ECCV 2026 Papers

Camera traps are crucial for large-scale biodiversity monitor-ing, yet accurate automated analysis remains challenging due to diversedeployment environments. While the computer vision community haspredominantly framed this challenge as cross-domain (e.g., cross-site)generalization, this perspective overlooks a primary challenge faced byecological practitioners: maintaining reliable recognition at the fixed siteover time, where the dynamic nature of ecosystems introduces profoundtemporal shifts in both background and animal distributions. To bridgethis gap, we present the first unified study of camera-trap speciesrecognition over time. We introduce a realistic, large-scale bench-mark comprising 546 camera traps with a streaming protocol that evalu-ates models over chronologically ordered intervals. Our end-user-centricstudy yields four key findings. (1) Biological foundation models (e.g.,BioCLIP 2) underperform at numerous sites even in initial intervals,underscoring the necessity of site-specific adaptation. (2) Adaptation ischallenging under realistic evaluation: when models are updated usingpast data and evaluated on future intervals (mirrors real deployment life-cycles), naive adaptation can even degrade below zero-shot performance.(3) We identify two main drivers of this di!culty: severe class imbalanceand pronounced temporal shift in both species distribution and back-grounds between consecutive intervals. (4) We find that e"ective integra-tion of model-update and post-processing techniques can largely improveaccuracy, though a gap from the upper bounds remains. Finally, we high-light critical open questions, such as predicting when zero-shot modelswill succeed at a new site and determining whether/when model updatesare necessary. Together, our benchmark and analysis provide actionabledeployment guidelines for ecological practitioners while establishing newdirections for future research in vision and machine learning.

Localizing a font into new languages is a highly intricate task requiring precisedesign adaptation of glyphs, color/texture, and spacing/kerning, from source to target lan-guages. Most existing methods focus on single glyph generation with limited capability inhandling multilingual font rendering. In this work, we propose LoGAN, a VLM-based agenticframework for few-shot multilingual font localization, which takes in a small number of indi-vidual glyphs from a font or letters from a logo and uses them to generate complete charactersets in other languages. LoGAN breaks down this task into multiple components: a glyph-leveldiffusion model, a style finetuning module, a spacing and kerning transfer algorithm, and atexture expansion model, with a VLM agent coordinator. LoGAN achieves broad languagecoverage for font localization with various styles, including Chinese/Korean/Japanese (CJK).We evaluate our approach on both font and real-world logo datasets spanning more than27 languages and compare it against both specialized font generation and state-of-the-artimage editing models with strong text rendering capabilities (e.g., FLUX, Nano-Banana).Our approach yields higher glyph fidelity while maintaining better style, texture, and kerningconsistency according to both quantitative and qualitative evaluations.

Multimodal object tracking has received extensive attentiondue to its strong robustness and complementary collaboration. However,most existing methods only evaluate the cross-modal fusion at specificmulti-modal layers, and overlook the interaction among different fea-ture layers from single modal, which limits the utilization and propa-gation of cross-modal complementary information and consequently de-grades tracking performance. To alleviate this issue, we propose a pro-gressive multimodal tracker based on State Space Model (SSM) namedPSMTrack, which consists of three key modules. First, Difference-guidedBidirectional State-space Mamba Enhancement (DBME) is developedto leverage difference-aware cues and bidirectional SSM modeling toprogressively enhance modality representations for subsequent tracking,while suppressing background and similar distractions. Second, we adoptan expert routing mechanism to adaptive select a subset of layers andperform cross-layer interaction to alleviate the information loss causedby relying solely on the last layer. Finally, we design a Hybrid SpiralMamba Fusion (HSMF) module that performs dynamic spatial modelingwith spiral Mamba from both local and global scales for final cross-modalfusion, thereby capturing long-range dependencies with near-linear com-plexity and improving bounding-box regression stability. Extensive ex-periments on multiple mainstream RGB-X benchmarks validate that ourmodel consistently improves both accuracy and robustness.

Accurate dialogue description in audiovisual video captioning is crucial for downstream understanding and generation tasks. However, existing models generally struggle to produce captions that faithfully reflect spoken content and speaker dynamics. To mitigate this limitation, we propose DiaDem, a powerful audiovisual video captioning model capable of generating captions with more precise dialogue descriptions while maintaining strong overall performance. We first design a dedicated pipeline to synthesize high-quality dialogue-aware audiovisual captions for supervised fine-tuning (SFT), and then introduce a difficulty-partitioned two-stage reinforcement learning (RL) strategy to further enhance dialogue descriptions. To enable systematic evaluation of dialogue description capabilities, we present DiaDemBench, a comprehensive benchmark designed to evaluate models across diverse dialogue scenarios, focusing on both speaker attribution accuracy and utterance transcription fidelity in audiovisual captions. Extensive experiments on DiaDemBench reveal even commercial models still exhibit substantial room for improvement in dialogue-aware captioning. Notably, DiaDem not only outperforms the Gemini series in dialogue description accuracy but also achieves competitive performance on general audiovisual captioning benchmarks, demonstrating its overall effectiveness. Our project is available at https://diadem-captioner.github.io/.

Automating filament tracing in Cryo-Electron Microscopy(Cryo-EM) is essential for 3D helical reconstruction but challenged byintersecting topologies and extremely low Signal-to-Noise Ratios (SNR =σs2 /σn2 < 0.1 or -10 dB). Existing paradigms fail: pixel-wise segmenterssuffer from severe topological fracturing, box-based detectors face ghostcenter drift, sequential trackers derail due to error accumulation, and tra-ditional active contours collapse under artificial closed-curve constraints.To resolve these bottlenecks, we present FPicker, the first topology-guided framework reconciling these incompatibilities. It unifies percep-tion via a center-endpoint representation and an open-curve evolutionmodule to explicitly model non-cyclic connectivity. On simulated bench-marks, FPicker outperforms top baselines by over 40% relative gain inmean spatio-angular precision (mSAP) and reduces topological gap ratesby over 60% under extreme noise (−20 dB). By learning intrinsic phys-ical geometry rather than local texture, FPicker demonstrates strongpotential as a resilient geometric backbone. Its zero-shot performance onthe real-world EMPIAR dataset exhibits robust topological resistance,achieving a state-of-the-art 82.9% mSAP upon fine-tuning. Our resultsalso suggest modeling physical priors is a highly robust path towardbridging the sim-to-real gap in signal-starved scientific imaging. The codeis publicly available at: https://github.com/tomzhaosky/FPicker.

Due to the scarcity of large-scale in-the-wild triplet data and the improper use of masks, the performance of video virtual try-on models remains limited. In this paper, we first introduce TripVVT-10K, the largest and most diverse in-the-wild triplet dataset to date, providing explicit video-level cross-garment supervision that existing video datasets lack. Built upon this resource, we develop TripVVT, a Diffusion Transformer–based framework that replaces fragile garment masks with a simple, stable human-mask prior, enabling reliable background preservation while remaining robust to real-world motion, occlusion, and cluttered scenes. To support comprehensive evaluation, we further establish TripVVT-Bench, a 100-case benchmark covering diverse garments, complex environments, and multi-person scenarios, with metrics spanning video quality, try-on fidelity, background consistency, and temporal coherence. Compared to state-of-the-art academic and commercial systems, TripVVT achieves superior video quality and garment fidelity while markedly improving generalization to challenging in-thewild videos. We publicly release the dataset and benchmark at https://huggingface.co/datasets/TripVVT/TripVVT-10K, which we believe provide a solid foundation for advancing controllable, realistic, and temporally stable video virtual try-on.

Given the remarkable ability of 2D foundation image modelsto generate high-fidelity outputs, we investigate a fundamental question:do 2D foundation image models inherently possess 3D world model ca-pabilities? To answer this, we systematically evaluate multiple state-of-the-art image generation models and Vision-Language Models (VLMs)on the task of 3D world synthesis. To harness and benchmark their po-tential implicit 3D capability, we propose an agentic framing to facilitate3D world generation. Our approach employs a multi-agent architecture:a VLM-based director that formulates prompts to guide image synthesis,a generator that synthesizes new image views, and a VLM-backed two-step verifier that evaluates and selectively curates generated frames fromboth 2D image and 3D reconstruction space. Crucially, we demonstratethat our agentic approach provides coherent and robust 3D reconstruc-tion, producing output scenes that can be explored by rendering novelviews. Through extensive experiments across various foundation mod-els, we demonstrate that 2D models do indeed encapsulate a grasp of3D worlds. By exploiting this understanding, our method successfullysynthesizes expansive, realistic, and 3D-consistent worlds.

Hierarchical structure is common in image data, where finegrained clusters often merge into larger, coarser semantic clusters. In biological cell images, current self-supervised learning models tend to suppress the inherent hierarchical structure, as coarse factors (such as the imaging modality) systematically obscure finer attributes (e.g., morphological details) in the latent space. We propose a novel hierarchy-aware self-supervised training framework to address this problem. Our method integrates two components: we propose (i) a distillation framework by introducing a segmentation teacher that improves the latent space’s morphological awareness, and (ii) a hierarchy-aware contrastive loss based on HDBSCAN to improve decision boundaries between closely related yet distinct subtypes at each hierarchical level. Both components counteract the self-supervised learning’s tendency to overemphasize coarse factors by aligning embeddings with semantic and morphological cues, yielding biologically meaningful sub-clusters driven by fine morphological detail. We train and evaluate our method on a curated corpus of 2.3M single cells aggregated from 20 microscopy datasets (labeled and unlabeled), covering 208 cell classes. Our method improves upon the counterparts and baseline, with the average top-K accuracy up by +2.8%, top-9 retrieval on the dataset with a deeper hierarchy up by +6.3%, and an F1-score on biologically relevant classification of administered drug from perturbed cell morphology downstream up by +7.8%.

We present FFAvatar, a Transformer-based 3D Gaussian framework for fast construction of high-quality and animatable 4D head avatars from one or more reference portrait images. Unlike existing feed-forward approaches that require a fixed number of input views, FFAvatar supports incremental reconstruction, progressively refining the avatar representation as additional reference images become available. At the core of our method is an alternating attention mechanism that disentangles identity appearance from expression and viewpoint variations, enabling the reconstruction of a canonical 3D appearance that remains consistent across poses and facial expressions. To balance visual fidelity and computational efficiency, we introduce a sparse-to-dense learning paradigm. Coarse appearance features are first learned using sparse primitives anchored to the FLAME vertex level and are subsequently densified in the UV domain to capture fine-grained geometric and texture details. We further propose a plug-and-play motion refinement module that enables subject-specific dynamic personalization by modeling residual motion beyond parametric deformation. Extensive experiments demonstrate that FFAvatar efficiently produces high-fidelity and controllable 4D head avatars, achieving superior flexibility, driving efficiency, and identityconsistent rendering across diverse expressions and viewpoints. Project Page: https://jj-yao.github.io/ffavatar/

Instruction-guided image editing has a training-time blindspot. Generative editors are never required to semantically verify whethertheir outputs actually satisfy the instruction. Supervision stops at recon-struction and input textual-level conditioning. This produces incompleteedits, spatial spillover, and poor localization. We present IABEdit, amodel-agnostic framework that embeds differentiable semantic verifica-tion into training. A frozen vision-language model extracts spatially-aware descriptors from the ground-truth edit. A trainable aligner thenreproduces them from the generated output. The residual between thetwo becomes a gradient that teaches the generator both what to editand where, with no inference-time VLM cost. IABEdit is compatiblewith diverse backbones, including U-Net (Stable Diffusion) and MMDiT(FLUX), without altering their inference pipelines. On MagicBrush, itimproves structural fidelity by +3.49 DINO-I over the best diffusion base-line and +1.26 over the best overall baseline, while remaining competitiveon instruction alignment. It also achieves state-of-the-art instruction ad-herence performance on RealEdit and EMU Edit benchmarks based onembedding-based metrics. Most consequentially, on the D-LORD surveil-lance benchmark, it surpasses the proprietary Gemini agent by +5.13DINO-P under heavy occlusion, where preserving identity is hardest.This shows that gradient-aligned VLM distillation holds up under real-world-like surveillance and occlusion conditions. Human and GPT-4oevaluations confirm perceptually precise, well-localized edits.

Cross-view geo-localization matches ground-level observationsagainst geo-tagged satellite imagery. Recent methods show that sequen-tial queries such as video clips yield richer spatiotemporal cues thansingle images, yet they overlook a complementary sequential modality:route descriptions—which capture the same trajectory at a higher levelof abstraction and are often the only input available (e.g., a user direct-ing an autonomous vehicle to a pickup point). To bridge this gap, weintroduce SeqGeo-VL, a dataset of ∼39K video–text–satellite triplets,and TrajLoc, a unified framework capable of processing both video clipsand route descriptions. By leveraging both dense visual and abstract lin-guistic semantics, TrajLoc enables these modalities to mutually reinforcecross-view matching. We further propose TrajMod, a lightweight mod-ule that conditions query embeddings on trajectory geometry, yieldingspatially-aware representations. Experiments show that TrajLoc achievessubstantial gains over state-of-the-art methods on both video and textgeo-localization. Code, model weights, and the dataset are released athttps://humblegamer.github.io/trajloc/.

Significant progress has been achieved in subject-driven text-to-image (T2I) generation, which aims to synthesize new images depictingtarget subjects according to user instructions. However, evaluating thesemodels remains a significant challenge. Existing benchmarks exhibit criti-cal limitations: 1) insufficient diversity and comprehensiveness in subjectimages, 2) inadequate granularity in assessing model performance acrossdifferent subject difficulty levels and prompt scenarios, and 3) a pro-found lack of actionable insights and diagnostic guidance for subsequentmodel refinement. To address these limitations, we propose DSH-Bench,a comprehensive benchmark that enables systematic multi-perspectiveanalysis of subject-driven T2I models through four principal innovations:1) a hierarchical taxonomy sampling mechanism ensuring comprehensivesubject representation across 58 fine-grained categories, 2) an innovativeclassification scheme categorizing both subject difficulty level and promptscenario for granular capability assessment, 3) a novel Subject IdentityConsistency Score (SICS) metric demonstrating a 9.4% higher correlationwith human evaluation compared to existing measures in quantifyingsubject preservation, and 4) a comprehensive set of diagnostic insightsderived from the benchmark, offering critical guidance for optimizing fu-ture model training paradigms and data construction strategies. Throughan extensive empirical evaluation of 19 leading models, DSH-Bench un-covers previously obscured limitations in current approaches, establishingconcrete directions for future research and development.

A striking feature of the human visual system is that itingests image information through a sequence of local glimpses, ratherthan a single global computation. This makes the human vision distinctlydifferent from most popular computer vision models in use today, whichingest images globally and in a single shot. A natural question thereforeis whether local, sequential vision models may provide any fundamentalcomputational benefits, in addition to being biologically more plausiblethan global models. In this work, we propose an answer to this questionfrom the perspective of state tracking and length generalization. Inspiredby recent studies of length generalization in language models, we studythe behavior of vision models trained on simple vision tasks that requirethe aggregation of local information across an image. Our experimentsreveal that, similar to language models, vision models can learn to ex-ploit global shortcuts and thereby fail to generalize over task length orcomplexity. We also show that recurrent vision policies based on strictlylocal perception can mitigate these failures, thereby allowing models togeneralize on these tasks. Our results show that local attention may be anessential overlooked requirement for robust compositional generalizationin vision tasks.

Although Multimodal Large Language Models (MLLMs) havemade impressive progress in understanding videos—such as recognizingobjects, actions, and events—they still struggle with spatial reasoning.In particular, they have difficulty forming a consistent 3D understandingof a scene from separate 2D video frames. Unlike humans, who can natu-rally infer depth, distance, object size, and their own movement throughspace, these models often lack the ability to accurately reconstruct a co-herent 3D environment. This limitation makes it challenging for them toestimate real-world measurements, such as how far an object is or howmuch the camera has moved, when relying only on 2D image sequences.To bridge these gaps, we propose VoxAnchor, a framework for explicitspatial-semantic grounding. Specifically, we reify the 2D video sequenceinto a spatiotemporally continuous representation by unprojecting visualtokens, geometric features, and view-consistent semantics into a unified3D voxel grid. To facilitate precise spatial reasoning, we propose a Voxel-Aware Attention mechanism, which constrains feature interactions withinphysically consistent volumetric units. This approach enables the modelto effectively aggregate fragmented observations and infer metric-awarespatial relationships from monocular video. VoxAnchor sets a new state-of-the-art on VSI (66.8%) and VSTI (66.3%) benchmarks, outperform-ing significantly more parameter-intensive models and confirming thatexplicit geometric grounding effectively bridges the 2D-to-3D gap. Codewill be available at https://github.com/Embrace-Arch/VoxAnchor.

Recovering dense 3D geometry from unposed images remainsa foundational challenge in computer vision. Current state-of-the-artmodels are predominantly trained on perspective datasets, which implic-itly constrains them to a standard pinhole camera geometry. As a result,these models suffer from significant geometric degradation when appliedto wide-angle imagery captured via non-rectilinear optics, such as fisheyeor panoramic sensors. To address this, we present CAM3R, a Camera-Agnostic, feed-forward Model for 3D Reconstruction capable of process-ing images from wide-angle camera models without prior calibration.Our framework consists of a two-view network which is bifurcated into aRay Module (RM) to estimate per-pixel ray directions and a Cross-viewModule (CVM) to infer radial distance with confidence maps, pointmaps,and relative poses. To unify these pairwise predictions into a consistent3D scene, we introduce a Ray-Aware Global Alignment framework forpose refinement and scale optimization while strictly preserving the pre-dicted local geometry. Extensive experiments on various camera modeldatasets, including panorama, fisheye and pinhole imagery, demonstratethat CAM3R establishes a new state-of-the-art in pose estimation andreconstruction.

Deep Neural Network (DNN) classifiers suffer from poor calibration when their softmax outputs (predictive confidence) deviate from the empirical likelihoods. This manifests itself as either overconfident incorrect predictions or under-confident correct predictions. Label smoothing (LS) enhances model calibration by introducing entropy regularization during training through redistributing probability mass from the ground-truth label to the remaining classes. LS, including Margin-based LS (MbLS), have restrictive assumptions: they rely on predefined, uniform smoothing rules and only tackle overconfidence. In reality, samples exhibit diverse characteristics, such as difficulty/ambiguity, that interact with the evolving nature of the model being trained. In training, samples may have various degrees of underor overconfidence. To overcome this, a mechanism that identifies the specific confidence state of each sample and determines the appropriate degree of smoothing in each training step is needed, tailoring the adjustment to the individual sample. We propose FeDLaS: Feature-Modulated Bidirectional Label Smoothing, a plug-and-play algorithm for label smoothing-based losses. In FeDLaS, we introduce a Feature Norm-based Confidence Indicator (NCI) to control smoothing and a Bidirectional Calibration Gating (BCG) module to detect both over and under-confidence. Our algorithm can be integrated with LS and MbLS based losses when applied to standard DNNs, enhancing performance. Extensive experiments on standard and fine-grained high-resolution vision benchmarks show that FeDLaS consistently improves calibration compared to modern baselines, reducing Expected Calibration Error (ECE) and Adaptive ECE while maintaining Top-1 accuracy. Code: github.com/nadarasarbahavan/FEDLAS

In the pursuit of robust and generalizable category-level ob-ject pose estimation, most existing methods adopt parametric formula-tions that learn effective representations from data, yet they primarilyencode category-level patterns into fixed shape priors or static param-eter weights, which limits their scalability to highly diverse instances.In this paper, we rethink category-level pose estimation from a memory-centric perspective and present MemPose, a memory-augmented frame-work that explicitly incorporates category-level geometric memory intothe pose estimation pipeline. We introduce an external memory bufferthat stores and dynamically updates structural representations frompreviously observed instances, enabling the model to leverage accumu-lated experience to support current perception. Extensive experimentson four challenging benchmarks (REAL275, CAMERA25, Housecat6Dand Wild6D) demonstrate the superiority of our proposed method overprevious state-of-the-art approaches.

Attention-based models often develop attention sinks, wherea small number of tokens repeatedly attract attention and accumulateunusually large activations. In vision transformers, these outliers areclosely related to registers, which have been diagnostically linked toglobal, low-frequency image structure. Existing work has largely studiedregisters through interpretability analyses and linear probes, leaving openwhether they can be operationalized as plug-and-play signals for genera-tion without retraining. We revisit this question in tokenized image gen-eration. Using OpenCLIP and DINOv2 on ImageNet, we find that test-time register features exhibit stronger low-frequency concentration thanboth [CLS] readouts and patch-mean features, and show a consistent (al-beit moderate) correlation with pixel-space DCT low-frequency energy.Motivated by these diagnostics, we introduce RegToken, a training-free procedure that converts register structure into a small set of globalprior tokens by (i) NFN-based layer localization, (ii) TokenRank-guidedsubspace extraction, and (iii) a projection-and-conservation update onthe register subspace. Inserted into a frozen compact 1D token gener-ation pipeline, RegToken improves ImageNet generation and alignmentmetrics (e.g., FID-5k 20.5 → 20.1, SigLIP 3.6 → 3.9) without modify-ing pretrained weights, and accelerates test-time optimization (Steps@τ74 → 52). Overall, our results suggest that structures often viewed asattention artifacts can be repurposed as lightweight global priors for to-kenized generation.

Advances in radiance fields have enabled photorealistic novelview synthesis. In several domains, large-scale real-world datasets havebeen developed to support comprehensive benchmarking and to facilitateprogress beyond scene-specific reconstruction. However, for distractor-free radiance fields, a large-scale dataset with clean and cluttered imagesper scene remains lacking, limiting the development. To address thisgap, we introduce DF3DV-1K, a large-scale real-world dataset compris-ing 1,048 scenes, each providing clean and cluttered image sets for bench-marking. In total, the dataset contains 89,924 images captured using con-sumer cameras to mimic casual capture, spanning 17 scenario types, 128distractor types, and 161 scene themes across indoor and outdoor envi-ronments. A curated subset of 41 scenes, DF3DV-41, is systematicallydesigned to evaluate the robustness of distractor-free radiance field meth-ods under challenging scenarios. Using DF3DV-1K, we benchmark ninerecent distractor-free radiance field methods and 3D Gaussian Splatting,identifying the most robust methods and the most challenging scenarios.Beyond benchmarking, we demonstrate an application of DF3DV-1K byfine-tuning a diffusion-based 2D enhancer to improve radiance field meth-ods, achieving average improvements of 0.96 dB PSNR and 0.057 LPIPSon the held-out set (e.g., DF3DV-41) and the On-the-go dataset. We hopeDF3DV-1K facilitates the development of distractor-free vision and pro-motes progress beyond scene-specific approaches. The dataset and leader-board are available at https://johnnylu305.github.io/df3dv1k_web/.