ECCV 2026 Papers

Recent progress in CLIP-like vision-language models (VLMs)has greatly advanced medical image analysis. However, most existingmedical VLMs still rely on coarse image-text contrastive objectives andfail to capture the systematic visual knowledge encoded in well-definedmedical phenotype ontologies. To address this limitation, we constructPhenoKG, the first large-scale, phenotype-centric multimodal knowl-edge graph that encompasses around 524K high-quality image-text pairslinked to more than 3,000 phenotypes. Building upon PhenoKG, we pro-pose PhenoLIP, a pretraining framework that explicitly incorporatesstructured phenotype knowledge into medical VLMs through a two-stage process. We first learn a knowledge-enhanced phenotype embed-ding space that captures the hierarchical structure of phenotype ontolo-gies and then distill this textual knowledge into multimodal pretrainingvia a teacher-guided knowledge distillation objective. To support eval-uation, we further introduce PhenoBench, an expert-verified bench-mark designed for phenotype recognition, comprising over 7,800 image-caption pairs covering more than 1,000 phenotypes. Extensive experi-ments demonstrate that PhenoLIP outperforms previous state-of-the-artbaselines, improving upon BIOMEDICA in phenotype classification ac-curacy by 8.06% and in cross-modal retrieval by 15.03%, underscoringthe value of integrating phenotype-centric priors into medical VLMs forstructured and interpretable medical image understanding.

Industrial anomaly inspection faces a major challenge dueto the lack of real-world anomaly samples. While generative models areused to create anomaly data, existing methods still struggle when han-dling small-scale anomalies. This failure occurs because extreme down-sampling in diffusion models causes the information of small anomaliesto be lost in the latent space. To address this, we introduce UniScale,a unified training and inference framework for high-fidelity industrialanomaly generation across arbitrary scales. During training, we intro-duce an Error-Suppressed Multi-Scale Training (EMT) strategy, whichenables the model to learn the rich location-aware textures of anomalies,while suppressing upsampling-induced interpolation errors in texture ac-quisition, ensuring the model is capable of learning small-scale anomalies,while remaining effective for regular scale anomalies. For inference, wepropose Generation-then-Fusion Denoising. It decouples anomaly genera-tion from background integration, preventing small anomalies from beingoverwhelmed. Extensive experiments demonstrate that our method out-performs state-of-the-art competitors in both anomaly generation qualityand downstream detection performance. It achieves a relative IS(a) im-provement of 45.86% (from 1.81 to 2.64) on VisA and 37.70% (from 1.22to 1.68) on MVTec AD 2, while also improving the downstream pixel-level IoU by 4.22% on VisA and AUROC by 6.55% on MVTec AD 2.Code is available at https://github.com/HUST-SLOW/UniScale.

Self-supervised learning (SSL) has become a leading paradigmfor skeleton-based action recognition, yet the choice of prediction targetremains a central limitation. Existing masked modeling methods typi-cally reconstruct raw joint coordinates, emphasizing low-level detail andremaining sensitive to noise, while recent non-reconstruction variantspredict learned latent features that improve semantics but capture lit-tle explicit motion structure. We propose Path-JEPA, a self-supervisedpredictive learning framework that adopts path signatures as the predic-tion target for skeleton sequences. Rather than predicting coordinatesor abstract embeddings, Path-JEPA predicts latent representations ofcontinuous-time, multi-scale geometric descriptors computed over joint,edge, and chain paths, capturing motion across multiple spatial and tem-poral scales. These targets encode displacement, signed area, and higher-order interactions, yielding a geometry-grounded representation of hu-man motion that is naturally robust to temporal resampling and frame-rate variation. To make such targets effective within a JEPA framework,we introduce signature-augmented masking, which propagates joint-spacemasks to all dependent signature tokens, forcing the model to infer themissing motion geometry from broader anatomical and temporal context.Because signatures summarize motion over intervals rather than indi-vidual frames, they also yield greater computational efficiency on longsequences. Extensive experiments on NTU RGB+D 60, NTU RGB+D120, and PKU-MMD show that Path-JEPA learns stronger representa-tions than prior masked prediction methods, achieving state-of-the-artperformance across diverse downstream tasks while exhibiting improvedrobustness to irregular sampling. Project website:Path-JEPA.

Reconstructing 3D geometry from streaming video requirescontinuous inference under bounded resources. Recent geometric foun-dation models achieve impressive reconstruction quality through all-to-all attention, yet their quadratic cost confines them to short, offline se-quences. Causal-attention variants such as StreamVGGT enable single-pass streaming but accumulate an ever-growing KV cache, exhaustingGPU memory within hundreds of frames and precluding the long-horizondeployment that motivates streaming inference in the first place. Wepresent OVGGT, a training-free framework that bounds both memoryand compute to a fixed budget regardless of sequence length. Our ap-proach combines Self-Selective Caching, which leverages FFN residualmagnitudes to compress the KV cache while remaining fully compatiblewith FlashAttention, with Dynamic Anchor Protection, which shieldscoordinate-critical tokens from eviction to suppress geometric drift overextended trajectories. Extensive experiments on indoor, outdoor, andultra-long datasets show that OVGGT processes arbitrarily long videoswithin a constant VRAM envelope while achieving state-of-the-art 3D ge-ometric accuracy. The code is available at github.com/VAISR/OVGGT.

Facial landmark detectors trained on real human faces oftenfail to generalize effectively to stylized domains such as caricatures andartistic portraits, necessitating unsupervised domain adaptation (UDA).Although self-training is a widely used UDA strategy to bridge do-main gaps, it frequently breaks down under large domain shifts as it isprone to amplifying confident yet erroneous pseudo-label predictions. Tothis end, we propose PriSM (Parsing and Style-Mixed Consistency), anovel method for robust landmark pseudo-label validation. PriSM lever-ages two complementary signals: a Parsing Network enforces high-levelstructural alignment through face parsing consistency, while a StyleMixNetwork enforces fine-grained geometric constraints by reducing the re-construction error between the input face and another face synthesizedfrom the pseudo-label’s structure and the input face’s appearance. Ex-tensive experiments on the challenging CariFace and ArtiFace bench-marks under the UDA setting demonstrate that PriSM significantly out-performs existing state-of-the-art methods and exhibits strong gener-alizability to unseen domains. The source code is available at: https://github.com/jerrychild999922/PriSM-UDA.

Differentiable rendering of planar rational splines remains largely underexplored, despite their widespread use in vector graphics and design. Existing differentiable vector renderers primarily focus on Bézier curves and rely on analytic rasterization, which can suffer from gradient instability and limited flexibility. We propose NURBS Splatting, a unified framework that represents planar rational curves as continuous Gaussian fields. By sampling Gaussians along the curve parameter domain and inside closed regions, rendering is reformulated as a smooth accumulation process with stable gradients. Our method naturally supports long splines, rational weights, non-uniform knots, and closed-region filling. We demonstrate its effectiveness in calligraphy reconstruction, vectorization frameworks, and long-spline image abstraction, showing improved stability and reconstruction quality over existing approaches.

Multi-modality image fusion (MMIF) enhances scene representation by exploiting complementary cues from different modalities. Adverse weather, however, causes significant image degradation, disrupting feature representation and requiring simultaneous feature restoration and cross-modal complementarity. Existing methods often struggle with effective representation learning under such conditions, limiting their practical performance. To address these challenges, we propose a mask-guided MMIF method that integrates feature restoration and interaction. We first introduce "Pseudo Ground Truth" to simplify training, promoting faster and more effective feature learning. Then, we design a mask generation mechanism based on the mapping relationship between the fused result and the source images, quantifying the relative contribution of each modality during the fusion process. By incorporating the proposed mask-guided cross-modal cross-attention mechanism, the network is encouraged to selectively attend to informative features during modality interaction, mitigating the risk of overfitting to the static distribution of the "Pseudo Ground Truth". Additionally, we propose a mask-guided learning strategy and a task-coupled degradation-aware learning strategy to balance feature restoration and interaction. Extensive experiments on synthetic and real-world datasets demonstrate that our method surpasses state-of-the-art approaches in visual quality, quantitative metrics, and downstream tasks. The source code is available at https://github.com/ixilai/AMG-Fuse.

We present Split-then-Merge (StM), a controllable genera-tive video composition framework that minimizes reliance on annotateddatasets and handcrafted rules. Instead of requiring manual supervision,StM decomposes unlabeled videos into dynamic foreground and back-ground layers. By self-composing these elements, the model learns tosynthesize complex interactions between moving subjects and diversescenes, capturing the intricate dynamics necessary for high-_x001C_delity videogeneration. Speci_x001C_cally, StM introduces a transformation-aware train-ing pipeline utilizing multi-layer fusion and augmentation to address af-fordance challenges in video composition. An identity-preservation lossfurther maintains foreground _x001C_delity during blending. Extensive exper-iments show that StM outperforms state-of-the-art methods on bothquantitative benchmarks and qualitative evaluations, including humanstudies and Vision-Language Model assessments. Finally, we release StM-50K, the _x001C_rst multi-layer video dataset, to facilitate future research ingenerative video composition. Data, code, and more details are availableat our project page.Fig. 2: Video Composition. Given input foreground and background videos, image-based methods (a)_x0015_(b) use only the _x001C_rst frame, while (c)_x0015_(e) take full video inputs.(a) Object insertion [59] followed by Image-to-Video (I2V) and (b) end-to-end I2Vcomposition SkyReels [13] fails to retain motion due to lack of video access. (c) Manualcopy-paste preserves motion but violates a_x001B_ordance (swan placed on ground). (d) Naivegenerative composition yields appearance and motion drift (e.g ., black swan turnswhite). (e) Our method preserves identity and motion, and achieves a_x001B_ordance-awareplacement with realistic blending (swan placed in water with wave and shadows).

Diffusion models have recently achieved remarkable successin high-fidelity image synthesis, yet their application to visual text gener-ation and editing remains relatively underexplored. Unlike general imagegeneration, visual text tasks demand precise structural regularity andlegibility, which may pose additional challenges for small-scale text andnon-Latin scripts such as Chinese. Existing UNet-based models oftenstruggle to produce clear and coherent text, while DiT-based models,though more expressive, are typically limited to a single task, whichmay lead to redundant training pipelines, inconsistent visual styles, andreduced cross-task generalization. To address these challenges, we pro-pose InnoText, a unified DiT-based framework capable of performingboth text generation and editing within a single model. We introduce aFont Size-Aware Modulation (FSAM) module to enhance representationsacross font scales, a Small-Character Aware Augmentation strategy toimprove fine-grained fidelity, and a Task-Specific Region Weighted Lossfor adaptive optimization. To support training and evaluation, we alsoconstruct a high-quality bilingual (English-Chinese) visual text datasetcovering diverse fonts, sizes, and backgrounds. Experimental results demon-strate that our method achieves superior generation accuracy and editingquality, producing visually appealing and realistic text images.

Vision foundation models trained with self-supervised objec-tives achieve strong performance across diverse tasks and exhibit emer-gent object segmentation properties. However, their alignment with hu-man object perception remains poorly understood. Here, we introducea behavioral benchmark in which participants make same/different ob-ject judgments for dot pairs on naturalistic scenes, scaling up a classicalpsychophysics paradigm to over 1000 trials. We test a diverse set ofvision models using a simple readout from their representations to pre-dict subjects’ reaction times. We observe a steady improvement acrossmodel generations, with both architecture and training objective con-tributing to alignment, and transformer-based models trained with theDINO self-supervised objective showing the strongest performance. Toinvestigate the source of this improvement, we propose a metric to quan-tify the object-centric component of representations by measuring patchsimilarity within and between objects. Across models, stronger object-centric structure predicts human segmentation behavior more accurately.We further show that matching the Gram matrix of supervised trans-former models, capturing similarity structure across image patches, withthat of a self-supervised model through distillation improves their align-ment with human behavior, converging with the prior finding that Gramanchoring improves DINOv3’s feature quality. Together, these resultsdemonstrate that self-supervised vision models capture object structurein a behaviorally human-like manner, and that Gram matrix structureplays a role in driving perceptual alignment. Code and data are availablehere.

Existing diffusion-based methods have recently made signif-icant progress in image dehazing. However, they typically neglect thephysics of haze formation and reconstruct clean images from pure Gaus-sian noise, thereby limiting their restoration potential. To address thisissue, we propose Haze-Noise Diffusion (HNDiff), a novel diffusion frame-work that embeds the atmospheric scattering model as an inductive bias.By grounding diffusion in physical principles, HNDiff ensures that therestoration aligns more closely with underlying mechanisms of haze for-mation. In its forward process, we introduce joint haze-noise diffusionwith a haze-aware noise scheduler, which progressively adds both hazeand noise to an image. Essentially, the scheduler adapts noise levels ac-cording to haze density, meaning that regions with heavier haze receivestronger noise injection to encourage content generation, while clearer re-gions receive lighter noise to better preserve details, which directly linksthe forward degradation process with the physics of haze. In the reverseprocess, we then derive a physically consistent dehazing-denoising pro-cess that simultaneously removes haze and noise to restore a clean imagein a manner aligned with the forward degradation process. To furtherenhance practicality, we propose Latent HNDiff, which compiles cleanlatent priors that can be seamlessly integrated into existing dehazing net-works to boost performance. Extensive experiments show that our worksignificantly improves leading dehazing backbones and achieves state-of-the-art results on benchmark datasets. The project page is available athttps://jin-ting-he.github.io/HNDiff/.

Reconstructing 3D scenes of objects displayed in glass enclo-sures remains a significant challenge for 3D Gaussian Splatting (3DGS),particularly in museum and exhibition hall settings where transparentprotective cases are ubiquitous. The high specular reflections (HSR) gen-erated by glass surfaces severely degrade reconstruction quality, yet theirunderlying properties and impact on 3DGS remain poorly understood.Through systematic investigation, we reveal two insights into HSR be-havior in 3DGS: (1) Geometry-inconsistency property: HSR causes 3Dpoints to exhibit drastically different appearances across viewpoints, fun-damentally violating the geometric consistency assumption that under-pins 3DGS; (2) Concept-preservation property: Despite corrupting lo-cal appearance, HSR has remarkably limited influence when learninghigh-level object concepts, as the true object structure remains intactbeneath the reflective layer. Motivated by these insights, we proposeGlassGS, a geometry and concept-aware 3DGS framework with an it-erative two-stage optimization. First, we compute geometry-consistencymaps for each input view to evaluate the appearance variations acrossviewpoints, explicitly identifying HSR-affected pixels by measuring theappearance variations of 3D points across views. Second, we introducea geometry-inconsistency-guided object concept learning module thatleverages these maps to learn robust object representations while activelysuppressing reflection interference. The two stages mutually reinforceeach other through iterative refinement, improving both inconsistencydetection and concept learning. For this new task, we collect the firstdataset specifically designed for 3DGS reconstruction of glass-enclosedobjects. Extensive experiments demonstrate that GlassGS significantlyoutperforms state-of-the-art methods, enabling high-fidelity digitizationof glass-enclosed scenes and opening new possibilities for virtual museumexperiences.

Accurate Remaining Useful Life (RUL) prediction is funda-mental to Prognostics and Health Management, yet remains challeng-ing due to stochastic degradation and non-stationary vibration patterns.Existing image-based approaches predominantly rely on time–frequencyrepresentations (e.g., spectrograms or wavelet transforms), which cap-ture spectral energy variations but overlook the intrinsic geometric struc-ture of system dynamics. In this work, we reformulate RUL predictionthrough a phase-space-inspired visual representation. We introduce thePhase Space Density Image (PSDI), a novel representation that encodesthe spatial density of time-delay embedded trajectories. Unlike spectralheatmaps, PSDI characterizes degradation as a progressive geometric dis-persion of the system attractor, revealing structural transitions invisibleto conventional representations. To ensure that these geometric changesreflect true degradation rather than coordinate drift, we further proposea Globally Anchored Reconstruction strategy that enforces consistentphase-space alignment across samples and time. We evaluate the effec-tiveness of the proposed representation across multiple vision architec-tures and integrate it with a compact knowledge distillation frameworkthat transfers useful visual priors while reducing prediction jitter throughtemporal aggregation. Experiments on benchmark datasets demonstratethat PSDI achieves competitive and robust performance compared withclassical signal representations and state-of-the-art time-series models,supporting phase-space density maps as an effective visual representa-tion for vibration-based degradation tracking.

Generating interactive and dynamic 4D scenes from a single static image remains a core challenge. Most existing generate-then-reconstruct and reconstruct-then-generate methods decouple geometry from motion, causing spatiotemporal inconsistencies and poor generalization. To address these, we present MoGe4D (Motion and GeometryAware image-to-4D Synthesis), a geometry-conditioned framework for single-image 4D synthesis that models a scene as dense 4D point trajectories. Instead of treating geometry and dynamics as two disconnected stages, our method starts from an initial geometric prior inferred from the input image and predicts future time-varying trajectories in a diffusion process, improving spatiotemporal coherence while preserving structural stability. To support this task, we first introduce TrajScene-60K, a large-scale dataset of 60,000 video samples with dense 4D point trajectories, addressing the scarcity of high-quality training data for scene-level 4D generation. Built on this, our diffusion-based 4D Scene Trajectory Generator (4D-STraG) predicts geometry-consistent and motionplausible trajectory fields conditioned on the input image, with a depthguided motion normalization strategy to reduce scale ambiguity and a Motion Perception Module (MPM) to inject motion-aware priors. We further propose a 4D View Synthesis Module (4D-ViSM) to render the generated 4D representation into videos under arbitrary camera trajectories. Experiments show that MoGe4D produces high-quality 4D scenes with strong temporal coherence, favorable geometry-aware consistency, and compelling novel-view synthesis from a single image. Code: https://github.com/Zhangyr2022/MoGe4D.

Scalable Vector Graphics are a fundamental medium for reso-lution-independent visual content, yet the deep learning community lacksa continuous, dense, and invertible latent space for vector representa-tions, the kind of foundational building block that Variational Autoen-coders and their descendants have long provided for raster images. Weintroduce SLS (SVG Latent Space), a Transformer-based autoencoderthat learns compact dense representations of individual SVG paths, theatomic visual elements from which any SVG image can be composed. Bymodeling SVG commands, coordinate data, and visual properties withina unified BPE-based token vocabulary, SLS learns fixed-size latent rep-resentations that jointly capture structure and appearance, and can bedecoded back into valid, style-consistent SVG paths with high fidelity.The resulting embedding space is robust, invertible, and structured: em-beddings lie on a unit hypersphere, enabling efficient similarity search,composition, and downstream conditioning through simple vector-spaceoperations. Finally, we demonstrate that SLS generalizes across diversetasks reducing their FLOPs by over 150× compared to token-based ap-proaches, and establishing a general-purpose latent foundation for vectorgraphics research.

Inference-time scaling for text-to-image generation has pro-gressed from simple Best-of-N (BoN) sampling to guided search meth-ods that verify and steer candidate trajectories at intermediate denoisingsteps. These approaches focus on when and how often to verify duringdenoising but largely treat the cost of generation itself as fixed. More-over, the standard practice of comparing methods by number of functionevaluations (NFEs) counts only denoising forward passes and ignores ver-ifier overhead, which can distort e!ciency rankings. We show that underwall-clock evaluation, simple BoN already matches or outperforms sev-eral guided search techniques, suggesting that compute is better spent onbroader exploration than on repeated intermediate verification. This mo-tivates Flash-BoN, which generates a large pool of inexpensive draft can-didates by combining three complementary acceleration knobs: timesteptruncation, layer skipping, and activation proxies into a single config-uration optimized once per model. An e!cient multi-stage verificationprocedure then identifies the most promising draft, which is refined at fullquality. Across three benchmarks and three model scales, Flash-BoN con-sistently outperforms all baselines under fixed wall-clock budgets, withgains that grow at larger model scales (+8% AUC). We further showthat our strategy combines well and improves existing orthogonal tech-niques such as reflection-based prompt optimization (+16% AUC). Thegains correlate with increased candidate diversity, which also enablesdraft-guided selection to accelerate RL post-training convergence.

Localizing document tampering is extremely challenging, asmanipulations are crafted to appear visually consistent and often leaveonly subtle traces that are nearly invisible to the human eye. In priorwork, evaluation has been largely dominated by synthetic benchmarksthat closely match the training distribution, and methods have shownsteady progress under this setting. However, these gains often translatepoorly to human-made forgeries and to cross-domain evaluation, whereboth the source documents and the tampering pipeline can change, lead-ing to a distribution shift. In addition, since the introduction of the Fre-quency Perception Head for the discrete cosine transform (DCT) modal-ity, it has become a standard choice, and subsequent work has largelyfocused on downstream modules and fusion strategies rather than revis-iting the backbone itself. To help close this gap in cross-domain perfor-mance and improve the DCT backbone design, we propose DiffNet, arelatively simple yet effective RGB–DCT early-fusion architecture drivenby two key design choices. First, to ensure that the decoder aggregatesmulti-scale inconsistency evidence rather than operating on raw, content-heavy activations, we apply a lightweight multi-level discrepancy trans-formation at the output of each backbone stage, replacing features withmagnitude-only responses to learned zero-sum filters. Second, we designan efficient DCT-domain backbone that relies on a lightweight frequency-index-aware DCT–quantization joint embedding. Our approach achievesstate-of-the-art performance on cross-domain and human-made docu-ment tampering localization, outperforming prior methods by around30%, with up to 7× higher throughput than the previous best model.

Modeling human-object interactions (HOI) from an egocen-tric perspective is a critical yet challenging task, particularly when re-lying on sparse signals from wearable devices like smart glasses andwatches. We present ECHO, the first unified framework to jointly recoverhuman pose, object motion, and contact dynamics solely from head andwrist tracking. To tackle the underconstrained nature of this problem,we introduce a novel tri-variate diffusion process with independent noiseschedules that models the mutual dependencies between the human, ob-ject, and interaction modalities. This formulation allows ECHO to oper-ate with flexible input configurations, making it robust to intermittenttracking and capable of leveraging partial observations. Crucially, it en-ables training on a combination of large-scale human motion datasetsand smaller HOI collections, learning strong priors while capturing in-teraction nuances. Furthermore, we employ a smooth inpainting inferencemechanism that enables the generation of temporally consistent interac-tions for arbitrarily long sequences. Extensive evaluations demonstratethat ECHO achieves state-of-the-art performance, significantly outper-forming existing methods lacking such flexibility.

Volumetric video seeks to model dynamic scenes as tem-porally coherent 4D representations. While recent Gaussian-based ap-proaches achieve impressive rendering fidelity, they primarily emphasizeappearance but are largely agnostic to instance-level structure, limit-ing stable tracking and semantic reasoning in highly dynamic scenarios.In this paper, we present Director, a unified spatio-temporal Gaussianrepresentation that jointly models human performance, high-fidelity ren-dering, and instance-level semantics. Our key insight is that embeddinginstance-consistent semantics naturally complements 4D modeling, en-abling more accurate scene decomposition while supporting robust dy-namic scene understanding. To this end, we leverage temporally alignedinstance masks and sentence embeddings derived from Multimodal LargeLanguage Models to supervise the learnable semantic features of eachGaussian via two MLP decoders, enabling language-aligned 4D repre-sentations and enforcing identity consistency over time.To enhance temporal stability, we bridge 2D optical flow with 4D Gaus-sians and finetune their motions, yielding reliable initialization and re-ducing drift. For the training, we further introduce a geometry-awareSDF constraints, along with regularization terms that enforces surfacecontinuity, enhancing temporal coherence in dynamic foreground model-ing. Experiments demonstrate that Director achieves temporally coher-ent 4D reconstructions while simultaneously enabling instance segmen-tation and open-vocabulary querying.