ECCV 2026 Papers

Recent advancements in 3D Gaussian Splatting (3DGS) haveachieved significant progress in novel view synthesis (NVS), offering com-putationally efficient and photorealistic rendering, even in the few-shotsetting where input training views are sparse. However, several repre-sentative 3DGS-based methods for few-shot NVS face a critical issue ofsparsity discrepancy, i.e., Gaussian initialization leverages dense fusedstereo points from extended views, creating an inconsistency with sparsetraining views. To resolve this issue, we propose ReInGS, a novel methodfeaturing a two-stage coarse-to-fine 3D Gaussian re-initialization pro-cess designed explicitly for few-shot NVS. In the coarse-level initializa-tion stage, an expansion-based hybrid point sampling (EHPS) strategyis deployed to generate dense points and effectively capture the scene’sglobal geometric structure. In the fine-level re-initialization stage, a view-dominant details-aware point sampling (VDPS) strategy, which com-prises two sequential sub-strategies on intra-view point sampling andcross-view point sampling, is adopted to reconstruct the scene’s localgeometric details with high fidelity. Comprehensive experiments on twobenchmarking datasets demonstrate that ReInGS achieves state-of-the-art performance across 2-view, 3-view, and 4-view settings, especiallydelivering PSNR gains of up to 2.92 dB, 1.92 dB, and 1.38 dB, respec-tively, while synthesizing high-quality novel views without data leakagefrom extended views.

We study sinusoidal recurrence as an iterative mechanism forharmonic spectral enrichment in implicit neural representations (INRs).Our analysis reveals that sinusoidal activations induce a harmonic linespectrum, providing a spectral account of how recurrent unrolling en-riches the effective spectral support. We realize this principle with ashared sinusoidal block that iteratively refines the latent representation.We empirically validate the resulting spectral behavior against feed-forward INRs, non-sinusoidal recurrent variants, and equilibrium-stylesinusoidal models. Complementing this analysis, we evaluate the pro-posed architecture across image and 3D representation tasks. On RGBimage benchmarks, our method achieves higher fidelity than feed-forwardbaselines with fewer parameters and fewer optimization steps, and it fur-ther transfers favorably to super-resolution, NeRF, and SDF tasks.

Online vectorized map construction provides autonomous ve-hicles with essential, real-time semantic and geometric scene understand-ing. While leveraging prior maps presents an opportunity to improveaccuracy, they are often biased due to outdated information or tem-porary road changes. Existing methods, which typically treat priors asfully reliable or only consider limited noise patterns, suffer severe perfor-mance degradation when these priors are imperfect. To address this, wepropose PriorMaskMap, a robust framework for prior-guided vectorizedmap construction. Its first innovation is a Prior-Aware Confidence Esti-mator (PACE) that evaluates the input rasterized map against currentvisual observations. It assigns confidence scores to regions classified asreliable, erroneous, or blank, creating a foundational mask that guidestwo subsequent branches. In one branch, the Dual-Prior Query Genera-tor (DPQG) selectively filters and embeds both rasterized and vectorizedpriors to initialize informative queries from trustworthy map structures.In the other, the Confidence-Guided Prior Fusion (CGPF) module spa-tially propagates and refines the mask, using the refined version to gov-ern the fusion of BEV features with the prior map, thereby providingrich contextual representations for the map decoder. Evaluation on thenuScenes dataset shows that PriorMaskMap achieves superior mappingprecision over existing approaches and exhibits marked robustness whenprovided with priors containing apparent errors. Our code is released athttps://github.com/healenrens/PriorMaskMap.

Object detection in remote sensing images (RSIs) is chal-lenged by the coexistence of geometric and spatial complexity: targetsmay appear with diverse aspect ratios, while spanning a wide range ofobject sizes under varied contexts. Existing RSI backbones address thetwo challenges separately, either by adopting anisotropic strip kernelsto model slender targets or by using isotropic large kernels to capturebroader context. However, such isolated treatments lead to complemen-tary drawbacks: the strip-only design can disrupt spatial coherence forregular-shaped objects and weaken tiny details, whereas isotropic largekernels often introduce severe background noise and geometric mismatchfor slender structures. In this paper, we extend PKINet, and presenta powerful and efficient backbone that jointly handles both challengeswithin a unified paradigm named Poly Kernel Inception Network v2(PKINet-v2). PKINet-v2 synergizes anisotropic axial-strip convolutionswith isotropic square kernels and builds a multi-scope receptive field, pre-serving fine-grained local textures while progressively aggregating long-range context across scales. To enable efficient deployment, we furtherintroduce a Heterogeneous Kernel Re-parameterization (HKR) Strategythat fuses all heterogeneous branches into a single depth-wise convolutionfor inference, eliminating fragmented kernel launches without accuracyloss. Extensive experiments on four widely-used benchmarks, includingDOTA-v1.0, DOTA-v1.5, HRSC2016, and DIOR-R, demonstrate thatPKINet-v2 achieves state-of-the-art accuracy while delivering a 3.9×FPS acceleration compared to PKINet-v1, surpassing previous remotesensing backbones in both effectiveness and efficiency.

In animation production, paint-bucket colourisation for hand-drawn animation is a labour-intensive procedure that assigns each en-closed region in line sketches a colour from reference design sheets. Re-cent automatic paint-bucket colourisation pipelines mirror this workflowvia region correspondence, but correspondences can be brittle when re-gions are ambiguous fragments without proper context. In this paper,we propose Palette Context Assisted (PeCA), a new training-free, plug-and-play framework for animation video colourisation that aims to closethis gap at test-time via reasoning over spatial and temporal contexts.Extensive experiments on existing benchmarks and a newly introducedlong-video test case show consistent performance boosts.

Robust 3D object detection under adverse weather remains acritical hurdle for autonomous driving. Despite progress with LiDAR–4Dradar fusion, most methods are constrained by a closed-world assump-tion, implicitly requiring training and test weather to align in both typeand severity. This premise fails in practice: the open-ended nature ofweather, and even variations within a single type like rain, cause dramat-ically different LiDAR degradation patterns, leading to significant per-formance drops in unseen conditions. To address this, we present Dual-Critic Guided Diffusion Alignment (DCDA), a weather-agnostic frame-work that learns to recover degraded LiDAR features toward a cleanmanifold. Rather than modeling specific weather types, DCDA employsa 4D radar-conditioned diffusion process to progressively refine features,guided by two complementary critics. (i) A detection-guided critic, an-chored by a pre-trained clean-weather model, ensures that the refinedfeatures retain object-level discriminability and localization accuracy. (ii)A weather adversarial critic enforces holistic distributional consistencywith clean-weather representations. By aligning features through seman-tic and distributional constraints rather than explicit weather model-ing, DCDA generalizes effectively to unseen weather types and severitieswithout requiring paired data or weather labels. We further introduce astructured open-weather benchmark with held-out type–severity combi-nations and extensive experiments verify DCDA’s advantages. Code isavailable at https://github.com/Mangonn/DCDA.

Building one-shot 3D animatable head avatars is an impor-tant yet challenging problem. Existing methods generally collapse underlarge camera pose variations, compromising the realism of 3D avatars.In this work, we propose a new framework to tackle the novel settingof one-shot 3D full-head animatable avatar reconstruction in a singleforward pass via inpainted UV-space Gaussian modeling, enabling 360◦rendering views and real-time animation. To facilitate efficient animationcontrol, we model 3D head avatars with Gaussian primitives embeddedon the surface of a parametric face model within the UV space, andproject the input image features to the UV space, resulting in incom-plete local UV feature maps. To inpaint the missing regions, we obtainknowledge of full-head geometry and textures from rich 3D full-head pri-ors within a pretrained 3D generative adversarial network (GAN) forglobal full-head feature extraction and multi-view supervision. Specifi-cally, to enhance the fidelity of 3D reconstruction during inpainting, wetake advantage of the symmetric nature of the UV space and humanfaces to fuse incomplete yet detailed local UV feature maps with theextracted global full-head textures, resulting in inpainted UV Gaussianattribute maps for avatar modeling. Extensive experiments demonstratethat our method is the first to achieve high-quality 3D full-head animat-able avatar modeling, significantly improving side and back views whileoutperforming state-of-the-art animation approaches, thereby improvingthe realism of 3D animatable avatars. The project page is available athttps://shaelynz.github.io/fhavatar/.

Robotic Vision-Language-Action (VLA) models generalizewell for open-ended manipulation, but their perception is fragile undersensing-stage degradations such as extreme low light, motion blur, andblack clipping. We present E-VLA, an event-augmented VLA frameworkthat improves manipulation robustness when conventional frame-basedvision becomes unreliable. Instead of reconstructing images from events,E-VLA directly leverages motion and structural cues in event streamsto preserve semantic perception and perception-action consistency un-der adverse conditions. We build an open-source teleoperation platformwith a DAVIS346 event camera and collect a real-world synchronizedRGB-event-action manipulation dataset across diverse tasks and illumi-nations. We also propose lightweight, pretrained-compatible event in-tegration strategies and study event windowing for stable deployment.Experiments show that even a simple parameter-free fusion, i.e., over-laying accumulated event maps onto RGB images, could substantiallyimprove robustness in dark and heavy-blur scenes: on Pick-Place at 20lux, success increases from 0% (image-only) to 60% with overlay fusionand to 90% with our event adapter; under severe motion blur (1000 ms-exposure proxy), Pick-Place improves from 0% to 20-25%, and Sortingfrom 5% to 32.5%. Overall, E-VLA provides systematic evidence thatevent-driven perception can be effectively integrated into VLA mod-els, pointing toward robust embodied intelligence beyond conventionalframe-based imaging. Code and dataset will be available at E-VLA.

Training-free guidance (TFG) steers a pretrained diffusionmodel toward a desired attribute at inference. To be effective, this guid-ance must be applied from the earliest, high-noise steps of sampling.Because its objective (a classifier or energy) is defined on clean images,ϵ- and v-prediction models must first estimate the clean image x̂ fromthe noisy state at each step, and the accuracy of that estimate deter-mines how easily guidance drifts off the data manifold. x-prediction,a recent alternative, outputs the clean image directly, removing thissource of error even at high noise. This is our motivation. We providea theoretical analysis of how each prediction target shapes this accu-racy, and introduce guided-class FID (Child FID), a metric that exposesthe manifold damage standard evaluation misses. Experiments on anew fine-grained bird benchmark and on style transfer confirm that x-prediction keeps guided samples on the manifold most reliably, making itthe strongest foundation for training-free guidance. Code is available athttps://github.com/ManLuML/on-manifold-tfg.

Current image-to-video models struggle to maintain appear-ance consistency such as the back of a garment. Lacking complete obser-vations, they hallucinate missing views, leading to visual inconsistencies,limiting deployment in applications like e-commerce, where strict visualfidelity is required. In this paper, we propose the Multiview EnhancedImage-to-Video Generation Model (MVI2V), which introduces additionalmulti-view images of a person or garment as reference inputs. Concretely,MVI2V adds a structurally identical forward stream for reference images,upgrading single- or dual-stream baselines into dual- or triple-stream ar-chitectures, and performs cross-stream fusion via self-attention to enablebidirectional information flow among token types. To better exploit thereferences, we adopt an inpainting sub-task that randomly masks theperson region in the conditioning image, forcing the model to rely moreon the reference views. We further design a data curation pipeline thatselects videos with large viewpoint changes and extracts diverse multi-view reference frames. Extensive experiments on single-stream Wan2.1and our in-house dual-stream model demonstrate that MVI2V effectivelyleverages multi-view references while preserving the base models’ single-image to video capability, validating our method’s generalizability todifferent architectures.

Neural video codecs have surpassed classical codecs in codingefficiency but remain impractical for deployment due to cross-platformincompatibility and high computational cost. Existing quantization-basedsolutions fail to produce deterministic results across diverse hardwareplatforms, leading to catastrophic decoding failures. We introduce MLVC,a hardware-robust neural video codec designed for practical cross-platforminference. The key idea is to explicitly transmit scale parameters throughthe hyperprior, which guarantees entropy coding consistency across de-vices without requiring bit-exact arithmetic. While this increases bitrateoverhead, we recover most of the coding efficiency through architecturalimprovements (gated memory, ReGLU activation), a long-term refer-ence recovery mechanism, and domain-specific perceptual training. Onthe VCD video conferencing benchmark, MLVC achieves >70% BD-rate (MOS) improvement over hardware HEVC, the strongest deployablebaseline, while reaching subjective quality competitive with DCVC-RT,which cannot operate across diverse platforms. Both the encoder anddecoder run at 100 FPS on average on commodity NPUs from Apple,Intel, and Qualcomm. MLVC is the first neural video codec to com-bine competitive compression performance, real-time speed, and cross-platform robustness across diverse consumer devices, making it suitablefor widespread deployment. Code will be released.

We present MagnetGS-Mesh, an integrated framework thatdirectly recovers high-quality, object-wise surface meshes from 3D Gaus-sian Splatting (3DGS) while preserving photorealistic novel-view syn-thesis. Prior 3DGS-to-mesh methods generate meshes by considering allGaussians without distinguishing objects from the background, whichinevitably yields excessive and unnecessary vertices in irrelevant regions.To address this efficiency bottleneck, we adopt an object-wise reconstruc-tion approach. However, decomposing 3DGS representations into indi-vidual objects introduces distinct geometric challenges: numerous out-lier Gaussians appear far from true surfaces, and the remaining surfaceGaussians often exhibit highly non-uniform density. Since robust meshextraction fundamentally requires a uniform distribution of Gaussiansto prevent surface holes and artifacts, these irregularities severely de-grade geometric fidelity. To resolve these issues, we propose a geometry-aligned two-stage training procedure. In Stage 1, we employ EnhancedOccupancy Learning to produce crisp boundaries, ensuring accurate 3Dsegmentation. In Stage 2, to address the uneven distribution, we in-troduce the Adaptive Local Normal Consistency (ALNC) Loss.This mechanism acts as a “magnet,” strategically relocating outlier Gaus-sians to sparse surface regions to ensure uniform coverage. By adap-tively aligning these relocated Gaussians with local surface normals,ALNC effectively fills geometric gaps while maintaining structural co-herence. Experimentally, MagnetGS-Mesh achieves competitive or supe-rior mesh quality compared to state-of-the-art methods while simultane-ously reducing model size by substantial margins. The code is availableat https://github.com/MinsuPark0752/MagnetGS-Mesh.

Multi-arm collaboration is becoming a core capability in embodied manipulation. Recent vision-language-action (VLA) models integrate perception, language, and control, but most represent language as a single global instruction and do not provide an explicit mechanism for assigning and composing arm-specific behaviors. This design limits transfer to collaboration patterns that differ from those observed during training. We present MA-VLA, a unified framework for multi-arm collaboration via atomic action assignment. MA-VLA decomposes cooperative behavior into mid-level atomic prompts and allocates them to individual arms, enabling explicit subgoal specification and compositional reuse across tasks. To reduce reliance on fixed execution roles, we introduce Arm Shuffle, a training-time permutation of the observation, state, and assigned atomic prompts for each arm. This permutation enforces role-agnostic instruction following and supports recomposition into unseen coordination patterns, which we term multi-arm compositional generalization. We also construct a benchmark in which test-time collaboration patterns are absent in training set. Across simulation and real-world evaluations, prior state-of-the-art VLAs largely fail under these unseen collaborations, while MA-VLA consistently succeeds. These results indicate that structured, per-arm atomic action assignment offers a practical route to scalable generalization in multi-arm embodied systems. Code, models, and data are available at https://github.com/zhangzaibin/future-robots

005 We present Lunima-OmniLV (abbreviated as OmniLV), 005006 a universal multimodal multi-task framework for low-level vision that ad- 006007 dresses over 100 sub-tasks across four major categories, including image 007008 restoration, image enhancement, weak-semantic dense prediction, and 008009 stylization. OmniLV leverages both textual and visual prompts to offer 009010 flexible, user-friendly interactions. Built on Diffusion Transformer (DiT)- 010011 based generative priors, our framework supports arbitrary resolutions 011012 — achieving optimal performance at 1K resolution — while preserving 012013 fine-grained details and high fidelity. Through extensive experiments, 013014 we demonstrate that separately encoding text and visual instructions, 014015 combined with co-training using shallow feature control, is essential to 015016 mitigate task ambiguity and enhance multi-task generalization. Our find- 016017 ings also reveal that integrating high-level generative tasks into low-level 017018 vision models can compromise detail-sensitive restoration. These insights 018019 pave the way for more robust and generalizable low-level vision systems. 019

Real-world deployment of text-to-image diffusion models re-quires continual concept removal as new privacy, copyright, or safetyobligations arise over time. Existing unlearning methods, however, aredesigned for single-step deletion and collapse after only 3–5 sequentialapplications. We trace this instability to two compounding factors: (i)coarse mapping targets that cause degradation to accumulate unnec-essarily across steps, and (ii) the absence of local protection for se-mantically neighboring concepts, whose shared internal representationsmake them the first to suffer collateral damage. Because this damageis strongest in the local semantic neighborhood of the forget concept,global replay alone cannot prevent it. Building on this analysis, we pro-pose Locality-Aware Continual Unlearning (LACU), a frameworkwith two complementary mechanisms. Locality-Aware Target Selec-tion chooses, for each forget prompt, the context-preserving mappingprompt that the diffusion model itself treats as most similar to the orig-inal prompt, measured by score-prediction distance (how differently themodel denoises the same noisy image under two text conditions), ensur-ing each update is as small and targeted as possible. Locality-AwareReplay uses the same metric to identify the retain concepts closest to theforget concept in the model’s own representation and replays them as alocal functional regularizer, directly shielding the most vulnerable neigh-borhood. Combined with teacher-student distillation and lightweight ℓ2parameter regularization, LACU maintains stable unlearning over 10 se-quential steps, preserving significantly higher related retention (RRacc )and general retention (GRacc ) than recent baselines. The code is availableat https://github.com/SonyResearch/LACU.

Spatial understanding is a fundamental cornerstone of human-level intelligence. Nonetheless, current research predominantly focuses ondomain-specific data production, leaving a critical void: the absence of aprincipled, open-source engine capable of fully unleashing the potentialof high-quality spatial data. To bridge this gap, we elucidate the designprinciples of a robust data generation system and introduce OpenSpa-tial—an open-source data engine engineered for high quality, extensivescalability, broad task diversity, and optimized efficiency. OpenSpatialadopts 3D bounding boxes as the fundamental primitive to constructa comprehensive data hierarchy across five foundational tasks: SpatialMeasurement (SM), Spatial Relationship (SR), Camera Perception (CP),Multi-view Consistency (MC), and Scene-Aware Reasoning (SAR). Lever-aging this scalable infrastructure, we curate OpenSpatial-3M, a large-scale dataset comprising 3 million high-fidelity samples. Extensive eval-uations demonstrate that versatile models trained on our dataset achievestate-of-the-art performance across a wide spectrum of spatial reasoningbenchmarks. Notably, the best-performing model exhibits a substantialaverage improvement of 19%, relatively. Furthermore, we provide a sys-tematic analysis of how data attributes influence spatial perception. Byopen-sourcing both the engine and the 3M-scale dataset, we provide arobust foundation to accelerate future research in spatial intelligence.Code: github.com/VINHYU/OpenSpatialDataset: huggingface.co/datasets/JoyAI-Image-OpenSpatial

Decomposing CAD models into sweepable sub-blocks is acritical preprocessing step for high-quality hexahedral mesh generation.Existing block decomposition methods fall into two main categories: tra-ditional geometry-driven approaches and reinforcement learning (RL)-based methods. Traditional methods rely heavily on manual expertiseand interactive operations, resulting in intensive manual labor and ex-tremely low automation efficiency. Existing RL-based methods are ei-ther only applicable to simple 2D models, or dependent on large-scaleoffline datasets and pre-training, leading to high training costs and diffi-cult industrial deployment. To address these challenges, we propose thefirst pure online RL framework for 3D CAD model block decomposition.Specifically, we formulate the task as a tree-structured Markov Deci-sion Process (MDP), and adopt two core designs: a decoupled frozengeometric-topological feature extraction architecture for lightweight RLnetwork design, and a continuous-to-discrete action projection mecha-nism to ensure geometric validity of decomposition operations. Withoutrelying on any pre-training or offline datasets, our agent learns optimalcutting strategies purely through online interaction with a single targetCAD model. Experiments on our self-built dataset and public datasetsshow that our method outperforms the state-of-the-art offline RL base-line, and achieves competitive decomposition quality comparable to thehybrid fine-tuning paradigm with no pre-training dependency.

LiDAR has increasingly been integrated into traffic cam-eras to expand coverage and mitigate occlusion in roadside cooperativeperception. However, how unimodal and camera–LiDAR fusion archi-tectures behave under variations in LiDAR point sparsity induced bysensor configurations and scene-dependent sensing conditions remainsunderexplored. We introduce RESOLVE, a large-scale real-world bench-mark dataset featuring multi-resolution roadside LiDAR and synchro-nized camera-LiDAR sensing for systematic evaluation of unimodal andfusion-based architectures in roadside 3D detection and tracking. RE-SOLVE contains over 100k images and 26k point cloud frames with 220kmanually annotated bounding boxes, captured at a real-world urban in-tersection across diverse lighting and weather conditions and spanning10 classes of traffic participants. In particular, RESOLVE enables con-trolled evaluation across three LiDAR resolution levels while keeping allother sensing and environmental factors fixed. This allows fair cross-architecture comparisons under point cloud distribution shifts resultingfrom resolution variations, sensing distance, and training–inference res-olution mismatches. Results from extensive benchmark experiments re-veal insights into how multimodal fusion can compensate for LiDARpoint sparsity, offering clues for designing cost-efficient roadside multi-modal perception. The dataset and benchmark codes are available athttps://github.com/ASU-Suo-Lab/RESOLVE.

How to accurately predict a high-fidelity future world? Whilethe visual world is inherently continuous, existing deterministic videoprediction models operate in discrete pixel space and are mainly opti-mized with pixel-wise mean squared error (MSE), which often leads toover-smoothed predictions and a lack of fine-grained visual details. Toaddress these limitations, we propose Predictive Differentiable Rendering(PDR), a novel end-to-end video prediction paradigm that bridges thegap between discrete and continuous representations. Inspired by recentprogress in 3D reconstruction with 3D Gaussian Splatting, we introducePredGS, a lightweight and plug-and-play adapter based on 2D Gaus-sian representation, which could be seamlessly integrated with existingpixel space predictors, significantly improving spatial detail preserva-tion with negligible computational overhead. Furthermore, we developpredgsplat, a CUDA-accelerated differentiable 2D Gaussian renderersupporting arbitrary channels. Each Gaussian is defined by 5 + C learn-able parameters (position, scale, rotation, and C channel amplitudes)and achieves up to 10× faster rendering than the baseline. Optimizedby a combined L1 and SSIM loss, PDR overcomes the inherent blurringtendencies of MSE Loss, significantly enhancing the prediction perfor-mance. Extensive experiments on diverse real-world benchmarks, includ-ing TaxiBJ, WeatherBench, KTH, and Human3.6M, demonstrate thatPDR consistently surpasses existing methods, delivering superior detailpreservation, visual fidelity, and predictive accuracy.