ECCV 2026 论文列表

This work presents Lapis, a linear-attention-based pixel-space generative framework that achieves efficient and high-fidelity depthestimation with one-step diffusion. While generative frameworks havesignificantly advanced monocular depth estimation with superior detailfidelity, the O(N 2 ) complexity of standard attention and the multi-stepdenoising process introduce prohibitive computational costs when scal-ing them to high-resolution image applications. Although linear atten-tion and one-step prediction are intuitively viable, directly applying themleads to poor structural consistency, detail loss, and noise. Lapis recti-fies these limitations through a coarse-to-fine hierarchy. Specifically, aPatch-level Consistency Module restores structural coherence by inte-grating semantic and spatial priors. Subsequently, a Pixel-level Refine-ment Module recovers sharp geometric boundaries via skip-connection-based pixel correspondence. Furthermore, to mitigate sampling noise in-herent in one-step diffusion, we leverage the manifold assumption andadopt a direct x-prediction strategy to target the clean data manifold.Extensive evaluations on multiple benchmarks demonstrate that Lapisconsistently achieves state-of-the-art (SOTA) accuracy and boundarysharpness across various resolutions, reducing inference latency by upto 7.6× at 1080P and 10.9× at 1440P resolution compared to previousSOTA generative models.

Language-guided prompt-based continual learning emergesas a promising approach to tackle catastrophic forgetting in a dynamicenvironment under rehearsal-free constraint. However, existing state-of-the-art (SOTA) requires additional resources, such as LLM-generateddescriptors and an LLM decoder that may not be available in a real ap-plication. Meanwhile, the more straightforward method experiences sub-optimality due to the high similarity between language text embeddings.To address this problem, we propose a resource-efficient novel language-guided approach and algorithm that incorporates two novel ideas: (1)mixed vision and language modality for prompt generation, and (2) Dif-ferential prefix tuning for the model training process. Our experimentalanalysis shows that our method outperforms existing language-guidedprompt-based methods, i.e., up to 30%, 26%, and 10% for final aver-age accuracy, cumulative average accuracy, and final forgetting mea-sure, respectively. The historical analysis confirms our method’s stability-plasticity balance in every task. Our extended analysis shows that ourmethod consistently achieves better performance in various prompt lengthsand ViT layers. For further study and reproducibility, we also providerigorous analysis, details, and source code of our method in the sup-plementary document. The implementation of the proposed method isavailable at https://github.com/anwarmaxsum/D-VLAM.

LiDAR-based 3D object detection is essential for autonomousdriving systems. However, traditional Ego-only Perception (Eo-Perception)suffers from limited perspective and occlusions in a complex outdoor en-vironment, leading to performance bottlenecks. Recently, research onmulti-agent Collaborative Perception (Co-Perception) has demonstratedexcellent performance, but high communication costs and accumulatedpose error hinder its application. To address this, we explore a novelC2E (Co-Perception to Eo-Perception) paradigm through the Multi-to-Single (M2S) agent contrastive knowledge distillation framework. OurM2S framework first designs Multi-Level Feature Enhancement moduleto provide more stable features, and introduces Auxiliary Point CloudReconstruction and Multi-Teacher Contrastive Distillation mechanismsto mitigate domain gaps in point cloud and feature distributions withinthe C2E paradigm. Benefiting from this, our M2S can retain the ex-cellent performance of collaborative perception while effectively avoidingthe drawbacks, such as communication delays and positioning errors. Ex-tensive experiments on the V2XSet, V2V4Real and DAIR-V2X datasetsshow the effectiveness and generalizability of our M2S framework whencombined with the state-of-the-art CoSDH model and other excellent 3Ddetectors. Our M2S framework can deliver up to a 8.64% improvementin 3D mAP performance without introducing any communication costs.

Zero-shot skeleton-based action recognition aims to identifyunseen actions via semantic transfer. The existing methods often strug-gle with the topological mismatch between skeleton and text manifolds,leading to loose decision boundaries due to weak constraints and fail-ing to adapt to unseen distributions during static inference. To miti-gate this challenge, we propose ProtoFM, a novel generative frameworkbased on conditional flow matching and prototype guidance. First, in-stead of heterogeneous skeleton-text matching, ProtoFM introduces agenerative matching paradigm that performs homogeneous skeleton andpseudo-skeleton matching by synthesizing features via ODE-based flowintegration. Second, we propose a prototype-guided center loss that an-chors flow-trajectory states to learnable class prototypes, encouragingcompact semantic clusters and improved class separation. Last, at theinference phase, we develop a training-free prototype-guided rectificationstrategy that exploits a global memory bank for progressive test-timerefinement. Extensive experimental results obtained on multiple bench-marking datasets demonstrate the superiority of the proposed ProtoFMmethod over the state-of-the-art approaches.

Recent 3D generative models produce high-quality geometryfrom a single image using large-scale priors and diffusion architectures.However, they assume complete visibility and single-view inputs, limitingapplicability in multi-view, occluded, or editing scenarios. Although priorworks address these challenges individually, they lack a unified frameworkfor controllable 3D completion under diverse conditioning signals.We present Axolotl3D, a multi-modal and occlusion-aware 3D generationmodel that jointly conditions on images, visibility masks, camera param-eters, and a partial point cloud. The point cloud serves as a geometricanchor promoting faithful shape completion, while camera parametersensure consistent multi-view alignment in a shared 3D coordinate sys-tem. A unified training strategy synthesizes diverse conditioning regimesfrom large-scale 3D data, enabling robust cross-modal reasoning.Experiments on Toys4K and OmniObject3D demonstrate state-of-the-art performance under both clean and occluded settings, as well as strongresults in real-world reconstruction and geometry-consistent editing.

Traditional high-quality 3D scanning and reconstruction typ-ically relies on human labor to plan the scanning procedure. With therapid development of embodied systems such as drones and robots, thereis a growing demand of performing accurate 3D scanning and recon-struction in an fully automated manner. We introduce Auto3R, a data-driven uncertainty quantification model that is designed to automatethe 3D scanning and reconstruction of scenes and objects, includingobjects with non-lambertian and specular materials. Specifically, in aprocess of iterative 3D reconstruction and scanning, Auto3R can makeefficient and accurate prediction of uncertainty distribution over poten-tial scanning viewpoints, without knowing the ground truth geometryand appearance. Through extensive experiments, Auto3R achieves supe-rior performance that outperforms the state-of-the-art methods, partic-ularly on challenging viewpoints. We also deploy Auto3R on a robotarm equipped with a camera and demonstrate that Auto3R can beused to effectively digitize real-world 3D objects and delivers ready-to-use and photorealistic digital assets. Our code is available at https://tomatoma00.github.io/auto3r.github.io/.

Deep neural networks often show poor generalization whentrained on biased datasets presenting spurious relations between sam-ples and target labels. Unsupervised debiasing methods aim at mitigat-ing models’ dependency on bias, without relying on bias information.Bias shortcuts are typically learned early in training, with the modelrapidly overfitting the few samples not sharing the same related at-tributes. Avoiding bias conflicting memorization for mining bias infor-mation useful for mitigation (e.g., in the form of pseudo-labels) is still afundamental open issue in this context, with existing approaches propos-ing to learn for a few epochs or to rely on unbiased validation sets for earlystopping. In this work, we propose to exploit intermediate layers’ featuresfor achieving bias mitigation robust to the memorization problem, replac-ing the when to stop with a where to look paradigm. Specifically, we pro-pose AracNet, an unsupervised debiasing framework for self-mitigation.Attaching one linear layer to each network’s block, which we refer to asShallow Monitor, we obtain a useful debiasing signal capable of miti-gating bias dependency in the same model when randomly reinitialized.Our results on typical benchmark datasets with single and multiple biasesconfirm the effectiveness of the proposed approach, paving the way forfurther research in understanding how bias propagates in trained models.Code is available at https://github.com/Malga-Vision/AracNet

We introduce a framework toward scalable, finer-grained object-level 3DGS compression. First, we propose a post-training method namedRecastGS to reorganize pretrained 3DGS into a layered representationand progressively distill cumulative submodels to improve rate–distortionefficiency. Leveraging multi-view SAM predictions from user click prompts,Gaussians are further partitioned into user-defined regions of interest(ROI), enabling region-adaptive quality control without retraining. Sec-ond, built upon this reorganized region-aware layered hierarchy, a feed-forward 3DGS compression method named LayeredCGS is proposed tocompress position using a lightweight point cloud codec and attributeswith a layer-wise context model to exploit cross-layer correlations. Ex-tensive experiments show that LayeredCGS achieves 35% BD-Rate gainover the existing feed-forward method FCGS. With progressive distil-lation in RecastGS enabled, our method further outperforms most per-scene optimization methods. Moreover, the proposed method supportsROI-aware compression and flexible bitstream truncation, achieving upto 2 dB higher ROI PSNR at comparable bitrates compared with theuniform quality allocation baseline while enabling low-latency previewand progressive quality refinement. The code will be released at https://github.com/RuixiangXue/ScalableGSC.

Diffusion models are powerful deep generative models, butunlike classical models, they lack an explicit low-dimensional latent spacethat parameterizes the data manifold. This absence makes it difficultto perform manifold-aware operations, such as geometrically faithful in-terpolation or conditional guidance that respects the learned manifold.We propose a training-free Riemannian metric on the noise space, de-rived from the Jacobian of the score function. The key insight is thatthe spectral structure of this Jacobian separates tangent and normaldirections of the data manifold; our metric leverages this separation to en-courage paths to stay tangential to the manifold rather than drift towardhigh-density regions. To validate that our metric faithfully captures themanifold geometry, we examine it from two complementary angles. First,geodesics under our metric yield perceptually more natural interpolationsthan existing methods on synthetic, image, and video frame datasets.Second, the tangent–normal decomposition induced by our metric pre-vents classifier-free guidance from deviating off the manifold, improvinggeneration quality while preserving text-image alignment.

The growing availability of trajectory datasets has fueledmajor advances in data-driven motion prediction. Yet, models trained onone dataset often fail to generalize beyond their training domain as a resultof differences in scene layouts, agent behaviors, and sensing conditions. Aframework that learns latent representations of datasets and quantifiestheir similarity using distributional metrics is presented. This large-scalestudy covers 24 major datasets, including the most widely used motion-prediction benchmarks, and shows that the resulting transferability scoresstrongly correlate with cross-dataset model performance. The resultsprovide practical guidance for dataset selection, pretraining, and large-scale foundation models for motion prediction, paving the way towardmore generalizable and robust predictive systems.

How can we tell whether a video has been sped up or sloweddown? How can we generate videos at different speeds? Although videoshave been central to modern computer vision research, little attentionhas been paid to perceiving and controlling the passage of time. In thispaper, we study time as a learnable visual concept and develop modelsfor reasoning about and manipulating the flow of time in videos.We first exploit the multimodal cues and temporal structure naturallypresent in videos to learn, in a self-supervised manner, to detect speedchanges and estimate playback speed. We then show that these learnedtemporal reasoning models enable us to curate the largest slow-motionvideo dataset to date from noisy in-the-wild sources. Such slow-motionfootage, typically filmed by high-speed cameras, contains substantiallyricher temporal detail than standard videos. Using this data, we furtherdevelop models capable of temporal control, including speed-conditionedvideo generation, which produces motion at specified playback speed,and temporal super-resolution, which transforms low-FPS, blurry videosinto high-FPS sequences with fine-grained temporal details. Our findingshighlight time as a manipulable, perceptual dimension in video learning,opening doors to temporally controllable video generation, temporalforensics detection, and potentially richer world-models that understandhow events unfold over time. Project page: https://seeing-fast-and-slow.github.io/.

Recent text-guided image editing (TIE) models have achievedremarkable progress, while many edited images still suffer from issuessuch as artifacts, unexpected editings, unaesthetic contents. Althoughsome benchmarks and methods have been proposed for evaluating editedimages, scalable evaluation models are still lacking, which limits the de-velopment of human feedback reward models for image editing. To ad-dress the challenges, we first introduce EditHF-1M, a million-scale im-age editing dataset with over 29M human preference pairs and 148Khuman mean opinion ratings, both evaluated from three dimensions,i.e., visual quality, instruction alignment, and attribute preservation.Based on EditHF-1M, we propose EditHF, a multimodal large lan-guage model (MLLM) based evaluation model, to provide human-alignedfeedback from image editing. Finally, we introduce EditHF-Reward,which utilizes EditHF as a reward signal to optimize the text-guidedimage editing models through reinforcement learning. Extensive experi-ments show that EditHF achieves superior alignment with human prefer-ences and demonstrates strong generalization on other datasets. Further-more, we fine-tune the Qwen-Image-Edit using EditHF-Reward, achiev-ing significant performance improvements, which demonstrates the abil-ity of EditHF to serve as a reward model to scale-up the image editing.Both the dataset and code will be released in our GitHub repository:https://github.com/IntMeGroup/EditHF.

Despite the remarkable progress of large vision languagemodels (LVLMs), object hallucination remains a fundamental challengethat hinders their trustworthy deployment. A key finding motivates ourwork: real and hallucinated object tokens are clearly separable in hid-den representations, yet this separability is largely lost at the language-modeling (LM) head. We propose TruthLens, a self-evaluation frame-work that teaches the LM head to expose a per-object truthfulness sig-nal without any auxiliary model or additional inference cost. Concretely,a rarely-used special token is repurposed as a reference token. For eachobject-token position, we extract the log-probability assigned to this spe-cial token by the LM head, and define its difference from a predefinedconstant as the truthfulness score. The model is then fine-tuned withan MSE objective that drives scores toward 1 for real objects and 0for hallucinated ones, while a divergence constraint preserves the origi-nal generation capability. Despite being trained on only a limited set ofobject categories, TruthLens generalizes effectively to benchmarks withsubstantially larger label spaces. Extensive experiments across multipleLVLMs demonstrate state-of-the-art performance; notably, on Qwen2.5-VL-7B, TruthLens outperforms the previous best method on MS-COCOby over 17% in AUROC. Our code is available at https://github.com/wyqstan/TruthLens.

3D Gaussian Splatting achieves impressive novel view syn-thesis for ground-level scenes. However, its performance degrades sig-nificantly in aerial domains due to sparse viewpoint sampling imposedby platform constraints. Existing sparse-view methods typically rely onstructure-from-motion reconstruction, which often yields incomplete ge-ometry due to limited feature correspondences under repetitive texturesand sparse viewpoint overlap. We present Geometry-Propagated Gaus-sian Splatting (GeoProp-GS), which overcomes the limitations of in-sufficient initialization. Our approach introduces two core components:Depth-guided Geometric Initialization (DGI) generates dense point cloudsand extends coverage to unobserved regions; Anchor-constrained Gaus-sian Optimization (AGO) stabilizes under-supervised regions by decom-posing proposal Gaussians into reliable anchors and learnable residuals.Extensive experiments demonstrate that GeoProp-GS achieves state-of-the-art performance and serves as an effective plug-and-play module thatconsistently improves existing methods. Code available at GeoProp-GS .

Implicit neural representation (INR) has achieved remark-able progress in novel view synthesis and image/video coding in recentyears. Compared to conventional end-to-end image codecs, INR-basedcompressors demonstrate significant advantages in decoding complexity.However, their practical application has been hindered by the inferiorencoding speed and underutilized decoding efficiency. In this work, wepropose a feedforward INR image coding architecture, Practical INRImage Codec (PIC), that computes all the necessary information forINR network in a single forward pass, achieving an encoding speed of20 FPS. Additionally, we implement a highly optimized decoder thatreaches 2000 FPS decoding speed, significantly surpassing JPEG’s per-formance at comparable rate-distortion (RD) performance. To the best ofour knowledge, this work presents the first learning-based image codecthat simultaneously outperforms or is comparable with JPEG in bothRD performance and decoding speed while maintaining practical encod-ing speed. Code is available at https://github.com/actcwlf/PIC.

Uncalibrated volumetric video streaming for human reconstruction is essential for holographic communication and AR/VR, yet remains challenging due to the need for temporal consistency and computational efficiency from sparse-view inputs. Existing methods rely on perscene optimization or calibrated cameras, while recent feed-forward models are limited to low-resolution (0.5K) single-frame synthesis. We present HiReFF, a feed-forward method for 2K-resolution 360° human video reconstruction from uncalibrated sparse-view videos. Our framework decomposes the problem into two key tasks: foreground 3D Gaussian reconstruction from sparse-view videos (four views separated by 90°) and computationally efficient high-resolution synthesis. To enable the former, we propose Scale-synchronized Camera Calibration to resolve scale ambiguity for multi-view supervision, and Gaussian-wise Foreground Masking to reconstruct clean foregrounds by modulating Gaussian parameters. For efficient high-resolution synthesis, our High-resolution Side-tuning achieves 2K rendering by augmenting the Gaussian head with supplementary features while keeping the backbone at 0.5K, drastically reducing computational overhead. Experiments demonstrate that HiReFF significantly outperforms existing methods in high-resolution streaming volumetric video reconstruction. https://iridescentjiang.github.io/ HiReFF

Recent diffusion-based models have enabled realistic audio-driven avatar generation in real-time streaming. However, existing ap-proaches struggle to maintain visual temporal consistency and fail toexplicitly perceive user intent in complex interactive streaming scenar-ios. To address these challenges, we propose InteractiveAvatar, a real-time infinite-streaming video generation framework that supports vi-sually consistent avatar video generation and intent-aware interactions.With autoregressive distillation, InteractiveAvatar achieves real-time str-eaming generation of human avatars over arbitrarily long durations. Forvisual consistency, we introduce a Long-Short Visual Memory (LSVM)mechanism that flexibly compresses historical visual information intocompact tokens, preserving both short-range coherence and long-termconsistency. To generate avatars with speeches and actions aligned withuser intent, we propose a Reasoning-Reaction Module (RRM), which in-corporates a State-Cycling strategy and a Cache-Switching mechanism.Extensive experimental results over diverse scenarios demonstrate thatour method achieves state-of-the-art visual consistency in long-durationgeneration, while enabling complex user-avatar interaction in real time.

Learning effective robot control policies on physical hard-ware is challenging due to costly data collection and the difficulty ofreward specification. Prior work has incorporated demonstrations intoreinforcement learning (RL), yet existing approaches either require largenumbers of demonstrations or depend on continuous human interventionduring training. To address these limitations, we present AutoSERL, aframework that leverages a single demonstration to fully automate the in-tervention process in real-world robot RL. The framework includes threecomplementary mechanisms to accomplish certain tasks: a sliding win-dow intervention that continuously guides exploration to prevent localoptima and unsafe deviations, a safety recovery mechanism that detectsand corrects failure states via predefined trajectory recovery points, andan intervention termination criterion that automatically disables guid-ance once the policy can independently complete the task, preserving itsexploration advantage. We evaluate AutoSERL on six contact-intensivemanipulation tasks across two robot platforms, spanning insertion, hang-ing, and hinge-based tasks. AutoSERL consistently outperforms SERLinitialized with 20 demonstrations, behavior cloning, and MILES — adedicated one-shot imitation learning baseline — across all tasks whilematching HIL-SERL, achieves 100% success rate on insertion tasks, anddemonstrates improved robustness to positional variations, all from a sin-gle demonstration. Code and videos are available on our project website:https://autoserl.github.io/.

High definition map generation is essential for autonomousdriving, yet remains a labor-intensive process at scale. We present Map-Dreamer, a generative diffusion model that synthesizes lane-level vec-tor maps with explicit topology directly from a single aerial image.MapDreamer learns a compact latent representation of lane centerlinesand their topological relations using a variational autoencoder and pre-dicts graphs with a transformer-based latent diffusion model. To aligngenerated maps with the observed scene, we condition each denoisingstep on dense aerial features injected through cross-attention. To handlethe varying number of lanes across scenes, we propose a lane cardinal-ity module paired with background ghost lane latents, a learned bufferthat prevents slot collapse during diffusion. Furthermore, we introduce asliding-window global graph aggregation strategy that stitches local tilesinto city-scale maps while preserving connectivity through encoded laneboundaries. Experiments on UrbanLaneGraph derived from Argoverse2 show improved geometric and topological fidelity over non-generativebaselines.