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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

Thinking Ahead: Foresight Intelligence in MLLMs and World Model

Zhantao Gong, Liaoyuan Fan, Qing Guo, Xun Xu, Xulei Yang, Shijie Li

In this work, we introduce FSU-QA, a VQA dataset for au-tonomous driving scenarios designed to advance research on ForesightIntelligence—the ability to anticipate and reason about complex, long-horizon futures. Unlike existing benchmarks that mainly focus on imme-diate perception or reactive planning, FSU-QA evaluates future-orienteddriving understanding through multi-agent-aware and rule-grounded coun-terfactual QA. Rather than holding surrounding agents fixed or target-ing geometric path generation, our benchmark requires models to infersemantic future outcomes from front-view historical observations andpast ego trajectories. A comprehensive evaluation on the accompanyingFSU-Bench reveals that state-of-the-art VLMs still face significant chal-lenges in anticipating future events. Furthermore, beyond model perfor-mance, we examine whether WM-generated predictions remain seman-tically consistent by using VLM-based proxy judges, and validate thisevaluation protocol through shuffled control experiments. Fine-tuningmodels on FSU-QA leads to substantial improvements in foresight under-standing, demonstrating the dataset’s effectiveness and offering a prin-cipled foundation for future research.

ECCV 2026

Think in Strokes, Not Pixels: Process-Driven Image Generation via Interleaved Reasoning

Lei Zhang, Junjiao Tian, Zhipeng Fan, Kunpeng Li, Jialiang Wang, Weifeng Chen, Markos Georgopoulos, Felix Juefei-Xu, Julian McAuley, Manling Li, Zecheng He

Humans paint images incrementally: they plan a global lay-out, sketch a coarse draft, inspect, and refine details, and most impor-tantly, each step is grounded in the evolving visual states. However, canunified multimodal models trained on text-image interleaved datasetsalso imagine the chain of intermediate states? In this paper, we introduceprocess-driven image generation, a multi-step paradigm that decom-poses synthesis into an interleaved reasoning trajectory of thoughtsand actions. Rather than generating images in a single step, our ap-proach unfolds across multiple iterations, each consisting of 4 stages: tex-tual planning, visual drafting, textual reflection, and visual refinement.The textual reasoning explicitly conditions how the visual state shouldevolve, while the generated visual intermediate in turn constrains andgrounds the next round of textual reasoning. A core challenge of process-driven generation stems from the ambiguity of intermediate states: howcan models evaluate each partially-complete image? We address thisthrough dense, step-wise supervision that maintains two complementaryconstraints: for the visual intermediate states, we enforce the spatial andsemantic consistency; for the textual intermediate states, we preserve theprior visual knowledge while enabling the model to identify and correctprompt-violating elements. This makes the generation process explicit,interpretable, and directly supervisable. To validate proposed method,we conduct experiments under various text-to-image benchmarks.

ECCV 2026

SUM: Unified Geometric Surgery on Spatio-Temporal Adaptation Vectors for Federated Class Incremental Learning

Jaeik Kim, Jaeyoung Do

Real-world intelligent systems often require both distributedcollaboration across data-isolated clients and continual adaptation toevolving tasks. This setting naturally gives rise to Federated Class In-cremental Learning (FCIL), which combines Federated Learning (FL)and Continual Learning (CL). However, their combination introducestwo coupled sources of interference: spatial interference from heteroge-neous clients and temporal interference from sequential tasks, jointlyleading to Spatial_x0015_Temporal Catastrophic Forgetting (ST-CF). Exist-ing approaches typically address spatial and temporal interference withseparate mechanisms, often incurring additional client-side computationor communication, while leaving directional interactions among updatesduring aggregation unregulated. In this paper, we reinterpret FCIL as auni_x001C_ed multi-task learning problem, where both client and task updatesare represented as adaptation vectors in a shared parameter space. Basedon this view, we proposeSurgery & Merge Sum ( ), a purely server-sideframework that performs geometric surgery on adaptation vectors duringaggregation. Spatial Sum mitigates client-level interference within eachSumround, while causal online temporal removes cross-task interferenceover time without additional client-side computation, communication, ormemory beyond standard federated training. Empirically, Sum achievesup to 22% improvement over prior FCIL methods across diverse visionand language benchmarks while remaining robust to unreliable clientsand maintaining computational e_x001E_ciency.

ECCV 2026

TerraDiT-Ω: Unified Spatial Control for Satellite Image Synthesis with Any Geospatial Primitive

Brian Wei, Srikumar Sastry, Daniel Cher, Eric Xing, Nathan Jacobs

Generative models have achieved remarkable progress, yetapplying them to satellite imagery remains challenging. Unlike natu-ral imagery, satellite scenes are structured by spatially complex andsemantically distinct geometries. Prior work addresses this complexityby adapting natural image frameworks using dense rasters or sparseprompts, trading off annotation cost and fidelity while breaking com-patibility with vector primitives commonly used to represent geographicinformation. We introduce TerraDiT-Ω, a unified spatial control frame-work that generates satellite imagery directly from any native geospatialprimitive. By jointly leveraging precise annotations (polygons, polylines)and coarser ones (bounding boxes, points), the model supports control-lable layouts across varying annotation budgets, broadening applicabilityto design tasks such as urban planning while remaining naturally com-patible with end-to-end GeoAI workflows. To effectively leverage theseprimitives during generation, we propose Geometry-Aware Local Atten-tion, a conditioning mechanism that injects explicit geometric cues intothe attention space. Across all conditioning formats, our approach con-sistently outperforms both dense-control and sparse-control baselines.Furthermore, this flexibility enables controllable synthetic data augmen-tation using a single generative model, improving downstream perfor-mance on land-cover segmentation, object detection, road graph extrac-tion, and scene classification. Code, data, and weights are available at

ECCV 2026

SuperVoxelGPT: Adaptive and Ordered 3D Tokenization for Autoregressive Shape Generation

Yuan Li, Congyi Zhang, Xifeng Gao, Xiaohu Guo

Autoregressive multimodal large language models (MLLMs)enable 3D generation but struggle to scale to high-resolution shapesdue to inadequate 3D tokenizations. Compact set-based representationsdiscard deterministic spatial ordering, leading to ambiguous sequenceprediction, while uniform or octree-based voxel grids preserve orderingat the cost of severe redundancy and excessively long sequences. Thisstructural trade-off limits stable and efficient autoregressive 3D gener-ation. We present SuperVoxelGPT, a representation-first frameworkthat resolves this tension through adaptive and deterministically orderedsupervoxel tokenization. Given a prompt, we first predict a coarse geo-metric saliency distribution and construct a shape-adaptive supervoxelpartition using saliency-guided centroidal Voronoi tessellation, allocatingfine-grained cells to complex regions and larger cells to smooth regions.Conditioned on this prompt and ordered supervoxel layout, we introducea SuperVoxelVAE and fine-tune a pretrained MLLM to autoregressivelygenerate supervoxel tokens. Experiments using Trellis-500K data showthat SuperVoxelGPT reduces token sequence length to 12.8% of uniformvoxel tokenization while achieving state-of-the-art generation quality andan average 10× speedup over prior methods.

ECCV 2026

Sticking Information in Plain Sight: Encoding and Detecting Hidden Stickers in the Real World

Christina Shatford, Szymon Rusinkiewicz

While there are many techniques (e.g., QR codes) that con-vey information via visual patterns, some applications would benefit fromusing imperceptible patterns. We present a method for designing subtlecode-conveying patterns that can be printed on transparent sticker paperand applied to real-world surfaces. An image of a scene with an encodedsticker can be captured and the sticker localized and decoded. Thus,our stickers, while mostly unobtrusive to the human eye, are detectableand decodable to our model. We jointly optimize the encoder, localizer,and decoder end-to-end, balancing imperceptibility and accuracy. Ourmethod encodes 100-bit secrets and experimental results show that itis robust to human placement errors, imaging conditions, multiple stick-ers, occlusions, and wrinkles in stickers. Importantly, this method is easyto deploy and applicable in situations such as object tracking for aug-mented reality, conveyance of information about tourist sites, and subtleidentification of manufactured goods or their components.

ECCV 2026

Stand Up and Move: Benchmarking Interactive Spatial Intelligence in WalkerBench

Zhiqi Ge, Gang Yang, Ziyang Pan, Jingzhe Zhu, Yuancheng Gu, Juncheng Li, Qizhou Wang, Rui Tang, Siliang Tang, Jun Xiao, Yueting Zhuang

The ultimate goal of spatial intelligence is to enable embod-ied agents to actively interact with the physical world. However, exist-ing benchmarks adopt a “Spectator View”—relying on single images orpassive video—neglecting the necessity of resolving geometric ambigu-ity through active viewpoint transformation. To address this, we presentWalkerBench, a global-scale interactive benchmark built on real-worldstreet-view and map data spanning 161 cities, with two categories ofhierarchically structured spatial tasks. Our empirical evaluation revealsa critical “representation misalignment”: VLMs’ one-dimensional linearcontext structures are fundamentally incompatible with the topologicalnature of 3D environments, causing inevitable spatial forgetting duringnavigation. To overcome this, we propose Spatial-IDE, which breaks fromlinear dialogue history via two mechanisms: (1) State Externalization,transforming implicit observations into an Explicit Topological Memory(ETM); and (2) Cognitive Decoupling, disentangling visual understand-ing into Goal-Directed Perception and Spatial Reasoning, letting theVLM focus on pure high-level decision-making. Spatial-IDE achieves sub-stantial improvements on WalkerBench and successfully generalizes zero-shot to real-world urban navigation on the Unitree humanoid robot. Theproject is available on https://github.com/lalayang123456- ctrl/WalkerBench.

ECCV 2026

NutriBench-Kitchen: Benchmarking Embodied AI for Nutrition Management

YuLin Wei, Xiangchen Wang, Jianhui Pan, Jinyu Xiao, Zheng Tan, Ruozai Tian, Guanhua Chen, Feng Zheng

An embodied kitchen assistant must do more than recog-nize food in isolated frames. It must track ingredient states over timeand integrate visual observations with recipe and nutritional knowledgeto support constraint-aware decision-making. We formalize this capa-bility as Embodied Nutrition Management: perceiving nutrition-relevantevents, maintaining a persistent food state, and using it for knowledge-grounded planning. Existing benchmarks evaluate static food under-standing or embodied cooking actions, but do not measure whether anagent can continuously update and use nutrition-relevant states in dy-namic kitchens. To fill this gap, we introduce NutriBench-Kitchen,a benchmark containing 1,500 manually verified question–answer pairsfrom 160 cooking videos. It covers five task families: Ingredient Entry,Memory Management, Recipe Query, Long-Term Planning, and Short-Term Planning, spanning food-state construction, maintenance, knowl-edge retrieval, and decision-making across different planning horizons.Evaluations of proprietary and open-source large vision-language modelsreveal a substantial gap from human performance, particularly in quan-titative ingredient estimation, long-term state tracking, and reasoningunder interacting constraints. We further introduce Nutri-Vgent, a di-agnostic long-video agent with separate episodic, food-state, and recipememories. Its consistent improvements demonstrate the value of explicitstate representations and structured memory for nutrition management.Together, NutriBench-Kitchen and Nutri-Vgent provide a testbed forstudying persistent state tracking and knowledge-grounded reasoning indynamic kitchens. Code is available at https://github.com/V1ol1n/NutriBench-Kitchen.

ECCV 2026

Rethinking Attention Reallocation for Multimodal Emotion Recognition

Yazhe Lyu, Yixiong Zou, Jinghan Hu, Yuhua Li, Ruixuan Li

The emergence of Multimodal Large Language Models hasenabled a new generative paradigm for multimodal emotion recognition(MER), where emotional interpretations are produced through multi-modal token-level reasoning. In view of multimodal inputs, existing stud-ies commonly observe that reallocating attention from generated tokensto modality tokens during inference consistently improves performance.However, in this work, we reveal a counterintuitive phenomenon: such astrategy only benefits two-modality settings, but degrades performancein three- or more-modality scenarios, where the opposite reallocation di-rection instead leads to improvements. To understand this phenomenon,through comprehensive analysis, we show that with more modalities, thecomplex interactions between modalities make the model hard to paymore attention to tokens with more information, especially in deeperlayers, leading to improper attention allocation and the observed contra-dictory behavior. Based on these insights, we propose a training-freeattention rectification method that leverages structured shallow-layerattention as a prior to regularize entangled final-layer attention dur-ing inference, without introducing additional parameters or modifyingthe backbone model. Extensive experiments on nine datasets in MER-UniBench demonstrate that our method achieves state-of-the-art per-formance, consistently outperforming both reallocation directions acrossdiverse multimodal scenarios. Our code is available at https://github.com/yzl77/ReAR.

ECCV 2026

Social-Mamba: Socially-Aware Trajectory Forecasting with State-Space Models

Po-Chien Luan, Wuyang Li, Yang Gao, Alexandre Alahi

Human trajectory forecasting is crucial for safe navigationin crowded environments, requiring models that balance accuracy withcomputational efficiency. Efficiently modeling social interactions is key toperformance in dense crowds. Yet, most recent methods rely on attentionmechanisms, which are effective at capturing complex dependencies, butincur quadratic computational costs that scale poorly with the grow-ing number of neighbors. Recently, Selective State-Space Models haveprovided a linear-time alternative; however, their inherently sequentialdesign is misaligned with the unstructured and dynamic nature of socialinteractions. To address this challenge, we propose Social-Mamba, a fore-casting architecture that reformulates social interactions as structuredsequential processes. At its core is the Cycle Mamba block, a novel mod-ule that enables continuous bidirectional information flow. Social-Mambaorganizes agents on an egocentric grid and introduces social triplet fac-torization, which decomposes interactions into temporal, egocentric, andgoal-centric scans. These are dynamically integrated through a learnablesocial gate and global scan to generate accurate and efficient trajectorypredictions. Extensive experiments on five trajectory forecasting bench-marks show that Social-Mamba achieves state-of-the-art accuracy whileoffering superior parameter efficiency and computational scalability. Fur-thermore, embedding Social-Mamba into a flow-matching framework fur-ther enhances both accuracy and efficiency, establishing it as a flexibleand robust foundation for future trajectory forecasting research. Thecode is publicly available: https://github.com/vita-epfl/Social-Mamba.

ECCV 2026

SICAGE: Speaker-Independent Culture-Aware Gesture Generation using TED4C-L Dataset

Ariel Gjaci, Antonio Sgorbissa, Vittorio Murino

Recent co-speech gesture generation methods often overlookcultural differences, limiting their effectiveness in human–agent inter-action. Moreover, culture-conditioned models are rarely evaluated un-der speaker-disjoint splits, so apparent “cultural” behavior may be con-founded with speaker-specific gesturing style. We introduce SICAGE, amodular framework for culture-aware co-speech gesture generation thatconditions motion synthesis models on speaker-independent cultural rep-resentations. SICAGE learns these representations from audio and textby treating each speaker as a separate domain while imposing invari-ance across speakers. This encourages representations to remain culture-discriminative while reducing dependence on speaker identity. The re-sulting cultural embeddings condition a multimodal generator to pro-duce culturally appropriate gestures. We instantiate this idea with twodomain generalization approaches: adversarial learning and Fishr reg-ularization. We further introduce ALaDiT, a real-time diffusion-basedgesture generator designed to efficiently incorporate the learned culturalembeddings. To validate our method, we built TED4C-L, a 106-hourmultimodal dataset of 764 TED speakers from four cultural groups. Ex-periments show that SICAGE improves motion realism, diversity, beatsynchronization, semantic relevance, and cultural consistency.

ECCV 2026

Sequential Visual Place Recognition: Exploiting Trajectory Priors for Robust Localization

Dominik Kloepfer, Patrick Wenzel

Modern Visual Place Recognition (VPR) methods excel onstandard benchmarks yet remain brittle in feature-poor environments.By treating each query image in isolation, they discard the sequentialcontext in any real trajectory. We formalize a task that exploits thiscontext: given a query sequence, localize the final image against an un-ordered reference database—which, unlike sequence-to-sequence meth-ods, requires no sequential structure in the database. We propose TRAIL(TRajectory-Aware Image Localization), a principled framework basedon Conditional Random Fields (CRF) that combines learned functionsfor visual similarity and for camera-motion consistency, refining a dis-tribution over candidate references as each query arrives. A lightweightpost-processing layer atop any pre-trained VPR backbone, TRAIL im-proves a state-of-the-art baseline by up to 8.3 percentage points on ourprimary benchmark, transfers to unseen datasets without retraining, anddelivers its largest gains where visual cues are scarce.

ECCV 2026

Self-Evolving Agentic Image Restoration via Deliberate Planning and Intuitive Execution

Shuang Cui, Fan Ji, Guanglong Sun, Yufei Guo, Xiongxin Tang, Jiangmeng Li, Fanjiang Xu

Real-world image restoration (IR) remains challenging dueto complex and coupled degradations. While recent agentic IR frame-works leverage Large Language Models for flexible tool planning, theyface two critical limitations. First, from a search scheme perspective,excessive reliance on greedy strategies fails to balance exploration andexploitation. Second, existing agentic systems underutilize information,exhibiting episodic amnesia. To address these challenges, we proposeSelf-Evolving Agentic Image Restoration (SEAR), which formu-lates restoration as a sequential decision-making problem. Inspired by thedual-process theory, SEAR comprises an Intuitive Executor and a Delib-erate Planner, respectively following the fast-thinking System 1 and slow-thinking System 2 principles. The Deliberate Planner employs Pruning-Aware Monte Carlo Tree Search for long-horizon reasoning, utilizing ahybrid no-reference reward and a Multimodal Large Language Model(MLLM)-based tournament to prevent metric exploitation. Complemen-tarily, the Intuitive Executor leverages a self-evolving episodic memoryindexed by degradation-aware state fingerprints. This mechanism dis-tills expensive search trajectories into adaptive expertise, overcomingepisodic amnesia while progressively amortizing cold-start explorationcosts through memory reuse. Extensive experiments on synthetic andreal-world benchmarks demonstrate its strong perceptual and quantita-tive performance.

ECCV 2026

ReInGS: Re-Initializing 3D Gaussians against Sparsity Discrepancy in Few-Shot Novel View Synthesis

Junao Shen, Tian Feng, Haojie Dong, Jinkang Ji, Tianjia Shao

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.

ECCV 2026

Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation

Hyunmin Cho, Jaejun Yoo, Kyong Hwan Jin

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.

ECCV 2026

PriorMaskMap: Robust Online Vectorized Map Construction with Biased Priors

Haoming Xu, Wei Li, Yu Hu

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.

ECCV 2026

PKINet-v2: Towards Powerful and Efficient Poly-Kernel Remote Sensing Object Detection

Xinhao Cai, Liulei Li, Gensheng Pei, Zeren Sun, Yazhou Yao, Wenguan Wang

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.

ECCV 2026

PeCA: Palette Context Assisted Inference for Test-Time Paint-Bucket Colourisation on Animation Videos

Dongheng Lin, Jianbo Jiao

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.

ECCV 2026

Open-Weather Robust 3D Detection via Dual-Critic Diffusion Alignment

Shuyao Li, Chuanxing Geng, Heyang Sun, Qiang Zhou, Jingjing Gu

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.

ECCV 2026

One-Shot Feed-Forward 360° Animatable Avatar via Inpainted UV-Space Gaussian Modeling

Shuling Zhao, Dan Xu

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/.

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