reli-able graph nodes to guide the motion of nearby unreliable nodes. To eval-uate dynamic Gaussian Splatting under challenging real-world scenarios,we introduce a new multiview dataset collected under an ego-exo setup.Extensive experiments demonstrate that SMG achieves state-of-the-artperformance on monocular dynamic Gaussian Splatting across challeng-ing real-world benchmarks. Project: https://smg-gaussian.github.io/.

Sign language video generation demands precise hand andfacial articulation, yet modern video diffusion models, trained predom-inantly on spoken-language video, produce artifacts that render signingunintelligible. We propose SignRefine, a sign language video genera-tion model that produces comprehensible signing from 2D keypoint con-ditioning alone, generalizing across appearances and visual conditions.Our approach builds on a pretrained video diffusion transformer and in-troduces local adapters with spatial grounding to selectively refine handand face regions, steering the strong base model’s prior toward accu-rate articulation. To enable this work and support broader sign languageresearch, we present NVSign, a large-scale dataset of video content na-tively produced in sign language, offering diverse signer appearances,environments, and natural conversational settings. Trained on this data,our model shows up to 30% improvement in hand pose precision metricsover the strongest baseline and is preferred by sign language users forvisual quality and comprehensibility in more than 80% of comparisons.

Large Vision-Language Models (LVLMs) excel at multimodaltasks but remain prone to object hallucinations. Prior training-free reme-dies often uniformly strengthen visual signals, which may also amplifyirrelevant regions and introduce spurious evidence, harming fluency. Wepropose Context-aware Attention Intervention (CAI), a training-free inference-time mechanism that enforces a see only when needed prin-ciple via two-axis selectivity: where to look and when to intervene. Ateach decoding step, CAI derives token-specific visual relevance fromearly-layer representations to localize semantically aligned regions, andapplies a conservative, entropy- and depth-gated attention tilt only foruncertainty-spiking tokens in deeper layers where visual grounding de-grades, leaving confident tokens and irrelevant regions largely unchanged.This targeted intervention strengthens visual grounding while preserv-ing linguistic fluency, and it yields consistent improvements even with-out contrastive decoding, which remains optional as an auxiliary bias-suppression module. Extensive experiments across multiple LVLM back-bones and benchmarks show that CAI achieves state-of-the-art halluci-nation mitigation, and our analysis characterizes CAI as a KL-minimalattention reweighting with bounded interference under inactive gates orsmall tilts. Code is available at https://github.com/Iris1946/CAI.

We propose triangular consistency as a first-principled con-straint for optical flow, which is agnostic to network architecture, super-vision type, and dataset, and applies to both image-pair and multi-framesettings. This simple but powerful constraint is to compose two flows toinduce a third flow and enforce consistency among the three. The com-posed flows may arise from (i) image pairs, yielding cycle consistency; (ii)multiple video frames, producing longer-range motion through temporalchaining; or (iii) image pairs combined with controlled synthetic transfor-mations, which becomes data augmentation. This triangular consistencyintroduces negligible computational overhead and requires no additionalannotations. Since it is derived directly from the geometry of opticalflow, it does not rely on model-specific assumptions and serves as a “uni-versal” plug-and-play component for optical flow training. Experimentsshow consistent improvement across supervised, unsupervised, and trans-fer learning settings. Code: https://github.com/lsuvision/tri-flow.

Scaling monocular 3D Gaussian Splatting (3DGS) SLAM to kilometer-level outdoor environments poses two tightly coupled challenges: fragile long-term pose tracking and excessive memory overhead during large-scale mapping. In this paper, we propose KiloGS-SLAM, a highly efficient and robust monocular 3DGS-SLAM system that jointly addresses both bottlenecks. Since high-fidelity scene reconstruction fundamentally relies on drift-free camera poses, we first introduce a motionadaptive hybrid tracking module. This module features a conditiontriggered three-tier solving pipeline. It dynamically switches between Essential matrix and PnP models to handle geometric degeneracies. An ondemand foundation model can also be activated to rescue the trajectory from catastrophic drift. To ensure the system can sustain these long trajectories without memory exhaustion, we subsequently design a lifecyclemanaged Gaussian mapping strategy. By integrating probabilistic initialization with chunk-based multi-view densification and pruning, this full-pipeline optimization effectively reduces primitive redundancy while preserving high-frequency details. Together, the robust tracking guarantees the geometric foundation required for accurate mapping, while the memory-efficient lifecycle-managed mapping enables large-scale operation. Extensive experiments across three challenging outdoor datasets demonstrate that our approach achieves state-of-the-art tracking accuracy and rendering quality, successfully scaling to sequences of over 10,000 frames on a single GPU. Project Page: https://3dagentworld. github.io/KiloGS-SLAM/

Multimodal large language models (MLLMs) have demon-strated powerful capabilities in general spatial understanding and rea-soning. However, their fine-grained spatial understanding and reasoningcapabilities in complex urban scenarios have not received significant at-tention in the fields of both research and industry. To fill this gap, wefocus primarily on road markings as a typical example of fine-grainedspatial elements under urban scenarios, given the essential role of theintegrated road traffic network they form within cities. Around roadmarkings and urban traffic systems, we propose RoadBench, a system-atic benchmark that comprehensively evaluates MLLMs’ fine-grainedspatial understanding and reasoning capabilities using Bird’s-Eye View(BEV) and First-Person View (FPV) image inputs. This benchmarkcomprises eight tasks consisting of 3,040 strictly manually verified testcases, constructed from 2,137 unique BEV images and 721 unique FPVimages collected from five Chinese cities with relatively consistent trafficconventions. These tasks form a systematic evaluation framework thatbridges understanding at local spatial scopes to global reasoning. Theynot only test MLLMs’ capabilities in recognition, joint understanding,and reasoning but also assess their ability to integrate image informationwith domain knowledge. After evaluating 20 mainstream MLLMs, weconfirm that RoadBench is a challenging benchmark for MLLMs whilerevealing significant shortcomings in existing MLLMs’ fine-grained spa-tial understanding and reasoning capabilities within urban scenarios. Incertain tasks, their performance even falls short of simple rule-basedor random selection baselines. These findings, along with RoadBenchitself, will contribute to the comprehensive advancement of spatial un-derstanding capabilities for MLLMs. The benchmark code is availableat https://github.com/tsinghua-fib-lab/RoadBench, and the supple-mentary material provides example data, prompts, evaluation scripts,and raw evaluation results.Road MarkingsBEV Image # of Lanes: 4Road MarkingsFPV Image # of Lanes: 4Fig. 1: Examples of road markings in BEV and FPV images. Road markings such aslane dividers and turning arrows provide fine-grained visual cues for lane counting andlane designation recognition.