ECCV 2026 论文列表

Depth completion aims to predict a dense depth map froma color image with sparse depth measurements. Although deep learningmethods have achieved state-of-the-art (SOTA), effectively handling thesparse and irregular nature of input depth data in deep networks remainsa significant challenge, often limiting performance, especially under highsparsity. To overcome this limitation, we introduce the Gaussian BeliefPropagation Network (GBPN), a novel hybrid framework synergisticallyintegrating deep learning with probabilistic graphical models for end-to-end depth completion. Specifically, a scene-specific Markov Random Field(MRF) is dynamically constructed by the Graphical Model ConstructionNetwork (GMCN), and then inferred via Gaussian Belief Propagation(GBP) to yield the dense depth distribution. Crucially, the GMCN learnsto construct not only the data-dependent potentials of MRF but also itsstructure by predicting adaptive non-local edges, enabling the capture ofcomplex, long-range spatial dependencies. Furthermore, we enhance GBPwith a serial & parallel message passing scheme, designed for effectiveinformation propagation, particularly from sparse measurements. Exten-sive experiments demonstrate that GBPN achieves SOTA performanceon the NYUv2 and KITTI benchmarks. Evaluations across varying spar-sity levels, sparsity patterns, and datasets highlight GBPN’s superiorperformance, notable robustness, and generalizable capability.

We present FLEG, a feed-forward network that reconstructslanguage-embedded 3D Gaussians from multi-view images. Previous feed-forward language-embedded Gaussian reconstruction methods are re-stricted to a fixed number of input views and typically attach a language-aligned semantic embedding to each Gaussian, resulting in impracticalinput settings and semantic redundancy. In contrast, we introduce ageometric-semantic dual-branch distillation framework that supports avariable number of uncalibrated and unposed multi-view images as input.We also propose a novel-view-based distillation strategy during trainingthat mitigates overfitting to input views. In addition, we observe that se-mantic representations are significantly sparser than geometric ones, andper-Gaussian language embedding is unnecessary. To exploit this spar-sity, we design a decoupled language embedding strategy that representslanguage information with a sparse set of semantic Gaussians, ratherthan attaching embeddings to every Gaussian. Compared with densepixel-aligned per-Gaussian embedding schemes, our method uses only5% of the language embeddings while maintaining comparable semanticfidelity, effectively reducing storage costs. Extensive experiments demon-strate that FLEG outperforms state-of-the-art feed-forward reconstruc-tion and language-embedded Gaussian methods in both reconstructionquality and language-aligned semantic representation.

Class-incremental learning (CIL) aims to continually acquirenew classes while preserving previously learned knowledge, posing thewell-known stability–plasticity dilemma. With the emergence of largepre-trained models, parameter-efficient fine-tuning methods such as LoRAhave become a practical paradigm for rehearsal-free CIL by restrictingadaptation to low-rank weight updates. Recent LoRA-based approachesoften mitigate cross-task interference by enforcing orthogonality betweentask updates. However, under strict rank budgets and long task se-quences, uniformly protecting previously used directions can becomeoverly conservative, progressively shrinking the effective update spaceand limiting plasticity. To address this limitation, we propose Varia-tional Direction-aware LoRA (VD-LoRA), a Bayesian framework thatenables adaptive consolidation within the low-rank update space. VD-LoRA maintains a recursive variational posterior over LoRA directionsto estimate direction-wise uncertainty, using posterior precision to adap-tively control protection strength. Based on this uncertainty signal, wefurther design a direction selection mechanism that favors updates thatare both task-relevant and safe to adapt. This enables selective reuseof low-risk directions while preserving critical ones, maintaining a task-aligned update subspace under fixed rank constraints. Extensive experi-ments on multiple rehearsal-free CIL benchmarks demonstrate that VD-LoRA consistently outperforms existing LoRA-based methods while im-proving long-horizon stability and plasticity. Our code is available at

With the emergence of Vision-Language Models (VLMs),cloud-based VLM inference services have raised critical privacy concernsregarding the potential leakage of sensitive visual prompts. However, ex-isting defenses compromise either efficiency or model utility to achieveprivacy. Recently, the covariant obfuscation paradigm has shown greatpotential for private large language model inference, but it is incom-patible with cross-modal architectures and tasks. In this paper, we pro-pose COVERT, the first practical privacy-preserving VLM inferenceframework, which explicitly adapts covariant obfuscation for multimodalarchitectures. Specifically, it integrates exact architectural reparameter-ization to robustly obfuscate visual features against naive inversion at-tacks, alongside utility-aware parameter tuning tailored to neutralizehidden-state inversion attacks, thereby achieving a favorable privacy-utility-efficiency trade-off. Extensive evaluations show that COVERT ef-fectively shields sensitive visual attributes against hidden-state inversionattacks, while keeping the average accuracy degradation below 3% acrossmajor benchmarks and sustaining high inference throughput with a neg-ligible overhead of less than 6%.

4D Gaussian Splatting (4DGS) can render dynamic scenesphotorealistically. However, with limited viewpoint coverage, some spa-tiotemporal regions remain sparsely observed, leading to artifacts, partic-ularly in scenes with large motion. Existing approaches leveraging gener-ative models rely on heuristic virtual-viewpoint selection before refiningrendered views. As a result, they cannot actively explore such sparselyobserved regions. To address this issue, we propose a pipeline that ac-tively selects spatiotemporal virtual viewpoints to improve 4DGS recon-struction. Our method selects virtual viewpoints for generative enhance-ment based on the rendering sensitivity and motion-aware observationdensity of 4D Gaussians, prioritizing views that alleviate observationsparsity. In the refined images, we filter out regions that conflict withcaptured observations or are likely to contain generative artifacts andthen fine-tune 4DGS using only the reliable regions. We evaluate ourmethod on multi-view video benchmarks using new train/test splits de-signed to induce observation gaps. Results show consistent improvementsover prior viewpoint selection strategies and fine-tuning methods in bothqualitative and quantitative evaluations, while reducing artifacts. 1

Existing Referring Video Object Segmentation tasks focus on referring expressions describing events, actions or appearances of relevant objects within the observed frames, lacking evaluation in scenarios that require pre-decisive spatio-temporal reasoning, thereby limiting their applicability. To address this, we propose Foresight Expression Video Object Segmentation, a task that queries future events in upcoming video segments and requires masks of the objects in the observed frames as visual answers. For example, in ego-centric scenes, the question “What tool will be used?” demands reasoning over spatio-temporal cues to predict the masks of the next tool to be used, which helps with the understanding of future actions and decisions. To support this task, we introduce FeVOS, a dataset with 968 video clips, 14,525 foresight expressions, and 2,904 chain-of-thought annotations to provide explicit and interpretable reasoning steps. We further develop FeVOS-R1, an MLLM-based model trained on our dataset via a two-stage pipeline of supervised fine-tuning and reinforcement learning. FeVOS-R1 not only achieves state-of-the-art performance on FeVOS, but also demonstrates strong generalization to existing RVOS benchmarks. We hope this work can inspire more research on predictive reasoning in video perception.

In recent years, face super-resolution (FSR) methods haveachieved remarkable progress, generally maintaining high image fidelityand identity (ID) consistency under standard settings. However, underextreme degradation scenarios (e.g., scale ¿ > 8×), critical facial at-tributes and ID information are often severely lost in the input image,making it difficult for conventional models to reconstruct realistic andID-consistent faces. Existing methods tend to generate hallucinatedfaces under such conditions, producing restored images that lack faith-ful ID constraints. To address this challenge, we propose a novel FSRmethod with Identity Decoupling and Fitting (IDFSR), designed to en-hance ID restoration under large scaling factors while mitigating hallu-cination effects. Our approach involves three key designs: (1) maskingthe facial region in the low-resolution (LR) image to eliminate unre-liable ID cues; (2) warping a reference image to align with the LRinput, providing style guidance; and (3) leveraging ID embeddingsextracted from ground-truth (GT) images for fine-grained ID modelingand personalized adaptation. We first pretrain a diffusion-based modelto explicitly decouple style and ID by forcing it to reconstruct maskedLR facial regions using both style and ID embeddings. Subsequently, wefreeze most network parameters and perform lightweight fine-tuning ofthe ID embeddings using a small set of target ID images. This embeddingencodes fine-grained facial attributes and precise ID information, signifi-cantly improving both ID consistency and perceptual quality. Extensivequantitative evaluations and visual comparisons demonstrate that theproposed IDFSR substantially outperforms existing approaches underextreme degradation, particularly in terms of ID consistency.

Sign Language Translation (SLT) is a core task in the field of AI-assisted disability. Traditional SLT methods are typically based on visible lightvideos, which are easily affected by factors such as lighting variations, rapidhand movements, and privacy concerns. This paper proposes the use of bio-inspired event cameras to alleviate the aforementioned issues. Specifically, weintroduce a new high-definition event-based sign language dataset, termed Event-CSL, which effectively addresses the data scarcity in this research area. Thedataset comprises 14,827 videos, 6,097 glosses, and 2,544 Chinese words in thetext vocabulary. These samples are collected across diverse indoor and outdoorscenes, covering multiple viewpoints, lighting conditions, and camera motions.We have also benchmarked existing mainstream SLT methods on this dataset tofacilitate fair comparisons in future research. Furthermore, we propose a novelevent-based, gloss-free sign language translation framework, termed EvSLT. Theframework first segments continuous video features into clips and employs aMamba-based memory aggregation module to compress and aggregate spatialdetail features at the clip level. Subsequently, these spatial features, along withtemporal representations obtained from temporal convolution, are then fused bya graph-guided spatiotemporal fusion module. Extensive experiments on Event-CSL, as well as other publicly available datasets, demonstrate the superior per-formance of our method. The dataset and source code are publicly available athttps://github.com/Event-AHU/OpenESL/tree/main/EvSLT.

We introduce EmbedCopilot-Bench, a multimodal bench-mark designed to evaluate large vision-language models (LVLMs) as as-sistants for embedded development. Compared with pure software pro-gramming, embedded programming inherently involves hardware, mak-ing it well-suited to benefit from LVLMs’ visual capabilities. However, tothe best of our knowledge, no existing benchmark comprehensively eval-uates how effectively LVLMs can serve as embedded copilots. Our bench-mark is developed to fill this gap. Built on real-world embedded develop-ment videos, we constructed 216 annotated multimodal QA triplets span-ning heterogeneous hardware platforms and peripheral modules, cov-ering hardware operation, software configuration, and code generationtasks. To assess model performance, we pair a rubric-driven LLM-as-a-Judge protocol with execution-centric evaluation in Wokwi platformand hardware-in-the-loop Execution Success Rate (ESR), jointly cap-turing semantic quality and functional correctness. Experiments on arange of closed- and open-source LVLMs show that visual context sig-nificantly boosts performance, especially for hardware-related tasks, butthat models remain far from reliable embedded copilots. ESR analysesand a pin-localization case study reveal frequent near-miss failures causedby brittle grounding and limited structured reasoning over board layouts.EmbedCopilot-Bench provides a challenging, realistic testbed for futuremethods that more tightly integrate perception, circuit understanding,and executable code synthesis for real-world embedded development.

Generalization is a central challenge in autonomous driv-ing, as real-world deployment requires robust performance under unseenscenarios, sensor domains, and environmental conditions. Recent world-model-based planning methods have shown strong capabilities in sceneunderstanding and multi-modal future prediction, yet their generaliza-tion across datasets and sensor configurations remains limited. In addi-tion, their loosely coupled planning paradigm often leads to poor video-trajectory consistency during visual imagination. To overcome these limi-tations, we propose DriveVA, a novel autonomous driving world modelthat jointly decodes future visual forecasts and action sequences in ashared latent generative process. DriveVA inherits rich priors on mo-tion dynamics and physical plausibility from well-pretrained large-scalevideo generation models to capture continuous spatiotemporal evolutionand causal interaction patterns. To this end, DriveVA employs a DiT-based decoder to jointly predict future action sequences (trajectories)and videos, enabling tighter alignment between planning and scene evolu-tion. We also introduce a video continuation strategy to strengthen long-duration rollout consistency. DriveVA achieves an impressive PDM-based planning performance of 90.9 PDM score on the NAVSIM bench-mark. Extensive experiments also demonstrate the zero-shot capabilityand cross-domain generalization of DriveVA, which reduces average L2error and collision rate by 78.9% and 83.3% on nuScenes and 52.5%and 52.4% on the Bench2Drive built on CARLA v2 compared with thestate-of-the-art world-model-based planner.

Camera-controlled video diffusion models (VDMs) have re-cently emerged as powerful world models, enabling users to explore 3Dscenes through flexible, user-defined camera trajectories. Nevertheless,current VDMs typically rely on implicit spatiotemporal representationswithout explicit 3D geometric grounding. Such geometry-agnostic mod-eling often leads to issues including geometrically implausible struc-tures and cross-view spatial inconsistencies. To alleviate this, we presentDreamWorld, a new recipe of world model that novelly bridges the strongspatial structure priors of 3D foundation models with the high-fidelitygenerative capabilities of video diffusion models for geometry-consistent3D scene generation. Specifically, given the input image and camera tra-jectory, DreamWorld first learns a geometry video diffusion model topredict compact geometry features for the target novel views, function-ing as explicit structure pivots to reflect the underlying 3D spatial layout.To achieve this, we introduce a distillation paradigm that transfers high-level structural knowledge from a pretrained 3D foundation model to thediffusion model, thereby enabling it to produce geometrically consistentand spatially coherent features. Conditioned on such geometry features,another appearance video diffusion model is then utilized to synthesizethe final video, ensuring improved geometric plausibility and cross-viewconsistency while maintaining high visual fidelity. Extensive experimentsdemonstrate that DreamWorld outperforms existing methods in visualquality, 3D consistency, and camera controllability. Our project page isavailable at https://yanghb22-fdu.github.io/DreamWorld.

Reconstructing physics-based 3D assets—geometry, mate-rials, and illumination—from multi-view images is a core problem incomputer graphics and vision, and a prerequisite for realistic relightingand editing. Physics-based inverse rendering offers an accurate image-formation model, but is severely underconstrained: without strong pri-ors, illumination is baked into materials, and reconstructions generalizepoorly to novel views and lighting. Data-driven diffusion models, in con-trast, predict visually plausible materials, yet their predictions rarelysatisfy the rendering equation and are not directly usable for physics-based rendering. We bridge these two paradigms rather than replacingeither. Our key idea is to treat the predictions of a state-of-the-art dif-fusion model not as target material values but as a similarity kernel foroptimization: we introduce a regularization loss that penalizes deviationsin the optimized material over surface regions where the diffusion predic-tions are near-constant, while leaving the optimization free to match theinput images. Built on this regularizer, our end-to-end pipeline jointlyreconstructs geometry, materials, and illumination, yielding high-qualityassets that drop into standard rendering pipelines and relight faithfully.On the Synthetic4Relight, Stanford-ORB, and DTC-Synthetic datasets,our method significantly outperforms state-of-the-art baselines in bothreconstruction accuracy and relighting quality.

Large vision-language models with CLIP as a core compo-nent have achieved remarkable progress across a wide range of tasks,yet they remain highly vulnerable to adversarial attacks. Existing ad-versarial fine-tuning methods typically optimize CLIP under a singlefixed perturbation strength, resulting in weak robustness generalizationand semantic instability. To address this limitation, we propose Path-Consistent Fine-Tuning (PACO), a new framework for unsupervised ad-versarial fine-tuning. Rather than optimizing solely on adversarial exam-ples, PACO adopts a two-stage procedure that regularizes representa-tions on continuous local paths along adversarial directions. Specifically,it first anchors adversarial embeddings to a clean reference to preventsevere semantic drift. Building upon this, it constructs a local path be-tween the clean sample and the adversarial anchor, regularizing inter-mediate representations to encourage a linear transition, which yields amore stable visual embedding. Extensive experiments show that PACOachieves a superior robustness-accuracy trade-off: it not only mitigatesthe clean-performance degradation common to prior methods, but alsodelivers superior robustness across diverse attack settings and perturba-tion strengths. Our code is available at https://github.com/Trusted-LLM/PACO.

Visual reasoning in immersive environments is critical forVR, AR, and robotics, yet is fundamentally challenged by partial ob-servability. Existing Vision-Language Models (VLMs) are ill-suited forthis task, as their rigid “look first, think later” paradigm decouples per-ception from reasoning. They either process distorted, low signal-to-noisepanoramas or reason over a fixed set of pre-determined views, preventingdynamic, task-driven exploration. We reframe immersive visual reason-ing as an iterative process of probabilistic belief updating. We introduceDiffusion-Based Immersive Visual Reasoning (DIVR), a novel frameworkthat models reasoning as a denoising diffusion process, progressively re-fining a latent reasoning hypothesis from a state of high uncertainty toa denoised low-uncertainty state. DIVR comprises two tightly coupledmodules: a Latent Hypothesis Refiner (LHR) that uses observed visualevidence to denoise the current hypothesis, and a Reasoning-Guided Ob-server (RGO) that uses the denoised hypothesis to actively select the nextmost informative viewport. This creates a closed-loop system where theevolving reasoning hypothesis continuously guides perception, distinctfrom the rigid, open-loop paradigm of prior work. Extensive experimentson multiple 360° VQA benchmarks demonstrate that DIVR establishesa new state-of-the-art, significantly outperforming strong baselines, par-ticularly on complex questions that require dynamic, multi-step evidencegathering.

Most digital videos are stored in 8-bit low dynamic range(LDR) formats, where much of the original high dynamic range (HDR)scene radiance is lost due to saturation and quantization. This loss ofhighlight and shadow detail precludes mapping accurate luminance toHDR displays and limits meaningful re-exposure in post-production work-flows. Although techniques have been proposed to convert LDR imagesto HDR through dynamic range expansion, they struggle to restore re-alistic detail in the over- and underexposed regions. To address this, wepresent DiffHDR, a framework that formulates LDR-to-HDR conversionas a generative radiance inpainting task within the latent space of a videodiffusion model. By operating in Log-Gamma color space, DiffHDR lever-ages spatio-temporal generative priors from a pretrained video diffusionmodel to synthesize plausible HDR radiance in over- and underexposedregions while recovering the continuous scene radiance of the quantizedpixels. Our framework further enables controllable LDR-to-HDR videoconversion guided by text prompts or reference images. To address thescarcity of paired HDR video data, we develop a pipeline that synthesizeshigh-quality HDR video training data from static HDRI maps. Extensiveexperiments demonstrate that DiffHDR significantly outperforms state-of-the-art approaches in radiance fidelity and temporal stability, produc-ing realistic HDR videos with considerable latitude for re-exposure.

Despite the exceptional reasoning capabilities of MultimodalLarge Language Models (MLLMs), their adaptation into universal em-bedding models is significantly impeded by task conflict. To addressthis, we propose TSEmbed, a universal multimodal embedding frameworkthat synergizes Mixture-of-Experts (MoE) with Low-Rank Adaptation(LoRA) to explicitly disentangle conflicting task objectives. Moreover,we introduce Expert-Aware Negative Sampling (EANS), a novel strategythat leverages expert routing distributions as an intrinsic proxy for se-mantic similarity. By dynamically prioritizing informative hard negativesthat share expert activation patterns with the query, EANS effectivelysharpens the model’s discriminative power and refines embedding bound-aries. To ensure training stability, we further devise a two-stage learningparadigm that solidifies expert specialization before optimizing repre-sentations via EANS. TSEmbed achieves state-of-the-art performance onboth the Massive Multimodal Embedding Benchmark (MMEB) and real-world industrial production datasets, laying a foundation for task-levelscaling in universal multimodal embeddings.

Federated parameter-efficient fine-tuning (PEFT) enables scalable adaptation of large pre-trained backbones under communication constraints. In heterogeneous federated settings, personalization is commonly realized through gated residual mixing between shared and clientspecific branches. However, this parameterization is inherently scale nonidentifiable: only the product of residual magnitude and mixing weight determines the functional contribution. When private residuals are optimized locally, their scales may drift across clients and communication rounds, rendering mixing coefficients an unreliable measure of personalization strength. We address this limitation with FedSDG, a structuredecoupled gating framework for federated PEFT. FedSDG maintains separate shared and private LoRA branches and introduces projection-level scalar gates for depth-aware personalization. Crucially, we propose Dynamic Alignment, a backbone-anchored calibration mechanism that stabilizes private residual scale prior to gated mixing, thereby improving gate identifiability without additional communication. Extensive experiments demonstrate consistent personalization improvements and stable depth-wise adaptation across diverse non-IID regimes.

Multi-person pose estimation (MPPE), which involves detecting body joint positions (keypoints) for every person in an image, is a fundamental task in computer vision. Despite recent advances, no transformer-based model currently achieves real-time performance. This work addresses the latency challenge by introducing DETRPose, the first family of real-time, end-to-end transformer models for multi-person 2D pose estimation. DETRPose significantly enhances the GroupPose decoder, enabling real-time inference. For training, a novel denoising keypoint technique is proposed to accelerate convergence. The varifocal loss is also extended for keypoints, termed Keypoint Similarity VariFocal loss, to improve query quality. Extensive evaluation demonstrates that DETRPose models achieve accuracy comparable to or exceeding that of leading alternatives while requiring five to ten times fewer training epochs. DETRPose-S matches the accuracy of YOLOv8-Pose-X and YOLO11- Pose-X on the COCO dataset (67.0 vs 67.3 and 67.2 in AP) with 81% fewer parameters (11.5M vs 69.4M and 58.8M) and 52% faster inference speed (2.39ms vs 5.23ms and 4.93ms). On the CrowdPose dataset, DETRPose-X has 13.1× fewer FLOPs (232.3G vs 3048.1G) and only 2% fewer precision (75.1 vs 76.6 in AP) than ED-Pose-SwinL-5S. On the OCHuman dataset, DETRPose-S surpasses all previous models, showing the robustness of DETRPose on out-of-distribution datasets. Code is available at https://github.com/SebastianJanampa/DETRPose

Recent radiance field methods represent scenes with 2D prim-itives that offer surface alignment and efficient rasterization, from Gaus-sian disks to triangles, yet all rely on convex boundaries: curved and con-cave structures demand excessive primitives. We introduce DeformableTriangle Splatting, which augments each triangle with K control pointsper edge, each parameterized by a single learnable scalar displacementthat shifts the boundary inward or outward, enabling non-convex shaperepresentation while preserving the three base vertices that define the 3Dplane. To render these non-convex primitives differentiably, we designa rasterization pipeline in the triangle’s barycentric coordinate space,ensuring view-consistent rendering. A winding number test determineswhether each pixel lies inside the deformed primitive, and a windowfunction controlled by two learnable parameters, sharpness and cornersmoothness, together with a per-primitive scalar opacity, produces thesmooth opacity transition from interior to boundary. Validation is donein a variety of real-world scenes, outperforming recent works based onnon-volumetric primitives in terms of visual quality and versatility whilestill achieving competitive rendering efficiency.Project page: orioljim1.github.io/detris