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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

MaterialFlow: Attribute-Disentangled Material Transfer via Trajectory-Aware Velocity Modulation

Sung-Lin Tsai, Bo-Kai Ruan, Yu-Hsuan Chen, Wen-Huang Cheng, Hong-Han Shuai

Material transfer seeks to re-render an object’s surface usinga reference exemplar while preserving its original geometry and identity.Current training-free methods often rely on attention-based injection,which entangles attributes like color, texture, and structural patternsinto a single representation. This entanglement leads to structural in-stability and imprecise results. Additionally, inversion-based editing inflow models is prone to reconstruction-induced trajectory drift, whereaccumulated errors degrade object details. We propose MaterialFlow, atraining-free framework for precise material transfer using pre-trainedflow models. Our approach introduces a trajectory-aware velocity modu-lation mechanism that rebalances the generative flow in an inversion-free manner. This ensures stable and semantically consistent editingdynamics without the need for explicit latent reconstruction. We fur-ther introduce an attribute-aware disentanglement paradigm that sepa-rates reference materials into color, texture, and meso-scale patterns forfine-grained control. Evaluations on the Material Transfer Benchmark(MTB) show that MaterialFlow surpasses state-of-the-art methods intransfer quality, inference efficiency, and controllability. Project page:https://github.com/Sung-Lin/MaterialFlow.

ECCV 2026

MArFE: Multi-Contrast MRI Arbitrary Scale Super-Resolution with Fourier Enhancement

ZHIWEN SHI

High-resolution multi-contrast MRI is essential for capturingrich anatomical structures and supports more accurate disease diagnosisthrough the reference images from another contrast. It often suffers fromlimited spatial resolution due to acquisition constraints, and requestsexploring arbitrary-scale reconstruction beyond fixed scale factors. How-ever, related research exists two following issues: 1) Implicit neural rep-resentations (INR) as mainstream methods are prone to spectral bias,which limits their ability to recover high-frequency details; 2) Multi-contrast MRI is often used as the effective prior, but lacks the targetednetwork design to further merge INR positional information. To solvethese problems, we propose a Fourier-enhanced implicit framework forarbitrary-scale multi-contrast MRI super-resolution (MArFE). First, theFourier reparameterization module (FRM) is introduced to character-ize frequency features through fixed Fourier basis decomposition. Then,Fourier neural operators can alleviate frequency degradation by the non-local Galerkin-type linear attention (NLGLA) based on kernel integralmechanisms. Additionally, we design a hybrid loss that jointly supervisesboth spatial and frequency domains to improve high-frequency texturereconstruction. Extensive experiments on two public multi-contrast MRIdatasets demonstrate that MArFE outperforms existing methods undervarious scale factors with superior visual performances and quantitativemetrics. These results highlight the clinical potential of MArFE in en-hancing MRI quality across multi-contrast sequences. The project codecan be found at https://github.com/zhiwen-shi/MArFE.

ECCV 2026

QSVideo: Query-Conditioned Semantic Temporal Retrieval for Video Understanding

Wei Ao, Lan Wang, Vishnu Boddeti

The performance of vision-language models (VLMs) in videounderstanding declines with increasing video duration, as video momentsunrelated to the query confuse their language components. Multimodalretrieval has emerged as a critical component of video understanding,addressing this challenge by localizing key visual evidence. However, ex-isting multimodal retrieval methods suffer from biased relevance estima-tion, limited diversity, and temporal collapse. In this paper, we proposeQSVideo, a unified framework that systematically addresses relevance,diversity, and temporal modeling in video retrieval. We first introducea query-conditioned semantic ranker, QSRanker, which reformulates ar-bitrary questions into retrieval-friendly queries and estimates structuredrelevance along object, action, and location dimensions. Building uponthis, we design QSRetrieval to jointly optimize relevance and diversityfor more informative frame selection. Moreover, we propose temporalalignment strategies tailored for both long and streaming videos to im-prove evidence recall. Extensive experiments on long and streaming videobenchmarks demonstrate that QSVideo greatly enhances video VLM per-formance under strict frame limit constraints. The code is available athttps://github.com/human-analysis/QSVideo.

ECCV 2026

LoT-Pass: Long-term-robust Image Watermarking for Image to Video Generation

Guanjie Wang, Zehua Ma, Han Fang, Weiming Zhang

While Image-to-Video models unlock new creative possibil-ities, they also raise risks of unauthorized adaptation, deepfakes, andscenario manipulation, making robust authentication and traceability so-lutions urgent for image owners. While image watermarking offers a solu-tion, existing methods expose a critical vulnerability: due to the inherentsemantic drift of generated videos, images watermarks survive only in ini-tial frames. This allows attackers to easily evade detection via temporalcropping—discarding early frames to obtain watermark-free derivativevideos. Consequently, in an era of pervasive I2V models, long-term ro-bustness is imperative for image watermarking. Since simply increasingforward embedding strength fails against severe generative divergence,we introduce LoT-Pass, a watermarking framework built on a new in-sight: only one-directional robustness enhancement is hard to achievelong-term robustness. We propose a two-way strategy: (1) expand therobustness distance by training under simulated temporal evolution inI2V generation, and (2) recover unextractable frames by reversing themthrough a temporal inversion module. This shift, from merely resistingdistortions to actively rewinding the video to a watermark-friendly state,enables LoT-Pass to maintain extractability even under temporal drift.Experiments on mainstream open-source and commercial I2V modelsdemonstrate that LoT-Pass achieves stronger long-term robustness whilepreserving imperceptibility, offering a new paradigm for image copyrightprotection in the era of widespread I2V adoption. Code

ECCV 2026

LESV:Language Embedded Sparse Voxel Fusion for Open-Vocabulary 3D Scene Understanding

Fusang WANG, Nathan Piasco, Moussab Bennehar, Luis G Roldao Jimenez*, Dzmitry Tsishkou, Fabien Moutarde

Recent advancements in open-vocabulary 3D scene under-standing heavily rely on 3D Gaussian Splatting (3DGS) to register vision-language features into 3D space. However, we identify two critical limi-tations in these approaches: the spatial ambiguity arising from unstruc-tured, overlapping Gaussians which necessitates probabilistic feature reg-istration, and the multi-level semantic ambiguity caused by pooling fea-tures over object-level masks, which dilutes fine-grained details. To ad-dress these challenges, we present a novel framework that leverages SparseVoxel Rasterization (SVRaster) as a structured, disjoint geometry repre-sentation. By regularizing SVRaster with monocular depth and normalpriors, we establish a stable geometric foundation. This enables a de-terministic, confidence-aware feature registration process and suppressesthe semantic bleeding artifact common in 3DGS. Furthermore, we resolvemulti-level ambiguity by exploiting the emerging dense alignment proper-ties of the AM-RADIO foundation model, avoiding the computationaloverhead of hierarchical training methods. Our approach achieves state-of-the-art performance on Open Vocabulary Point Cloud Understanding,and highly competitive results on 3D Object Retrieval benchmarks.

ECCV 2026

Learning to Generate Rigid Body Interactions with Video Diffusion Models

David Romero, Ariana Bermudez, Viacheslav Iablochnikov, Hao Li, Fabio Pizzati, Ivan Laptev

Recent video generation models have achieved remarkableprogress and are now deployed in film, social media production, and ad-vertising. Beyond their creative potential, such models also hold promiseas world simulators for robotics and embodied decision making. Despitestrong advances, current approaches still struggle to generate physicallyplausible object interactions and lack object-level control mechanisms. Toaddress these limitations, we introduce KineMask, an approach for videogeneration that enables realistic rigid body control, interactions, and ef-fects. Given a single image and a specified object velocity, our methodgenerates videos with inferred motions and future object interactions.We propose a two-stage training strategy that gradually removes futuremotion supervision via object masks. Using this strategy we train videodi!usion models (VDMs) on synthetic scenes of simple interactions anddemonstrate significant improvements and generalization to rigid bodyand hand-object interactions in real scenes. Furthermore, KineMask in-tegrates low-level motion control with high-level textual conditioning viapredicted scene descriptions, leading to support for synthesis of complexdynamical phenomena. Our experiments show that KineMask general-izes to di!erent VDMs and achieves strong improvements over recentmodels of comparable size. Ablation studies further highlight the com-plementary roles of low- and high-level conditioning in VDMs.Project Page: https://daromog.github.io/KineMask/

ECCV 2026

LaMP: Learning Vision-Language-Action Policies with 3D Scene Flow as Latent Motion Prior

Xinkai Wang, Chenyi Wang, Yifu Xu, Mingzhe Ye, Fu-Cheng Zhang, Jialin Tian, Xinyu Zhan, Lifeng Zhu, Cewu Lu, Lixin Yang

We introduce LaMP, a dual-expert Vision-Language-Actionframework that embeds dense 3D scene flow as a latent motion prior forrobotic manipulation. Existing VLA models regress actions directly from2D semantic visual features, forcing them to learn complex 3D physicalinteractions implicitly. This implicit learning strategy degrades underunfamiliar spatial dynamics. LaMP addresses this limitation by aligninga flow-matching Motion Expert with a policy-predicting Action Expertthrough gated cross-attention. Specifically, the Motion Expert generatesa one-step partially denoised 3D scene flow, and its hidden states condi-tion the Action Expert without full multi-step reconstruction. We eval-uate LaMP on the LIBERO, LIBERO-Plus, and SimplerEnv-WidowXsimulation benchmarks as well as real-world experiments. LaMP consis-tently outperforms evaluated VLA baselines across LIBERO, LIBERO-Plus, and SimplerEnv-WidowX benchmarks, achieving the highest re-ported average success rates under the same training budgets. On LIBERO-Plus OOD perturbations, LaMP shows improved robustness with an av-erage 9.7% gain over the strongest prior baseline. Our project page isavailable at https://summerwxk.github.io/lamp-project-page/.

ECCV 2026

K-Mask: Kinematic-Aware Masked Modeling for Controllable Text-to-Motion Synthesis

Faisal Ahmed, Chenqiu Zhao, Anup Basu

Masked generative models have made significant advancesin text-to-motion synthesis. Previous approaches adopt a non-factorized,whole-body tokenization, limiting compositional and fine-grained con-trol. We present K-Mask, a kinematic-aware generative framework thatfactors motion into anatomically grounded groups and learns temporaland cross-group interactions through masked modeling. In the proposedapproach, a kinematic-group residual VQ-VAE (KG-RVQ) encodes mo-tion into disentangled, group-aligned latents. In particular, a latent-awarekinematic dropout (LAKD) loss is proposed to promote within-groupreconstruction and suppress inter-group leakage. Then, we introduce twobidirectional spatiotemporal transformers: (i) a base model that generatescoarse tokens over a time-group lattice with axis-factorized attention, and(ii) a residual model that predicts deeper quantizer layers for refinement.Finally, We introduce kinematic group masking, which randomly occludesentire group-specific subtrees, to enhance inter-group reasoning. On theHumanML3D and KIT-ML text-to-motion benchmarks, K-Mask obtainscompetitive FID scores of 0.041 and 0.154, respectively, while providingan anatomically factorized token interface for localized motion control.

ECCV 2026

Inference-time Motion Calibration for Video Generation

Zile Huang, Ser Nam Lim

Modern video generation models, despite high-quality frame synthesis, still struggle with motion fidelity and temporal consistency, causing artifacts and semantic drift; current solutions typically require extensive backbone retraining. We introduce a training-free framework for inference-time motion calibration that intervenes in the sampling trajectory of pretrained models to correct nascent inconsistencies efficiently. Our framework has two complementary components. First, Dynamic Latent Control (DLC) at selected steps estimates a clean latent, evaluates a VFM-based motion–semantic loss on proxy frames, and applies a corrective update to steer the trajectory. To control overhead, Amortization Trajectory Rectification (ATR) interleaves sparse calibration with fast amortized steps by caching a rectification field. Second, Dynamic RoPE Control (DRC) modulates temporal RoPE in attention blocks from the same motion signals, strengthening long-range coherence or sharpening short-range dynamics when flicker is detected. Extensive experiments on VBench and VBench 2.0 show that our method improves advanced motion fidelity metrics while incurring a small trade-off on superficial temporal smoothness for some backbones.

ECCV 2026

Generative Relightable Avatars

Kunwar Maheep Singh, Christian Theobalt, Rishabh Dabral

We present Generative Relightable Avatars (GRA), a personspecific method for photorealistic free-view rendering and environmentmap relighting of full-body humans. We postulate that modeling finegrained appearance details is inherently a one-to-many problem that can benefit from a generative formulation. In contrast to fully regressive relightable avatar methods, GRA follows a hybrid approach that combines controllable, physics-grounded relighting with probabilistic refinement. Starting from a tracked animated mesh, we optimize material parameters in UV-space and render a coarse relit appearance under a target HDR environment map. Next, we refine the textures with a feedforward model to capture pose-dependent texture dynamics and illumination effects beyond simplified reflectance assumptions. Finally, a finetuned video-to-video diffusion model transforms the physically grounded renderings into temporally coherent, high-detail videos while preserving 3D control, with an error-recycling strategy for generating long videos. Experimental evaluations demonstrate our method’s improved perceptual quality over prior relightable avatar baselines. We urge the readers to watch the supplementary video. See the project page for more details.

ECCV 2026

DreamLite: A Lightweight On-Device Unified Model for Image Generation and Editing

Kailai Feng, Yuxiang Wei, Bo Chen, yang pan, Hu Ye, Songwei Liu, Chenqian Yan, Yuan Gao, Wangmeng Zuo

Diffusion models have made significant progress in both text-to-image (T2I) generation and text-guided image editing. However, thesemodels are typically built with billions of parameters, leading to high la-tency and increased deployment challenges. While on-device diffusionmodels improve efficiency, they largely focus on T2I generation and lacksupport for image editing. In this paper, we propose DreamLite, a com-pact unified on-device diffusion model (0.39B) that supports bothT2I generation and text-guided image editing within a single network.DreamLite is built on a pruned mobile U-Net backbone and unifies con-ditioning through in-context spatial concatenation in the latent space.It concatenates images horizontally as input, using a (target | blank)configuration for generation tasks and (target | source) for editing tasks.To stabilize the training of this compact model, we introduce a task-progressive joint pretraining strategy that sequentially targets T2I, edit-ing, and joint tasks. After high-quality SFT and reinforcement learning,DreamLite achieves GenEval (0.72) for image generation and ImgEdit(4.11) for image editing, outperforming existing on-device models and re-maining competitive with several server-side models. By employing stepdistillation, we further reduce denoising processing to just 4 steps, en-abling our DreamLite could generate or edit a 1024 × 1024 image in lessthan 3s on iPhone 17 pro. To the best of our knowledge, DreamLiteis the first unified on-device diffusion model that supports both imagegeneration and image editing. Our code and model are available at here.

ECCV 2026

DGSeg: Dynamic Gating of Semantic-Spatial Guided Predictions for Reasoning Segmentation

Ruizhe Zeng, Siyu Cao, Lu Zhang, Zhi-yong Liu

Reasoning segmentation aims to predict pixel-wise masks fortargets given complex language queries. Existing approaches leverageMultimodal Large Language Models (MLLMs) for vision-language rea-soning and generate intermediate target cues (e.g., points or boxes) toguide a segmentation model. However, compressing rich reasoning intosparse cues often introduces ambiguity and noise, preventing these cuesfrom accurately preserving the reasoning intent. While multiple comple-mentary cues can enrich target information, existing methods typicallyfeed them jointly into a single segmentation process, allowing ambiguousor erroneous cues to affect the entire prediction. Therefore, we proposeDGSeg, a reasoning segmentation framework that learns to fuse pre-dictions guided by semantic and spatial cues. Specifically, the MLLMjointly reasons about both target identity and spatial location, produc-ing complementary semantic and spatial cues that are fed into separatesegmentation branches. Their predictions are adaptively integrated by alightweight dynamic gating module trained with relative branch-qualitysupervision to suppress noisy or conflicting regions. Extensive experi-ments demonstrate that DGSeg consistently outperforms strong base-lines on multiple benchmarks and achieves 69.6% and 67.3% gIoU on thechallenging ReasonSeg validation and test splits.

ECCV 2026

Detecting Backdoors in Object Detection via Pre-NMS Prediction Distribution Shift

Longtian Wang, Chenhao Lin, Zhengyu Zhao, Le Yang, Shiwei Wang, Yuhan Zhi, Xiaofei Xie, Chao Shen

Object detection models deployed in safety-critical applica-tions remain vulnerable to backdoor attacks that cause targeted misbe-haviors when a hidden trigger is present. Existing detection methods ei-ther rely on trigger inversion or exploit architecture-specific assumptions,and critically, representative existing methods fail to generalize reliablyto scene-level attacks, where a single trigger induces anomalous behavioracross all objects in the scene simultaneously. We present DistScan, abackdoor detection framework based on a simple but previously unex-ploited observation: backdoor injection systematically shifts a model’spre-NMS prediction class distribution away from its training class fre-quencies, even on clean inputs without any trigger present. DistScanaggregates intermediate class predictions over a clean validation set andflags a model as backdoored if the resulting distribution deviates sig-nificantly from the training class frequencies, requiring no model weightaccess, no trigger knowledge, and no additional training. Extensive exper-iments on MS-COCO and PASCAL VOC across two architectures andthree scene-level attack scenarios demonstrate that DistScan substan-tially outperforms existing methods, improving average detection accu-racy over the best-performing applicable baseline by 27.32 percentagepoints.

ECCV 2026

DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing

Hao Chen, Renzheng Zhang, Scott Howard

From a Bayesian perspective, score-based di_x001B_usion solves in-verse problems through joint inference, embedding the likelihood withthe prior to guide the sampling process. However, this formulation failsto explain its practical behavior: the prior o_x001B_ers limited guidance, whilereconstruction is largely driven by the measurement-consistency term,leading to an inference process that is e_x001B_ectively decoupled from thedi_x001B_usion dynamics. We show that the di_x001B_usion prior in these solversfunctions primarily as a warm initializer that places estimates near thedata manifold, while reconstruction is driven almost entirely by measure-ment consistency. Based on this observation, we introduce DAPS++,which fully decouples di_x001B_usion-based initialization from likelihood-drivenre_x001C_nement, allowing the likelihood term to guide inference more directlywhile maintaining numerical stability and providing insight into whyuni_x001C_ed di_x001B_usion trajectories remain e_x001B_ective in practice. By requiringfewer function evaluations (NFEs) and measurement-optimization steps,DAPS++ achieves high computational e_x001E_ciency and robust reconstruc-tion performance across diverse image restoration tasks.

ECCV 2026

ControlHair: Synergizing Physics Simulator and Video Diffusion for Controllable Dynamic Hair Rendering

Weikai Lin, Haoxiang Li, Yuhao Zhu

Hair simulation and rendering are challenging due to complex strand dynamics, diverse material properties, and intricate light–hair interactions. Recent video diffusion models can generate high-quality videos, but they lack fine-grained control over hair dynamics. We present ControlHair, a hybrid framework that integrates a physics simulator with conditional video diffusion to enable precise and controllable dynamic hair rendering. ControlHair adopts a three-stage pipeline: it first encodes physics conditions into per-frame geometry using a simulator, then extracts per-frame control signals, and finally feeds control signals into a video diffusion model to generate videos with desired hair dynamics. This cascaded design decouples physics reasoning from video generation, supports diverse physics, and makes training the video diffusion model easy. Trained on a curated 10K video dataset, ControlHair outperforms textand pose-conditioned baselines, delivering precisely controlled hair dynamics. We also demonstrate three use cases of ControlHair, including dynamic hairstyle try-on, bullet-time effects, and cinemagraphic. Project page: https://linwk20.github.io/controlhair-web.

ECCV 2026

CogSENet: Blind Image Deblurring with Blur-Conditioned Semantic Routing and Explicit Frequency Fusion

pan wang, Yihao Hu, Xiujin Liu

Blind image deblurring demands the recovery of high-fidelitydetails and coherent structures from complex, unknown degradations.Current blind image deblurring methods struggle with real-world, spa-tially varying degradations, and lack the semantic awareness necessary toreliably differentiate valid textures from artifacts. To bridge this gap, wepropose CogSENet, a dynamic, semantic-aligned reconstruction frame-work inspired by the eagle’s visual system. By mimicking the eagle’sactive saccadic scanning, we devise a Semantic-Driven State Space Mod-ule (SDSSM) with semantic-aware token regrouping via differentiablerouting, enabling prompt-conditioned long-range dependency modeling.To ensure physically interpretable recovery of textures and structures,a BiFreqFusionBlock (BFFB) mirrors functional differentiation of theeagle’s retina by decomposing features into high and low frequenciesusing wavelet transforms. Finally, we estimate a continuous Blur Field(CBF) from blur image and fuse it with CLIP semantic priors to modu-late the deepest latent features, emulating focal adaptation and enablingadaptive restoration under spatially non-uniform blur. Extensive exper-iments demonstrate that CogSENet outperforms state-of-the-art deblur-ring methods in both visual quality and structural fidelity with fewerparameters, while also performing favorably on dehazing, deraining, anddenoising tasks.

ECCV 2026

Coding with Eyes: Visual Feedback Unlocks Reliable GUI Code Generating and Debugging

Zhilin Liu, Ye Huang, TingXie TingXie, Ruizhi Zhang, Wen Li, Lixin Duan

Recent advances in Large Language Model (LLM)-based agentshave shown remarkable progress in code generation. However, currentagent methods mainly rely on text-output-based feedback (e.g. command-line outputs) for multi-round debugging and struggle in graphical userinterface (GUI) that involve visual information. This is mainly due totwo limitations: 1) GUI programs are event-driven, yet existing methodscannot simulate user interactions to trigger GUI element logic. 2) GUIprograms possess visual attributes, making it difficult for text-based ap-proaches to assess whether the rendered interface meets user needs. Tosystematically address these challenges, we first introduce InteractGUIBench, a novel benchmark comprising 984 commonly used real-worlddesktop GUI application tasks designed for fine-grained evaluation ofboth interaction logic and visual structure. Furthermore, we propose VF-Coder, a vision-feedback-based multi-agent system for debugging GUIcode. By perceiving visual information and directly interacting with pro-gram interfaces, VF-Coder can identify potential logic and layout issuesin a human-like manner. On InteractGUI Bench, our VF-Coder approachincreases the success rate of Gemini-3-Flash from 21.68% to 28.29% andraises the visual score from 0.4284 to 0.5584, indicating the effectivenessof visual feedback in GUI debugging.

ECCV 2026

CLIMP: Contrastive Language-Image Mamba Pretraining

Nimrod Shabtay, Itamar Zimerman, Eli Schwartz, Raja Giryes

Contrastive Language-Image Pre-training (CLIP) relies onVision Transformers whose attention mechanism is susceptible to spu-rious correlations and scales quadratically with resolution. To addressthese limitations, we present CLIMP, the first fully Mamba-based con-trastive vision-language model that replaces both the vision and text en-coders with state-space architectures. VMamba’s cross-scan mechanismcaptures spatial inductive biases that reduce reliance on spurious correla-tions, producing an embedding space with tighter cross-modal alignmentand lower hubness - geometric properties that translate to superior re-trieval and out-of-distribution robustness, surpassing even CLIP-ViT-Btrained on a dataset 167× larger on ImageNet-O. CLIMP naturally sup-ports variable input resolutions without positional encoding interpolationor specialized training, achieving up to 6.6% higher retrieval accuracyat 16× training resolution while using 5× less memory and 1.8× fewerFLOPs. Mamba’s autoregressive nature further enables processing of ar-bitrarily long text, overcoming CLIP’s fixed 77-token context limitationfor dense captioning retrieval. Our scaling experiments across model sizesand dataset sizes show consistent, unsaturated improvements - indicat-ing that CLIMP’s architectural advantages are not limited by trainingscale. These results demonstrate that Mamba is a compelling alternativeto Transformers for vision-language pre-training. The code and modelsare publicly available at https://github.com/NimrodShabtay/CLIMP

ECCV 2026

Calibrated Harmonic Overlaid Implicit Neural Representations for Multi-Dimensional Data

Honghang Chen, XIUJUN ZHANG, Xiaoli Sun, Mingqing Xiao

Implicit neural representation (INR) has emerged as a pow-erful prior for multi-dimensional data (e.g., multispectral images andvideos). However, most INR methods employing periodic activation func-tions (e.g., Sine) predominantly rely on function composition. This mech-anism introduces optimization instability as network depth increases,thereby limiting their performance. Meanwhile, these methods fail to in-corporate proper physical priors to effectively alleviate spectrum bias. Toaddress these issues, inspired by the commonalities between deep periodicnetworks and generalized Fourier series, we propose a novel CalibratedHarmonic Overlaid Implicit Neural Representation (CHOIR). Specifi-cally, we utilize Coordinated Harmonic Superposition (CHS) to replacethe conventional function composition used in most INRs, thereby en-suring optimization stability when scaling network depth. Furthermore,we introduce a Perceptual Spectrum Calibration (PSC) to mitigate spec-trum bias. This calibration embeds the ubiquitous power-law spectrumprior of natural images and adjusts the globally fixed spectrum towards aphysically plausible log-uniform distribution. Extensive experiments onvarious multidimensional data recovery problems demonstrate that ourmethod achieves superior performance over state-of-the-art approaches.Code is available at https://github.com/chorl0229/CHOIR.

ECCV 2026

Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negatives

Junli Wang, HuaZhihua HuaZhihua, Xueyi Liu, Zebin Xing, Wei Zhang, Kun Ma, Guang Chen, Hangjun Ye, Long Chen, Pengxuan Yang

Existing imitation learning methods for end-to-end autonomousdriving predominantly learn from successful demonstrations by minimiz-ing geometric deviations from expert trajectories. This paradigm implic-itly assumes that spatial proximity implies behavioral safety, leading toa critical objective mismatch: trajectories with nearly identical imita-tion losses may exhibit drastically different safety outcomes, where oneremains recoverable while the other results in collision. To address thislimitation, we propose BeyondDrive, a failure-aware imitation learn-ing framework that jointly learns from successful and failed driving be-haviors. First, we introduce a flow matching-based negative trajectory† Corresponding author. Code link: https://github.com/wjl2244/BeyondDrive.This work was supported by Beijing Natural Science Foundation-Xiaomi InnovationJoint Fund L253007, by Beijing Nova Program (202604841268) and Beijing NaturalScience Foundation under Grant 4242052.generator that synthesizes safety-critical yet expert-proximate trajecto-ries, enabling explicit modeling of safety asymmetry. Second, we developa diversity-aware sampling strategy that mitigates mode collapse andimproves coverage of diverse failure modes during negative trajectorygeneration. Third, we propose a Repulsive Distance Loss that simultane-ously attracts predictions toward expert demonstrations while repellingthem from hard negative trajectories, thereby establishing discrimina-tive safety boundaries in trajectory space. Applied to the uni-modalbaseline Latent TransFuser, BeyondDrive achieves 89.7 PDMS on theNAVSIMv1 closed-loop benchmark, outperforming prior state-of-the-artmethods. Moreover, BeyondDrive generalizes effectively across differentautonomous driving architectures, including multi-modal planners, andfurther demonstrates strong zero-shot transferability on the HUGSIMbenchmark.

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