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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

To Erase, or Not to Erase: Robust Training-Free Concept Erasure with Preservation aware Adaptive Ranked Subspace Expansion

Shaswati Saha, Rajasekhar Anguluri, Manas Gaur

Concept erasure techniques (CETs) edit text-to-image diffusion models to erase undesired targets (e.g., NSFW content or copyrighted styles) while preserving model utility on image generation for benign concepts. Current CETs often face a trade-off between erasure robustness and model utility: stronger edits erase target more reliably, but degrade model utility on non-target concepts and vice-versa. A key source of this trade-off is how existing methods define what to erase and what to preserve. Many CETs rely on static concept banks, where erase or retain concepts are manually specified, generated by Large Language Models (LLMs), or selected using CLIP-based image–text similarity. However, such static banks do not explicitly model how prompts steer the diffusion model during denoising, leaving edited models vulnerable to triggers that can reintroduce the target while unintentionally suppressing nearby benign concepts. We present Preservation aware Adaptive Ranked Subspace Expansion (PARSE), a training-free framework for robust concept erasure in latent diffusion models. Given a target, PARSE queries the diffusion model using classifier-free guidance to dynamically discover target-inducing erase concepts and nearby retain concepts from the model vocabulary. PARSE then edits the cross-attention value space with a preservation-aware projection that removes target directions while leaving retain directions intact. To address triggers beyond the vocabulary-indexed search space, PARSE iteratively searches for re-emergence triggers using textual inversion and adaptively expands the erased subspace only when a new trigger direction does not conflict with retain semantics. We further introduce Balanced Erasure Utility Score (BEUS), a balanced erasure utility score that combines robustness (ASR under multiple attacks) and utility preservation (FID) via bounded monotone transforms and harmonic mean aggregation. Extensive experiments across NSFW, artistic style, and object erasure, together with a large-scale robustness utility analysis over many CET baselines, show that PARSE achieves robust erasure across multiple concepts without sacrificing post-edit model utility. Project page: https: //shaswati1.github.io/parse/

ECCV 2026

TexTailor: Inference-Time Textual Guidance Tailoring for Multimodal Diffusion Transformers

Binglei Li, Mengping Yang, Zhiyu Tan, Junping Zhang, Hao Li

Recent breakthroughs of transformer-based diffusion models, particularly with Multimodal Diffusion Transformers (MMDiT) driven models like FLUX and Qwen Image, have facilitated thrilling experiences in visual generation. However, these models rely only on the interactions between textual conditions and visual features to produce semantically aligned images. Once the interactions fail to reflect the nuanced compositional structure of the prompt, the generated images might be unsatisfactory. Thus, a comprehensive understanding of how different blocks and their interactions with textual conditions is crucial for better understanding the intrinsic attributes and for enhancing their interactions accordingly to strengthen the prompts adherence. In this paper, we first develop a systematic pipeline to comprehensively investigate each block’s functionality by removing, disabling, and enhancing textual hidden-states at corresponding blocks. Our analysis reveals that 1) semantic information appears in earlier blocks and finer details are rendered in later blocks, 2) removing specific blocks is usually less disruptive than disabling text conditions, and 3) enhancing textual conditions in selective blocks improves semantic attributes. Building on these observations, we propose TexTailor, a novel inference-time method for tailoring block-wise textual guidance. Our approach not only improves text-image alignment but also enables a range of downstream applications, including precise editing and inference acceleration. Extensive experiments demonstrated that our method outperforms various baselines and remains flexible across text-to-image generation, image editing, and inference acceleration. Our method improves T2I-Combench from 56.92% to 63.00% and GenEval from 66.42% to 71.63% on SD3.5, without sacrificing synthesis quality. These results advance understanding of MMDiT models and provide valuable insights to unlock new possibilities for further improvements. Our code is available at https://github.com/phil329/TexTailor.

ECCV 2026

TARS: MinMax Token-Adaptive Preference Strategy for Hallucination Reduction in MLLMs

Kejia Zhang, Keda TAO, Zhiming Luo, Chang Liu, Jiasheng Tang, Huan Wang

Multimodal large language models (MLLMs) often halluci-nate, producing fluent but visually ungrounded outputs, partly becausedirect preference optimization (DPO) overfits to superficial linguisticcues under static preference supervision. We propose TARS, a token-adaptive preference strategy that reformulates DPO as a min-max op-timization problem: the inner maximization perturbs visual-agnostic to-kens to induce worst-case distributional shifts, while the outer minimiza-tion enforces alignment with causal visual signals rather than surface-level patterns. A spectral alignment loss further regularizes hidden rep-resentations in the frequency domain via the Fast Fourier Transform(FFT). With only 4.8k preference samples and no expert feedback, TARShalves the hallucination rate (26.4% → 13.2%) and reduces the cognitionscore from 2.5 to 0.4, outperforming standard DPO. It also surpasses 5×larger LLM-based data augmentation (28.8k samples; Hal-Rate 16.0% vs.13.2%) and narrows the gap to GPT-4o, suggesting that token-adaptiveoptimization improves data efficiency beyond simply scaling augmentedpreference data.

ECCV 2026

Synthetic Sub-Aperture Phase Augmentation for Demosaicing 2×2 Shared Microlens Sensors

Jeisung Lee, Wonil Song

Modern high-resolution mobile sensors often adopt clusteredcolor filter array (CFA) layouts such as Quad and Hexadeca Bayer,while some premium sensors use quad-pixel phase-detection or 2 × 2 on-chip-lens (OCL) architectures with shared microlenses for dense phase-detection autofocus (PDAF). While effective for PDAF, this structurecan introduce grid-aligned phase artifacts during demosaicing, which aredifficult to model without calibrated sub-aperture streams, PSF super-vision, or sensor-specific optical calibration. We propose Synthetic Sub-Aperture Phase Augmentation (SPA), a physics-inspired data genera-tion framework for artifact-aware demosaicing. SPA uses compact phasekernels to approximate focus-dependent sub-aperture shifts in syntheticRAW, requiring no measured point spread functions (PSFs) or sensor-specific calibration. Experiments on synthetic and real RAW data fromHexadeca and Quad Bayer sensors show consistent suppression of zipper-ing, color bleeding, and grid-aligned artifacts. We also introduce spatiallattice imbalance (SLI) and Nyquist energy ratio (NER) for 2× 2 artifactanalysis and validate robustness to spatial phase variations.

ECCV 2026

SpaR3D-MoE: Adaptive 3D Spatial Reasoning from Sparse Views Meets Geometry-Inductive Mixture-of-Experts

Haida Feng, Hao Wei, Haolin Wang, Shiwei Li, Chade Li, Yihong Wu

Recent Multimodal Large Language Models (MLLMs) strug-gle to bridge the representational gap between 2D semantic understand-ing and 3D spatial geometry. Existing 3D-aware models either rely oncostly 3D-specific data or utilize RGB-only inputs with heuristic sam-pling and monolithic, shallow fusion, which respectively disrupt essentialspatiotemporal connectivity and induce modality contention across di-verse spatial tasks. To overcome these bottlenecks, we introduce SpaR3D-MoE, an end-to-end framework that enables adaptive spatial reasoningby equipping MLLMs with geometry-aware capabilities from only sparseRGB inputs. First, we propose an adaptive spatiotemporal manifoldsampling mechanism that constructs a geometry-aware spatiotemporalgraph to extract informative keyframes, effectively mitigating sequenceredundancy while preserving the scene’s topological connectivity. Second,we introduce the heterogeneous geometry-inductive Mixture-of-Expertsdriven by an instruction-pose aware router, which adaptively routes mul-timodal tokens to specialized experts, resolving the cross-modal con-tention inherent in monolithic fusion. Extensive experiments on VSI-Bench, ScanQA, and SQA3D demonstrate that our method achievesstate-of-the-art performance. Notably, SpaR3D-MoE achieves the highestaverage score of 63.5 on VSI-Bench, outperforming the strongest base-line by 7.8 absolute points, alongside relative improvements of 35.4% and51.4% in Route Plan and Relative Direction tasks, respectively.

ECCV 2026

SkillSpotter: Pose-Aware Multi-View Skilled Action Detection and Grading in Ego-Exo Videos

Björn Braun, Christian Holz

To enable personalized, real-time coaching using AugmentedReality glasses or fixed camera setups in domains such as sports, cooking,or music, a system must understand not just what a person does, buthow well they execute an activity. In an ego-exo video setting, this re-quires simultaneously detecting individual skilled actions and classifyingeach as correct or needing improvement, which Ego-Exo4D’s proficiencydemonstration benchmark formalized. We first adapt seven state-of-the-art temporal action detection architectures to this task, extend the eval-uation protocol to disentangle detection from grading, and show that ex-isting methods grade near-randomly. We then introduce SkillSpotter , apose-aware multi-view architecture that jointly detects and grades skilledactions through three task-specific modules: (1) adaptive temporal sup-pression to handle the varying density of skilled actions across diverseactivities, (2) gated 3D body pose fusion to leverage body kinematics as acomplementary signal to visual features, and (3) bidirectional cross-viewattention to combine ego and exo views e!ectively. SkillSpotter improvesclass-specific mAP from 12.40 to 21.82 (+76%) and balanced accuracyfrom 55.99% to 60.40% over the best baseline. SkillSpotter ’s modulestransfer to other temporal action detection models with consistent gainsand our method generalizes beyond Ego-Exo4D to HoloAssist.Code: https://github.com/eth-siplab/SkillSpotter

ECCV 2026

Sim, Yet Same: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds

Yunsong Zhou, Hangxu Liu, Xuekun Jiang, Xing Shen, Yuanzhen Zhou, Hui Wang, Baole Fang, Yang Tian, Mulin Yu, Qiaojun Yu, Li Ma, Hengjie Li, Hanqing Wang, Jia Zeng, Jiangmiao Pang

Robotic manipulation with deformable objects represents adata-intensive regime in embodied learning, where shape, contact, andtopology co-evolve in ways that far exceed the variability of rigids. Al-though simulation promises relief from the cost of real-world data acqui-sition, prevailing sim-to-real pipelines remain rooted in rigid-body ab-stractions, producing mismatched geometry, fragile soft dynamics, andmotion primitives poorly suited for cloth interaction. We posit that simu-lation fails not for being synthetic, but for being ungrounded. To addressthis, we introduce SIM1, a physics-aligned real-to-sim-to-real data enginethat grounds simulation in the physical world. Given limited demon-strations, the system digitizes scenes into metric-consistent twins, cali-brates deformable dynamics through elastic modeling, and expands be-haviors via diffusion-based trajectory generation with quality filtering.vision with near-demonstration fidelity. Experiments show that policiestrained on purely synthetic data achieve parity with real-data baselinesat a 1:15 equivalence ratio, while delivering 90% zero-shot success and50% generalization gains in real-world deployment. These results vali-date physics-aligned simulation as scalable supervision for deformablemanipulation and a practical pathway for data-efficient policy learning.

ECCV 2026

SENTRY: SAM2-Enhanced Neighbor-Aware and Temporally Reasoned Memory for Visual Tracking

Mohamad Alansari, Yonathan Michael, Hasan AlMarzouqi, Muhammad Muzammal Naseer, Naoufel Werghi, Sajid Javed

We revisit the memory update mechanism in SAM2-basedvisual object tracking and identify confidence-only mask selection asthe dominant cause of drift under occlusion, rapid motion, and dis-tractors. We introduce SENTRY, a training-free, plug-and-play, refine-before-write module that validates each memory update for short-horizontemporal consistency before committing it. SENTRY aggregates diversesegmentation hypotheses per frame, backtracks them into short tracklets,and uses neighbor-aware cycle-consistent matching against recent trajec-tories to favor temporally and geometrically consistent masks. It leavesthe base architecture untouched, replacing confidence-driven writes withconsistency-validated ones. For fair evaluation, we re-evaluate major open-source SAM2-based trackers across all available scales and datasets, fill-ing gaps in prior reports. Integrated into five strong baselines, SENTRYdelivers consistent gains across nine benchmarks, achieving new zero-shotSOTA on LaSOT, LaSOText , GOT-10k, VOT20, VOT22, and DiDi. De-spite these checks, the SAM2-L version runs at 32.8 FPS on an A100, andacross compatible hosts adds only about 0.4–0.6 GB VRAM. Our resultsprovide the first unified all-scale evaluation of SAM2-based trackers andshow that enforcing temporal validity at write time stabilizes memory-augmented tracking without retraining.

ECCV 2026

Seeing as Humans Do: Learning from Motion to Segment Anything Without Supervision

Weijian Jian, Xiaoyue Zhang, Bin Xiao, Chunyu Xie, Yixiao He, Yutao Liu, Dawei Leng, Yuhui Yin

The Segment Anything Model (SAM) relies heavily on mas-sive manual annotations, creating a fundamental bottleneck for modelscaling. While unsupervised methods attempt to learn object conceptsfrom motion, they typically overfit to moving entities, lacking both multi-granularity understanding and the ability to generalize to static objects.To overcome this, we introduce Motion-Grounded Segment Anything(MoSA), a highly scalable unsupervised framework that learns a trans-ferable objectness prior from unlabeled videos. MoSA operates in threeprogressive stages: (1) automatically generating multi-granularity mo-tion pseudo-labels from large-scale video data; (2) training a PerceptualGrouping Model (PGM) via contrastive learning to internalize a gen-eralized, appearance-driven concept of objects; and (3) transferring thislearned prior into a prompt-guided architecture for segment-anything-style inference on images. Extensive zero-shot evaluations across sevenchallenging benchmarks (e.g., COCO and ADE20K) demonstrate thatMoSA significantly outperforms existing unsupervised methods. Notably,despite using zero manual annotations, MoSA achieves segmentation per-formance comparable to the fully supervised SAM. Our findings revealthat harnessing large-scale unlabeled motion is a feasible and highly scal-able alternative to annotation-driven segment-anything pipelines.

ECCV 2026

Schroedinger’s Cat: Probabilistic Representation and Prediction of Potential Scene Kinematics

Timy Phan, Jannik Wiese, Bjorn Ommer

Predicting how a scene may evolve from partial observationsrequires reasoning about multiple possible futures rather than committingto a single trajectory. Existing approaches either generate appearance-dominated video predictions or sample a small number of trajectorieswithout explicitly modeling the distribution of possible motion. We intro-duce Goal Aware Representations of Future kInEmatic Latent Distribu-tions (GARFIELD), a probabilistic model of scene kinematics that learnsa structured spatio-temporal latent representation of the distribution overpossible futures given an image and optional spatio-temporally sparseconstraints. The same latent representation enables both joint samplingof all trajectories and direct access to the underlying motion distributionthrough an efficient deterministic density decoder. As a result, uncer-tainty about future motion can be localized to specific scene elementsand timesteps and progressively refined through additional constraints.Experiments demonstrate strong motion planning performance competi-tive with large video generation models while sampling trajectories 97×faster. Our method further estimates motion densities two orders of mag-nitude faster than Monte-Carlo sampling from motion generation models,enabling interactive exploration and uncertainty-aware planning.Project page: https://compvis.github.io/schroedingers_cat/

ECCV 2026

SafeGuard: A Multi-Agent Perception-Reasoning Framework for Social-Risk AI-Generated Video Detection

Wenlin Wu, Sheng Zhou, Peipei Song, Wenhao Wang, Junbin Xiao, Xun Yang

As video generation paradigms evolve from localized manip-ulation to full-scene synthesis, AI-generated video detection becomes in-creasingly challenging, as forgeries exhibit coherent global structure andhigh perceptual realism. However, existing benchmarks are biased towardperceptual fidelity and primarily evaluate detectors based on perceptualartifacts, providing limited coverage of scenarios that require reasoningabout violations of physical laws, structural coherence, or social logic.This dataset bias shapes current approaches and results in a Percep-tion–Reasoning Gap: artifact-centric models capture low-level statisticalirregularities yet lack semantic inference, whereas vision-language mod-els perform semantic reasoning but remain insensitive to fine-grainedforensic cues. To bridge this gap, we propose SafeGuard, a multi-agentframework that enables collaborative specialization between forensic per-ception and semantic reasoning. A hierarchical perceptual solver extractsfine-grained forensic evidence, while a self-reflective verifier enforces con-sistency between semantic inference and physical plausibility, forming aninterpretable evidence chain. To support evaluation, we introduce Safe-Vid, a novel AI-generated video detection benchmark comprising 20Kvideos spanning 10 social risk categories, designed to evaluate physicalplausibility, structural consistency, and the rationality of social behav-iors. Extensive experiments demonstrate the generalization of SafeGuard,improving accuracy on SafeVid by +18.7% and consistently outperform-ing prior methods across four public benchmarks. The code and datasetare publicly available at https://github.com/williamw99/SafeGuard.

ECCV 2026

Representation Alignment for Just Image Transformers is not Easier than You Think

Jaeyo Shin, Jiwook Kim, Hyunjung Shim

Representation Alignment (REPA) has emerged as a simpleway to accelerate Diffusion Transformers training in latent space. At thesame time, pixel-space diffusion transformers such as Just image Trans-formers (JiT) have attracted growing attention because they remove adependency on a pretrained tokenizer, and then avoid the reconstruc-tion bottleneck of latent diffusion. This paper shows that the REPA canfail for JiT. REPA yields worse FID for JiT as training proceeds andcollapses diversity on image subsets that are tightly clustered in the rep-resentation space of pretrained semantic encoder on ImageNet. We tracethe failure to an information asymmetry: denoising occurs in the high di-mensional image space, while the semantic target is strongly compressed,making direct regression a shortcut objective. We propose PixelREPA,which transforms the alignment target and constrains alignment witha Masked Transformer Adapter that combines a shallow transformeradapter with partial token masking. PixelREPA improves both train-ing convergence and final quality. PixelREPA reduces FID from 3.66 to3.17 for JiT-B/16 and improves Inception Score (IS) from 275.1 to 284.6on ImageNet 256×256, while achieving > 2× faster convergence. Finally,PixelREPA-H/16 achieves FID= 1.81 and IS= 317.2.

ECCV 2026

Region-Aware Test-Time Scaling for Compositional Image Generation

Mingzhu Shen, Peng Ye, Xinyin Ma, Gongfan Fang, Christos-Savvas Bouganis, Yiren Zhao, Xinchao Wang

Test-time scaling (TTS) has emerged as a promising paradigm for im-proving the performance of large-scale models. However, existing vision TTSmethods largely rely on global exploration—e.g., resampling noise or rewritingprompts—and often struggle to efficiently search the vast compositional space.As a result, they can exhibit a “scaling plateau,” where additional computationyields diminishing returns in semantic alignment. In this paper, we propose Region-Aware Scaling (RAS), a framework that bridges region-aware generation andtest-time scaling. By treating regional decomposition as a powerful and previ-ously overlooked scaling axis, RAS converts complex compositional promptsinto coordinated regional sub-tasks, effectively increasing the density of validcandidates during inference. At its core, RAS builds on a training-free Region-Aware Generation (RAG). Unlike many layout-based methods that incur sub-stantial overhead, RAG injects regional guidance only during early denoising, en-abling precise attribute binding while preserving global structural coherence. Weevaluate RAS on the GenEval benchmark and observe consistent improvementsin scaling efficiency across diverse compositional challenges. RAS achieves anoverall score of 0.85 with only 2 samples, matching a 32-sample noise-scalingbaseline; with 4 samples, it reaches 0.88, surpassing the combination of 32-sample noise and prompt scaling. Overall, our results suggest that structuringthe search space via regional decomposition provides a principled and compu-tationally efficient direction for scaling compositional alignment.

ECCV 2026

Recolour What Matters: Region-Aware Colour Editing via Token-Level Diffusion

Yuqi Yang, Dongliang Chang, Yijia Ling, Ruoyi Du, Zhanyu Ma

Colour is one of the most perceptually salient yet least con-trollable attributes in image generation. Although recent diffusion mod-els can modify object colours from user instructions, their results of-ten deviate from the intended hue, especially for fine-grained and localedits. Early text-driven methods rely on discrete language descriptionsthat cannot accurately represent continuous chromatic variations. Toovercome this limitation, we propose ColourCrafter, a unified diffusionframework that transforms colour editing from global tone transfer intoa structured, region-aware generation process. Unlike traditional colour-driven methods, ColourCrafter performs token-level fusion of RGB colourtokens and image tokens in latent space, selectively propagating colourinformation to semantically relevant regions while preserving structuralfidelity. A perceptual Lab-space Loss further enhances pixel-level pre-cision by decoupling luminance and chrominance and constraining ed-its within masked areas. Additionally, we build ColourfulSet, a large-scale dataset of high-quality image pairs with continuous and diversecolour variations. Extensive experiments demonstrate that ColourCrafterachieves state-of-the-art colour accuracy, controllability and perceptualfidelity in fine-grained colour editing. Our project is available at https://yangyuqi317.github.io/ColourCrafter.github.io/

ECCV 2026

RealGen: Photorealistic Text-to-Image Generation via Detector-Guided Rewards

ye junyan, Leqi Zhu, Yuncheng Guo, Dongzhi Jiang, Zilong Huang, Yifan Zhang, Zhiyuan Yan, Haohuan Fu, Conghui He, Weijia Li

Rapid advancements in image generation have led models likeGPT-Image and Qwen-Image to excel in text-to-image consistency andworld knowledge. However, they still struggle with photorealism, oftenproducing "fake" images laden with obvious AI artifacts, such as overlysmooth skin or unnatural oily sheens. To recapture the original goal of"indistinguishable-from-reality" generation, we propose RealGen, a photo-realistic text-to-image framework. RealGen integrates an LLM componentfor prompt optimization and a diffusion model for realistic image genera-tion. Inspired by adversarial generation, RealGen introduces a "DetectorReward" mechanism, which quantifies artifacts and assesses realism usingboth semantic and feature-level synthetic image detectors. We leveragethis reward with the GRPO algorithm to optimize the entire generationpipeline, significantly enhancing image realism and detail. Furthermore,we propose RealBench, an automated evaluation benchmark employingDetector-Scoring and Arena-Scoring. It enables human-free photorealismassessment, yielding results that are more accurate and aligned with realuser experience. Experiments show that RealGen produces photorealisticimages with enhanced realism, detail, and aesthetics.

ECCV 2026

PointSplat: Compact Gaussian Splatting via Human-Centric Prediction

Yujie Guo, Yudong Jin, Lingteng Qiu, Zehong Shen, Zhen Xu, Zhang Jing, Xianchao Shen, Hujun Bao, Sida Peng, Xiaowei Zhou

Producing 3D human representations from input views onthe fly is essential for immersive live streaming systems, where repre-sentation compactness is as critical as high fidelity given limited com-putational power and transmission bandwidth. Although recent feed-forward reconstruction methods achieve impressive quality through theview-centric prediction of 3D representations, they repeatedly encode thesame subject content across multiple views, leading to significant inter-view redundancy. Our key insight is to perform predictions directly in 3Dspace, enabling the network to learn and produce a highly compact rep-resentation. To this end, we propose PointSplat, a novel human-centricapproach that directly infers Gaussian primitives from an input pointset. The proposed method first estimates a coarse geometric proxy andperforms ray casting to prune redundant points and establish explicit2D–3D correspondences. Subsequently, it employs a Point-Image Trans-former to fuse appearance and geometry features, predicting Gaussianattributes in a single forward pass. This design restricts predictions toforeground regions of interest, substantially reducing the total numberof Gaussians while improving novel-view rendering quality. Extensiveexperiments demonstrate that PointSplat achieves higher efficiency andquality while exhibiting strong robustness to variations in view countand image resolution across multiple datasets.

ECCV 2026

Parametric SDF for Dynamic Surface Reconstruction

Chong Gao, Kai Ye, Qiyu Dai, Yiming Shao, Qiong Zeng, Ding Liang, Yanpei Cao, Guanbin Li, Wenzheng Chen

Reconstructing high-fidelity surfaces of dynamic scenes re-mains a critical challenge. While current methods excel at novel viewsynthesis, they often struggle to recover accurate and stable geometry,particularly under large non-rigid deformations. This results in noisymeshes that are unsuitable for downstream simulation or editing. In thiswork, we introduce a new paradigm for dynamic surface reconstructionbased on a parametric Signed Distance Function (p-SDF). Our key in-sight is to generalize static SDF fields—where each spatial point stores aconstant value—into time-dependent parametric curves where each curvemodels a temporally evolving SDF trajectory. This parametric SDF rep-resentation provides a principled way to capture complex temporal varia-tions, naturally enforcing smoothness and continuity in shape dynamics.At each timestamp, a static SDF field can be queried from p-SDF andconverted into an explicit surface mesh via differentiable iso-surfacing.By rendering these meshes with a physically based differentiable ren-derer, we optimize the underlying parametric curves end-to-end against2D image observations. Our framework produces high-fidelity surfacesand inherently disentangles geometry, material, and lighting from multi-view videos. It robustly reconstructs geometry under large-scale motionsand resolves appearance ambiguities caused by lighting and occlusions.Experiments on both synthetic and real-world scenes demonstrate thatour method achieves state-of-the-art geometric accuracy. The projectpage for p-SDF is available at https://timchonggao.github.io/psdf.

ECCV 2026

OpenVE-3M: A Large-Scale High-Quality Dataset for Instruction-Based Video Editing

Haoyang He, Jie Wang, Jiangning Zhang, Zhucun Xue, Xingyuan Bu, Qiangpeng Yang, Min Zheng, Lei Xie

The quality and diversity of instruction-based image editing datasets are continuously increasing, yet large-scale, high-quality datasets for instruction-based video editing remain scarce. To address this gap, we introduce OpenVE-3M, an open-source, large-scale, and high-quality dataset for instruction-based video editing. It comprises two primary categories: spatially-aligned edits (Global Style, Background Change, Local Change, Local Remove, Local Add, and Subtitles Edit) and non-spatially-aligned edits (Camera Multi-Shot Edit and Creative Edit). All edit types are generated via a meticulously designed data pipeline with rigorous quality filtering, a process consuming in excess of 10,000 GPU-days. OpenVE-3M surpasses existing open-source datasets in terms of scale, diversity of edit types, instruction length, and overall quality. Furthermore, to address the lack of a unified benchmark in the field, we construct OpenVE-Bench, containing 431 video-edit pairs that cover a diverse range of editing tasks with three key metrics highly aligned with human judgment. We present OpenVE-Edit, a 5B model trained on our dataset that demonstrates remarkable efficiency and effectiveness by setting a new state-of-the-art on OpenVE-Bench, outperforming all prior open-source models including a 14B baseline.

ECCV 2026

End-to-End Training for Autoregressive Video Diffusion via Self-Resampling

Yuwei Guo, Ceyuan Yang, Hao He, Yang Zhao, Meng Wei, Zhenheng Yang, Weilin Huang, Dahua Lin

Autoregressive video diffusion models hold promise for worldsimulation but are vulnerable to exposure bias arising from the train–testmismatch. While recent works address this via post-training, they typi-cally rely on a bidirectional teacher model or discriminator. To achievean end-to-end solution, we introduce Resampling Forcing, a teacher-free framework that enables training autoregressive video models fromscratch and at scale. Central to our approach is a self-resampling schemethat simulates inference-time model errors on history frames during train-ing. Conditioned on these degraded histories, a sparse causal mask en-forces temporal causality while enabling parallel training with frame-leveldiffusion loss. To facilitate efficient long-horizon generation, we furtherintroduce history routing, a parameter-free mechanism that dynamicallyretrieves the top-k most relevant history frames for each query. Experi-ments demonstrate that our approach achieves performance comparableto distillation-based baselines while exhibiting superior temporal consis-tency on longer videos owing to native-length training. See our ProjectPage for more details.

ECCV 2026

OARS: Process-Aware Online Alignment for Generative Real-World Image Super-Resolution

Shijie Zhao, Xuanyu Zhang, Bin CHEN, Weiqi Li, Qunliang Xing, Kexin Zhang, Yan Wang, Junlin Li, Li Zhang, Jian Zhang, Tianfan Xue

Aligning generative real-world image super-resolution mod-els with human visual preference is challenging due to the perception–fidelity trade-off and diverse, unknown degradations. Prior approachesrely on offline preference optimization and static metric aggregation,which are often non-interpretable and prone to pseudo-diversity understrong conditioning. We propose OARS, a process-aware online align-ment framework built on COMPASS, a MLLM-based reward that eval-uates the LR→SR transition by jointly modeling fidelity preservation andperceptual gain with an input-quality-adaptive trade-off. To train COM-PASS, we curate COMPASS-20K spanning synthetic and real degrada-tions, and introduce a three-stage perceptual annotation pipeline thatyields calibrated, fine-grained training labels. Guided by COMPASS,OARS performs progressive online alignment from cold-start flow match-ing to full-reference and finally reference-free RL via shallow LoRA opti-mization for on-policy exploration. Extensive experiments and user stud-ies demonstrate consistent perceptual improvements while maintainingfidelity, achieving state-of-the-art performance on Real-ISR benchmarks.

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