ECCV 2026 Papers

Event cameras provide sequential visual data with spatialsparsity and high temporal resolution, making them attractive for low-latency object detection. Existing asynchronous event-based neural net-works exploit this low-latency advantage by updating predictions eventby event, but still suffer from two bottlenecks: recurrent architecturesare difficult to train efficiently on long sequences, and improving accu-racy often increases per-event computation and latency. Linear atten-tion is appealing because it enables parallel training and recurrent infer-ence. However, its dense state updates make per-event computation scalewith the state size, yielding a poor accuracy-efficiency trade-off for ob-ject detection, where accurate localization requires fine-grained spatialstates. The key challenge is therefore to introduce sparse state activa-tion that exploits the spatial sparsity of events while preserving efficientparallel training. We propose Spatially-Sparse Linear Attention (SSLA),which introduces a mixture-of-spaces state decomposition and a scatter-compute-gather training procedure, enabling state-level sparsity as wellas training parallelism. Building on SSLA, we develop an end-to-endasynchronous linear attention model, SSLA-Det, for low-latency event-based object detection. On Gen1 and N-Caltech101, SSLA-Det achievesstate-of-the-art accuracy among asynchronous methods, reaching 0.375mAP and 0.515 mAP, respectively, while reducing per-event computationby over 20

Prompt-conditioned foundation segmenters rely on explicit spatial prompts, such as points, boxes, or masks, to guide mask decoding. In prompt-absent deployment, however, no external/user prompts or detector-generated prompts are available at test time, creating a mismatch between the expected prompt-conditioned interface and automatic inference. We propose IP-SAM, a prompt-space adaptation framework that restores prompt-conditioned decoding without external prompts. Instead of bypassing SAM2’s prompt interface through direct feature-space adaptation, IP-SAM synthesizes complementary intrinsic foreground/background prompts and routes their dense logits through SAM2’s frozen prompt encoder, translating task-specific spatial cues into the native prompt embedding space. To reduce background leakage under severe camouflage, Prompt-Space Gating uses the intrinsic background prompt as an asymmetric suppressive constraint before decoding. Under a deterministic noexternal-prompt protocol, IP-SAM achieves state-of-the-art performance across four COD benchmarks, including MAE 0.017 on COD10K, with only 21.26M trainable parameters from SPG, PSG, a SAM2-initialized downstream mask decoder, and image-encoder LoRA, while keeping the prompt encoder frozen. Medical polyp experiments in the main paper and supplementary SOD results further show that the same prompt-space adaptation route transfers within foreground–background segmentation.

Text-conditioned human interaction generation must cap-ture both long-range temporal causality within each individual and tightlycoupled coordination between partners. Existing interaction diffusionmodels typically denoise full sequences using bidirectional attention, whichobscures causality and hinders streaming and long-horizon generation.Autoregressive alternatives enforce causality but often suffer from tem-poral drift, leading to coordination degradation and unstable interactiondynamics over time. We propose InterCMDM, a block-causal latent diffu-sion framework for autoregressive two-person interaction generation. In-terCMDM introduces a Dual-Stream Causal Diffusion Transformer thatmaintains separate causal streams for each person while modeling inter-person dependencies via unified dual-stream attention with multi-taskattention masks. These masks unify interaction modeling within a sin-gle attention mechanism and support diverse coordination behaviors, in-cluding simultaneous actions, reactive responses, leader–follower dynam-ics, and independent motion. By training a single model across thesemask configurations as a form of data augmentation, InterCMDM en-ables controllable interaction generation by simply selecting the desiredattention mask at inference time. Finally, a block-wise diffusion objec-tive enables stable latent rollout over long sequences without repeateddecode–encode cycles. InterCMDM achieves state-of-the-art performanceon InterHuman and Inter-X, improving text–motion alignment, realism,and long-horizon continuity.

Learning a generalist control policy for robotic manipula-tion typically relies on large-scale datasets. Given the high cost of real-world data collection, a practical alternative is to generate synthetic datathrough simulation. However, the resulting synthetic data often exhibitsa significant gap from real-world distributions. While many prior studieshave proposed algorithms to bridge the Sim-to-Real discrepancy, thereremains a lack of principled research that grounds these methods in real-world manipulation tasks, particularly their performance on generalistpolicies such as Vision-Language-Action (VLA) models. In this study,we empirically examine the primary determinants of Sim-to-Real gen-eralization across four dimensions: multi-level domain randomization,photorealistic rendering, physics-realistic modeling, and reinforcementlearning updates. To support this study, we design a comprehensive eval-uation protocol to quantify the real-world performance of manipulationtasks. The protocol accounts for key variations in background, light-ing, distractors, object types, and spatial features. Through experimentsinvolving over 10k real-world trials, we derive critical insights into Sim-to-Real transfer. To inform and advance future studies, we release boththe robotic platforms and the evaluation protocol for public access tofacilitate independent verification, thereby establishing a realistic andstandardized benchmark for robotic manipulation policies.

We address the problem of training on long-tailed data forvideo action recognition. We propose to augment the training set usinga text-to-video generative model, conditioned on diverse text promptsgrounded in action profiles and training exemplars. Our approach, calledGen2Balance, converts an imbalanced training set into a balanced com-bination of real and generated video clips. To effectively learn from suchdata, we employ a two-stage training strategy that mitigates domainshift and yields significant improvements.We evaluate on long-tailed versions of standard benchmarks: UCF-101(UCF-LT) and a 100-class subset of Kinetics (K100-LT) selected to pri-oritise temporally challenging actions. Gen2Balance improves accuracyover the strongest baselines for long-tailed learning by 5.1% and 7.0% onthe respective datasets. On rare actions from the RareAct dataset (e.g.,cut keyboard ), Gen2Balance improves accuracy by 31.9%, demonstrat-ing effectiveness for scarce actions. By varying the amount of syntheticdata added, we show that partial balancing already achieves 79% of theperformance gains at 27% of the compute cost on K100-LT, highlightingthe practical scalability of Gen2Balance.

Diffusion-based image editing models have achieved signifi-cant progress in real world applications. However, conventional modelstypically rely on natural language prompts, which often lack the pre-cision required to localize target objects. Consequently, these modelsstruggle to maintain background consistency due to their global im-age regeneration paradigm. Recognizing that visual cues provide an in-tuitive means for users to highlight specific areas of interest, we uti-lize bounding boxes as guidance to explicitly define the editing tar-get. This approach ensures that the diffusion model can accurately lo-calize the target while preserving background consistency. To achievethis, we propose FineEdit, a multi-level bounding box injection methodthat enables the model to utilize spatial conditions more effectively.To support this high precision guidance, we present FineEdit-1.2M,a large scale, fine-grained dataset comprising 1.2 million image edit-ing pairs with precise bounding box annotations. Furthermore, we con-struct a comprehensive benchmark, termed FineEdit-Bench, which in-cludes 1,000 images across 10 subjects to effectively evaluate region basedediting capabilities. Evaluations on FineEdit-Bench demonstrate thatour model significantly outperforms state-of-the-art open-source models(e.g., Qwen-Image-Edit and LongCat-Image-Edit) in instruction compli-ance and background preservation. Further assessments on open bench-marks (GEdit and ImgEdit Bench) confirm its superior generalizationand robustness. The dataset and code will be publicly available throughthe project page at https://laulampaul.github.io/FineEdit.

High-fidelity and expressive controllable human animation isessential for content creation and digital avatar applications. However,existing methods face a dilemma between expressiveness and disentangle-ment. Mainstream 2D pose-conditioned approaches suffer from "motion-shape entanglement", leading to the leakage of the driving subject’s bodyshape. Conversely, methods relying on 3D priors (e.g., SMPL) achievegeometric disentanglement but struggle to capture facial expressions andcomplex gestures, resulting in rigid animations. To this end, we proposeEMOSH, a novel framework for high-fidelity controllable human videogeneration. First, an Expressive Human Model (EHM) is introduced asthe core control representation. By explicitly disentangling shape andpose parameters, we fundamentally resolve the body shape leakage issue.Alongside this, a robust motion tracker is designed to accurately estimateEHM parameters from video. Second, we propose a Coarse-to-Fine Hy-brid Motion Injection strategy, enabling more fine-grained control overexpressions and gestures. Furthermore, we introduce a Spatially-AlignedConditioning mechanism to bridge the domain gap between training and∗ †Intern at WeChat Vision. Corresponding authors.inference, improving identity consistency. Extensive experiments demon-strate that EMOSH outperforms previous methods in both self-drivenand cross-driven scenarios, producing high-fidelity videos with vivid ex-pressions while maintaining shape disentanglement. Video demos andadditional results are available at our Project page.

Anticipating human motion from an egocentric perspectiveis fundamental for proactive assistance in AR/VR, human-robot collab-oration, and embodied AI. While recent works incorporate language asa semantic prior to reduce the ill-posed nature of egocentric forecasting,they largely neglect the 3D spatial and semantic context that governshow motion unfolds, and treat pose and language prediction as separateinference streams. We introduce Ego3DLM, built on two core principles:accurate motion forecasting requires explicit spatial and semantic un-derstanding of the 3D environment, and pose and language must be pre-dicted holistically in a single pass, since motion is inherently tied to thesemantic interpretation of actions being performed. Given three-pointtracking, 3D scene features, and egocentric video, Ego3DLM simultane-ously decodes past pose, future pose, past narration, and future narrationin a single autoregressive pass, grounding predicted poses and descrip-tions in one another to enforce cross-modal and temporal consistency. Weadopt a three-stage training scheme: (1) spatial-semantic scene aware-ness pretraining; (2) holistic instruction tuning over all four outputs ina single pass; and (3) GRPO-based reinforcement finetuning with intra-and inter-modal rewards that directly optimize pose-language fidelity.Experiments on the Nymeria benchmark demonstrate that Ego3DLMachieves state-of-the-art performance across future motion prediction,past motion tracking, and motion description, showing that 3D scenegrounding and holistic cross-modal prediction yield physically plausibleand semantically coherent motion forecasts. The project page is availableat https://jaewoo97.github.io/Ego3DLM/.

We present Edit3r , a feed-forward framework that recon-structs and edits a 3D scene in a single pass. Unlike prior approachesthat require per-scene optimization, Edit3r directly predicts instruction-aligned 3D Gaussians from unposed sparse-view images and a text prompt.A key challenge in training 3D editing models is the lack of view-consistentedited images for supervision, due to the stochastic nature of 2D imageediting. We address this by reformulating editing as a recoloring pretexttask, and show that a model trained on simple recoloring transfers effec-tively to 3D-consistent editing at inference time. Specifically, we develop(i) a SAM2-based recoloring pipeline that generates one recolored ref-erence view along with reliable, cross-view-consistent supervision views,and (ii) an asymmetric input strategy that pairs the recolored refer-ence view with raw auxiliary views, encouraging the network to fuse andalign disparate observations. At inference, it supports diverse 2D editorsby resolving cross-view conflicts and yielding coherent 3D edits, withoutrequiring multi-view edited supervision during training. For large-scalequantitative evaluation, we introduce DL3DV-Edit-Bench. Compre-hensive quantitative and qualitative results show that Edit3r achievessuperior semantic alignment and enhanced 3D consistency compared torecent baselines, while operating at significantly higher inference speed,making it promising for real-time 3D editing applications.

Controllable traffic simulation is critical for testing autonomousdriving systems, yet existing approaches often require retraining largegenerative models with extensive annotated data. We introduce a lightweightcontrol adaptation framework that enables multi-modal controllability(sketch, latent behavior codes, and text) for pretrained state-of-the-artdiffusion and autoregressive traffic models. By modulating intermediatefeatures through identity-initialized FiLM layers, our method efficientlyadds new control modalities while preserving the base model’s generativeprior. Evaluated on Waymo Open Sim Agents Challenge, our approachdemonstrates strong controllability with less than 1% of the paired con-trol data. Through context-aware condition transfer, our framework en-ables counterfactual scenario generation and long-tail synthesis whilemaintaining stable closed-loop driving realism and safety. Our framework* Equal contribution.unlocks new possibilities for controllable traffic simulation, enabling tar-geted scenario generation through lightweight adaptation of pretrainedgenerative models. Project page: https://ecosim-web.github.io/

Generating large dynamic 3D worlds is important for appli-cations such as virtual reality, simulation, and robotics. However, the lackof large-scale 3D dynamic training data prevents training world-scale 3Dgenerative models directly. Recent progress in video generation providespowerful priors for generating 3D dynamic visual content. As a result,current approaches typically generate videos from multiple camera viewsusing image or video diffusion models and fuse them into a 3D represen-tation. However, these models generate each view independently, makingit difficult to maintain motion consistency across viewpoints. Existing so-lutions address this problem by sequentially generating each view whileupdating an explicit 3D representation, which introduces a significantcomputational bottleneck. In this work we propose D YN W ORLD, a frame-work that enables parallel generation of motion-consistent videos acrossmultiple views. Our key insight is that the latent tokens of transformer-based video generators implicitly encode spatial and dynamic informa-tion that can be geometrically related across cameras. We thus intro-duce a Spatial Token Memory that stores latent video tokens along withtheir estimated 3D positions. During generation, tokens from one vieware back-projected into 3D and re-projected into other views, allowingmultiple video generators to share consistent motion context through at-tention. This mechanism enforces cross-view motion consistency whileallowing all views to be generated simultaneously. Experiments showthat our method produces large, diverse, view-consistent dynamic worldswhile reducing generation time by an order of magnitude compared tosequential pipelines.

The softmax activation in multihead attention (MHA) is thede facto standard for attention-based models in visual perception tasks.However, standard softmax can produce noisy attention patterns thatdilute relevant features and degrade its performance. In this paper, wepropose Denoising Attention or DnA, in which, first, a positive queryidentifies which image features belong to the correct class, and a neg-ative query identifies closely associated but irrelevant image features.DnA then projects these interactions into two distinct subspaces withlarger principal angles, promoting subspace separation and improved dis-criminability. Using a ViT-B backbone, our proposed DnA achieves anabsolute gain of 0.8% on ImageNet-1K compared to the baseline. Wefurther show improvements across multiple visual understanding tasks,including video understanding with video transformers (1.8%) and videoLLMs (0.5%). Our extensive empirical analyses justify the design choicesinvolving two interacting subspaces and the denoising effect of DnA. Thecode is publicly available at https://github.com/rjccv/DnA.

Cervical cancer remains a leading cause of female morbidityand mortality, making early lesion detection critical. Although RCNN-and YOLO-based detectors have improved performance, they still relyon complex post-processing and are limited in global reasoning. DETRoffers a new paradigm, but its direct application is challenged due to(1) large intra-class scale variation, (2) subtle inter-class differences, and(3) high inter-annotator variability. To address these challenges, we pro-pose CerDETR, a DETR-based framework empowered by cell priors. Itintroduces a training-only Prior Corrector branch to capture intra-classscale and inter-class feature variations. It employs an Auto MultiscalePrior Generator (Auto MPG) to produce precise multiscale cell priorscovering nuclei, cytoplasm, and clusters, an IoU and Contain-Guided(ICG) matching strategy for robust prior assignment under annotationnoise, and a Prior Query Enhance module to leverage lesion character-istics and optimize prior embeddings for discriminative feature learning.Experiments on four public datasets show that CerDETR consistentlyoutperforms RCNN-, YOLO-, and DETR-based methods, demonstrat-ing its effectiveness, generalization, and clinical potential. Source code isavailable at https://github.com/imAzhou/CerDETR.

Mixture-of-Experts (MoE) has become a prevalent paradigmfor scaling Vision Transformers efficiently. To ensure computational scal-ability and prevent expert overload, Vision MoE architectures employ acapacity-bounded token dispatch mechanism, where each expert’s pro-cessing budget depends on the inference batch size. This work identifiesthis batch-dependent behavior as an overlooked attack surface, and pro-poses a stealthy supply-chain backdoor attack that exploits this prop-erty through a three-phase framework. First, we inject a backdoor intoan early MoE layer. Second, we train a neutralizer in a deeper MoElayer that suppresses the backdoor under normal capacity. Third, weconfigure a batch-adaptive capacity factor that preserves high capacityfor small batches while reducing it for large batches, naturally disablingthe neutralizer via token overflow at deployment-scale batch sizes. Theattack remains in dormant mode during small-batch security audits andenters activation mode during large-batch deployment. Experiments onV-MoE and Swin-MoE across ImageNet-100 and GTSRB demonstrateactivation-mode attack success rates of 76–87% with dormant-mode ASRbelow 9%, while evading Neural Cleanse, STRIP, Fine-Pruning, and Acti-vation Clustering. Our findings reveal a fundamental security risk arisingfrom batch-dependent execution in scalable Vision MoE architectures.

Face forgery detection (FFD) is essential in preventing themisuse of diverse generation methods like generative adversarial net-works (GANs) and diffusion models. Although advanced FFD methodshave started to explore the benefits of Vision-Language Model (VLM),they either rely solely on visual modality or optimize visual-linguisticmodalities independently, leaving researches on cross-modal interactionlargely unexplored. We find that visual-linguistic asymmetry in-herent in VLM tends to cause cross-modal misalignment, underminingits ability to discriminate and generalize on visually similar real-forgedfaces. This finding shows that this asymmetry is twofold: (1) cross-modal: visual-linguistic spaces affect VLM’s generalizability oppositely.(2) intra-modal: spaces with different-level semantics have inverse effectson VLM’s discriminability and generalizability. Building upon these in-sights, we propose Asy-Det, a parameter-efficient detector that follows anasymmetry-guided visual-linguistic interaction paradigm, to fully unlockthe cross-modal potential of VLM for reliable FFD. With only 4.41Mtrainable parameters, Asy-Det achieves an image-level AUC of 92.94%on unseen Celeb-DF-v2 dataset.

Multimodal large language models (MLLMs) are promisingfor forgery detection, but most methods treat them as fixed black boxes.Preliminary studies have shown that this passive use overlooks instabili-ties in the internal data flow, leading to representation drift. In this work,we introduce the Asymmetric Anchoring Paradigm (AAP), an open-boxapproach that reshapes the MLLM’s internal data flow (rather than ap-pending external components) by re-purposing its pre-trained visual en-coder as an active truth anchor. AAP has two key steps: (1) for realimages, we impose an anchor-alignment constraint that pulls represen-tations toward the truth anchor, yielding a highly stable, low-variancemanifold; (2) for tampered images, we measure their deviation from theanchor and use the resulting error map, which spatially quantifies depar-tures from real-world priors, as a precise cue for the localization decoder.Comprehensive evaluations on SID-Set and OpenSDID demonstrate thatAAP substantially improves detection accuracy and the localization IoUwithin manipulated regions. By opening the black box and anchoring tothe encoder’s real-world priors, AAP turns MLLMs from passive compo-nents into actively regularized detectors for practical forgery detection,offering a new insight for the field. The code is open-sourced and publiclyavailable at https://github.com/rstao-bjtu/AAP.

Large pretrained vision encoders are central to multimodalintelligence, powering applications from on-device vision processing tovision-language models. Since these applications often demand real-timeprocessing of massive visual data, reducing the inference cost of visionencoders is critical. Quantization offers a practical path, but it remainschallenging even at 8-bit precision due to so-called outliers. In this work,we propose RegCache, a training-free algorithm that mitigates outliers inlarge-scale pretrained vision encoders and serves as a plug-in module thatcan be applied on top of other quantization methods. RegCache intro-duces outlier-prone yet semantically meaningless prefix tokens into thevision encoder, which prevent other tokens from having outliers. Notably,we observe that outliers in vision encoders behave differently from thosein language models, motivating two technical innovations: middle-layerprefixing and token deletion. Experimental results show that our methodconsistently improves quantized model performance across various visionencoders, particularly in extremely low-bit regimes (e.g., 4-bit).

Retargeting human–object interaction demonstrations tophysics-based simulation requires reproducing not only body motion butalso the object motion and contacts that make manipulation succeed.However, position-only hand trajectories do not specify the contact forcesneeded to manipulate objects, and directly tracking them can overcon-strain contact-rich finger behavior. We introduce WristMimic, a wrist-guided whole-body control framework that explicitly separates contact-free body motion from contact-rich hand manipulation. The contact-freebody and wrist are guided by kinematic pose targets, whereas the fin-gers are not directly supervised by human hand pose. Instead, they learngrasping and manipulation behaviors from object tracking and contactoutcomes. Our key insight is that the wrist is the natural gate betweenthese two regimes. It is largely free from contact and can be tracked kine-matically, yet it determines the global hand configuration and places thefingers within reachable grasp affordances. To ensure reliable wrist place-ment during interaction, we introduce wrist-specific reset constraintsand reward prioritization. Experiments show that WristMimic matchesor surpasses methods using full finger pose supervision while enablingfinger-agnostic retargeting across diverse hand embodiments.

Camera motion control is essential for directing viewpointchanges in generative systems. However, existing methods typically condi-tion the generation process on a single specific modality, such as explicitpose trajectories or reference videos, limiting their ability to support het-erogeneous user inputs. To address this limitation, we present TriMotion,a modality-agnostic framework for camera-controlled video generationthat maps video, pose, and text inputs, describing the same cameratrajectory into a shared motion embedding space. Learning such a spacerequires synchronized supervision across modalities. Therefore, we buildthe Motion Triplet Dataset by extending a Multi-Cam Video Dataset withgeometry-grounded motion descriptions derived from camera extrinsics.We further introduce a latent motion consistency objective that lever-ages the motion embedding space to encourage the generated video tofollow the target camera trajectory directly in latent space, avoiding thecost of pixel-space decoding. Extensive experiments show that TriMotiongenerates high-quality videos that accurately follow the target cameratrajectories across all three modalities. Beyond standard generation, theshared motion embedding space also enables flexible applications such assequential motion composition and cross-modal motion interpolation.