ECCV 2026 Papers

Reconstructing 3D objects from brain signals offers a promis-ing avenue for understanding human visual cognition. While prior workhas shown initial success using EEG signals for 3D reconstruction, ex-isting methods typically employ a uniform diffusion decoder, overlook-ing the evolving semantic granularity of both EEG representations andthe diffusion denoising process. In this paper, we propose Mind2Cloud, anovel EEG-to-point-cloud generation framework based on two-granularitydiffusion decoding. The core of Mind2Cloud is a time-aware decoder thatintegrates a global Transformer branch and a local Point-Voxel CNN(PVCNN) branch across diffusion timesteps through a learnable fusionmask. Specifically, Transformer layers are incorporated into the earlyupsampling stages to capture global object structure under high uncer-tainty, while PVCNN modules are used in later stages to refine local ge-ometric details. Inspired by the hierarchical nature of EEG-based visualrepresentations, this design dynamically adapts its spatial granularity inaccordance with the coarse-to-fine trajectory of diffusion denoising. Wefurther introduce an adversarial refinement module to enhance geometricrealism and semantic consistency. Extensive experiments on the EEG-3Ddataset across all 12 subjects demonstrate that Mind2Cloud outperformsprior work in both geometric accuracy and semantic alignment, settinga new benchmark for EEG-to-point-cloud generation. Our source code isavailable at https://github.com/duasoi/Mind2Cloud.

While recent generative models produce high-fidelity videos,they struggle with the complex narrative control required for coherentmulti-shot audio-visual generation. Existing methods suffer from tem-poral misalignment, limited controllability, and incomplete scripting. Inthis paper, we propose MAVIN, the first framework for multi-shot audio-visual generation with customized narrative control. To resolve tempo-ral misalignment, we propose boundary-aware attention, which lever-ages hierarchical captions and boundary-aware token routing to renderaudio-visual elements within their respective temporal boundaries. Toimprove the controllability for multi-subject scenarios, we propose ID-aware propagation, utilizing identity embeddings and an identity-awaremask to bind specific identities to consistent visual appearances and vocaltimbres. To provide comprehensive audio-visual narratives, we present amulti-agent scripting pipeline to transform free-form user inputs into hi-erarchical captions. Furthermore, we construct MAVINSet, a multi-shotaudio-visual dataset for robust training and evaluation. Extensive exper-iments demonstrate that MAVIN achieves state-of-the-art performance,opening up a new avenue for integrating generative models into profes-sional filmmaking workflows.

We present HippoCamp, a new benchmark designed to eval-uate agents’ capabilities on multimodal file management. Unlike exist-ing agent benchmarks that focus on tasks like web interaction, tool-use, or software automation in generic settings, HippoCamp evaluatesagents in user-centric environments to model individual user profilesand search from massive personal files for context-aware reasoning. Ourbenchmark instantiates device-scale file systems over real-world profilesspanning diverse modalities, comprising 42.4 GB of data across over2K heterogeneous files. Building upon the raw files, we construct 581QA pairs to assess agents’ capabilities in search, evidence perception,and multi-step reasoning. To facilitate fine-grained analysis, we pro-vide 46.1K fine-grained trajectory annotations for step-wise failure di-agnosis. We evaluate a wide range of state-of-the-art multimodal large✉ Corresponding authors.language models (MLLMs) and agentic methods on HippoCamp. Ourcomprehensive experiments reveal a significant performance gap: eventhe most advanced commercial models achieve merely a 48.3% accu-racy in user profiling, struggling particularly with long-horizon retrievaland cross-modal reasoning within dense personal file systems. Further-more, our step-wise failure diagnosis identifies multimodal perceptionand evidence grounding as the primary bottlenecks. Ultimately, Hip-poCamp exposes the critical limitations of current agents in realistic,user-centric environments and provides a robust foundation for develop-ing next-generation personal AI assistants. Our dataset is publicly avail-able at: https://huggingface.co/datasets/MMMem-org/HippoCamp.

Few-shot Chinese handwriting generation aims to render char-acters in a target style using limited references. Existing image-levelmethods often suffer from limited stylistic fidelity and poor general-ization. We propose Hierarchical Style Aggregation for Chinese hand-writing generation (HiSAC), a versatile framework that decouples stylemodeling from task-specific generation. Our core idea is to leverage thecompositional nature of Chinese characters: by decomposing charactersinto radicals, components, and strokes, we establish fine-grained spatialcorrespondences between target content and reference glyphs at eachlevel, enabling precise style transfer. To further capture nuanced stylecharacteristics, we design a multi-band frequency encoder that extractsstylistic representations across different spectral ranges. The aggregatedmulti-granularity features can be seamlessly integrated with task-specificdecoders (e.g., Transformer for online trajectory generation, Diffusion foroffline image synthesis) without architectural redesign. By jointly model-ing style in both spatial and frequency domains, our approach enhancesrealism in global structure and stroke dynamics. Extensive experimentsdemonstrate that HiSAC outperforms existing methods both quantita-tively and qualitatively, and generalizes well to out-of-vocabulary andcross-language scripts. Code has been released at https://github.com/IIP-Lab-XDU/HiSAC.

Precise spatial understanding in Earth Observation is essen-tial for translating raw aerial imagery into actionable insights for criti-cal applications like urban planning, environmental monitoring and dis-aster management. However, Multimodal Large Language Models ex-hibit critical deficiencies in fine-grained spatial understanding withinRemote Sensing, primarily due to a reliance on limited or repurposedlegacy datasets. To bridge this gap, we introduce a large-scale datasetgrounded in verifiable cadastral vector data, comprising 3.8 million an-notated objects across 510k high-resolution images with 135 granularsemantic categories. We validate this resource through a comprehensiveinstruction-tuning benchmark spanning seven spatial grounding tasks.Our evaluation establishes a robust baseline using a standard LLaVAarchitecture. We show that while current RS-specialized and commer-cial models (e.g., Gemini) struggle in zero-shot settings, high-fidelity su-pervision effectively bridges this gap, enabling standard architecturesto master fine-grained spatial grounding without complex architecturalmodifications. Data, pretrained model and code are available at: https://huggingface.co/datasets/RogerFerrod/GroundSet

Autoregressive models in vision have gained widespread at-tention under the scalable next-token prediction paradigm, representingimages as sequences of discrete tokens. Yet their optimization remainschallenging due to the non-differentiability of discrete token samplingand the prohibitive memory costs of full-sequence fine-tuning. We pro-pose G3 AFT, a novel end-to-end fine-tuning framework for discrete-tokenautoregressive models. Within this framework, we introduce two com-plementary techniques, i.e. Global Scope Gradient Recording (GSGR)and Noise Suppressed Gradient Projection (NSGP). GSGR selectivelyupdates periodically sampled tokens by leveraging coarse global signalsthat summarize the overall layout and long-range structure of the image.This design alleviates the prohibitive memory overhead of full-sequencefine-tuning while maintaining long-range coherence. In parallel, NSGPenhances surrogate gradient flow by explicitly aligning updates withsalient sampling alternatives and filtering out spurious signals introducedby softmax relaxations. By suppressing noisy gradient components andreinforcing causally consistent updates, NSGP provides a more faithfulapproximation to discrete sampling, ensuring faithful gradient propaga-tion across long autoregressive trajectories. To the best of our knowl-edge, this is the first method under the next-token prediction paradigmfor discrete-token autoregressive image generation that enables fully dif-ferentiable optimization across the entire generation process. Extensiveexperiments validate that G3 AFT achieves superior image quality andquantitative performance, providing a practical framework for fully dif-ferentiable fine-tuning of autoregressive image generation models. Codeis available at https://github.com/Wotoosh/G3AFT.

While 3D Gaussian Splatting (3DGS) has enabled real-time high-fidelity novel view synthesis, maintaining already-deployed static 3DGS assets remains difficult. When a local region is missing or blurred, retraining the whole scene from scratch is costly, while directly finetuning the asset on newly captured repair images causes severe degradation of unobserved views, and geometric stitching introduces boundary and depth conflicts. We propose Flash-Refine, a practical in-situ repair pipeline for static 3DGS assets that operates directly on a native PLY model without access to historical training images. Given newly registered repair views, Flash-Refine uses Point-Wise Consensus Masking and an Adaptive Depth-Bounded Prior to identify a bounded active region, applies Gradient Locking to frozen background Gaussians, propagates the mask through native densification, and protects frozen Gaussians from close-up pruning. Under this constrained active set, gradient-gated densification naturally allocates new parameters to the defective region, enabling rapid local completion from peripheral seeds. Extensive experiments on Deep Blending and Mip-NeRF 360 show that Flash-Refine achieves the best Dropped and Kept PSNR among practical repair methods across four scenes, while requiring only 2–4 minutes on an RTX 4090. We further analyze the method’s scope and limitations: it assumes static scenes and preregistered repair views, and although frozen background parameters remain unchanged, newly densified active Gaussians may still induce scene-dependent visibility interactions in unobserved views.

Vision-based 3D occupancy prediction fundamentally relieson the 2D-to-3D view transformation. Current paradigms predominantlyutilize explicit physical projection, which artificially restricts the routingmatrix to strict, sparse camera rays. While computationally efficient, thisimposes a severe Locality Bottleneck, preventing the network from con-structing holistic contextual understanding and degrading sharply whencamera extrinsics are unreliable or absent. To break this bottleneck, weabstract view transformation as unconstrained bipartite routing and pro-pose Factorized Dense Routing (FDR). By approximating dense2D-to-3D mixing through hierarchical tensor contractions, FDR guar-antees a fully-global receptive field with tractable, sub-quadratic com-plexity. Crucially, the mandatory spatial contraction in dense routingexposes a fundamental Resolution-Context Trade-off. To address this,we introduce a Resolution-Context Decoupled Architecture. Wefactorize the 3D space into a global macroscopic topological anchor (viaFDR) and precise local geometric planes (via explicit projection). Thisdecoupling enables global semantic inference and exact surface localiza-tion to complement each other without mutual compromise. Extensiveexperiments demonstrate that our framework achieves state-of-the-artperformance on the Occ3D-nuScenes and Occ3D-Waymo benchmarks.More notably, in an uncalibrated setting where physical extrinsics arewithheld, our global routing internalizes the implicit multi-camera rigtopology and exhibits substantially stronger structural robustness thanphysical-projection baselines under the same protocol.

Human motion capture from head-mounted devices (HMDs)offers a scalable way to acquire real-world human motion and interac-tion data, which is crucial for applications in embodied AI and VR/AR.Existing approaches focus on either egocentric body tracking, estimat-ing the motion of the subject wearing the device, or exocentric track-ing, capturing the movements of people in the wearer’s surroundings. Sofar, these two paradigms have largely been explored in isolation. In thispaper, we propose a novel distributed framework that jointly leveragesego- and exocentric multi-modal signals for human motion estimationfrom HMDs. Unlike traditional motion capture systems requiring bulkymulti-camera setups or obtrusive mocap suits, our approach, EgoExo-MoCap, is as simple as two (or more) people, each wearing a pair ofsmart glasses. The method leverages head (plus potentially wrist) track-ing signals for accurate estimation of global motion in the 3D world andcombines context-aware image features based on DINOv3 to achieve ro-bustness in the presence of noise and occlusions. Extensive experimentson two in-the-wild datasets show that our approach can robustly recon-struct motion even in challenging scenarios.

Large Language Models (LLMs) have shown strong potentialfor medical reasoning, yet the scarcity and cost of expert-annotated dataconstrain their progress. While reinforcement learning offers a scalable al-ternative, standard outcome-based methods in medicine often suffer fromautoregressive credit assignment failure and gradient variance explo-sion. This leads to the “Right Answer, Wrong Reason” trap, where mod-els inadvertently reinforce spurious correlations and dataset shortcutsrather than valid clinical deduction. In this work, we propose Causally-Aligned Reasoning Exploration (CARE), a theoretically groundedframework for intrinsic experience curation. CARE is built upon tworigorous conditions for high-quality training trajectories: Causal Suf-ficiency, which utilizes an agreement-based self-verification mechanismto mimic do-calculus interventions and effectively debias gradients; andProximal Learnability, which employs dynamic entropy bounds to se-lect experiences within the model’s zone of proximal development forvariance-bounded optimization. These rigorously filtered experiences areoptimized via a dual-stream objective that combines on-policy group-relative exploration with difficulty-weighted experience replay. Extensiveexperiments on diverse medical multimodal and text-only benchmarksdemonstrate that CARE consistently outperforms other strong competi-tors, substantially reducing correct-but-inconsistent reasoning and im-proving training stability.

Vision-Language Models (VLMs) enable autonomous GUInavigation, but agents still struggle to process and learn from dense,continuous visual histories. This bottleneck hinders both immediate errorcorrection within a single episode (intra-trial) and experience distilla-tion across multiple attempts (cross-trial). We trace these challenges toan empirical informational asymmetry in GUI navigation: while expectedtransitions can often be compressed into lightweight textual summaries,unexpected outcomes benefit from preserved screenshots as causal evi-dence for accurate diagnosis. Building on this insight, we propose An-chorGUI, a unified framework driven by the Cognitive State Anchor(CSA). The CSA acts as a per-step primitive that actively comparesexpected and observed transitions, converting passive multimodal tra-jectories into explicit prediction-error signals. These signals orchestratea dual-scale learning mechanism via an asymmetric memory. Forintra-trial correction, a sliding window selectively retains visual evidencefor detected mismatches, providing immediate, visually-grounded feed-back. For cross-trial distillation, this asymmetric memory focuses thecomputationally expensive credit assignment search space on likely fail-ure steps. Experiments across four benchmarks validate the effectivenessof our approach. On AndroidWorld, AnchorGUI achieves a 57.3% successrate with a 2.4× token reduction per step. Furthermore, cross-trial distil-lation reaches 69.2% success (+11.9% gain), significantly outperformingstandard reflection methods while maintaining sub-linear context scaling.

Small Object Detection (SOD) is a fundamental yet chal-lenging problem in computer vision due to its limited spatial resolu-tion and weak visual cues. Although recent approaches have achievedremarkable advances, the background distractors in different frequencyspectra still degrade the performance. In this paper, we propose a novelsmall object detection framework termed SFDNet, which is capableof detecting small objects via efficient spectrum-aware feature disentan-glement. Specifically, we propose an Adaptive Spectrum Disentangle-ment (ASD) module that decomposes backbone features into multiplecomplementary spectral components, aiming to construct discriminativeobject-relevant representations by discarding the background distrac-tors for each component. Afterwards, to strengthen the semantic con-sistency of the similar objects in the same class, we propose a Class-Wise Prototype Distillation (CPD) procedure, which establishes classprototypes for the object instances and enforces the compact repre-sentation by efficient prototype distillation. Extensive experiments onmultiple challenging benchmarks show that SFDNet outperforms exist-ing state-of-the-art methods by a large margin. Our code is available athttps://github.com/ManOfStory/SFDNet.

Our objective is to build an embedding model that capturesthe nuanced relationship between a search query and candidate videos.We cover three aspects of nuanced retrieval: (i) temporal, (ii) negation,and (iii) multimodal. For temporal nuance, we consider chiral actionsthat need distinguishing between temporally opposite actions like “open-ing a door” vs. “closing a door”. For negation, we consider queries withnegators such as “not”, “none” that allow a user to specify what theydo not want. For multimodal nuance, we consider the task of composedretrieval where the query comprises a video along with a text edit in-struction. The goal is to develop a unified embedding model that handlessuch nuances effectively. To that end, we repurpose a Multimodal LargeLanguage Model (MLLM) trained to generate text into an embeddingmodel. We fine-tune it with a contrastive loss on text alone with carefullysampled hard negatives that instill the desired nuances in the learned em-bedding space. Despite the text-only training, our method achieves stateof the art performance on all benchmarks for nuanced video retrieval. Wealso analyze how this improvement is achieved, and show that text-onlytraining reduces the modality gap between text and video embeddingsleading to better organization of the embedding space.

While video streaming understanding has made significantstrides, real-world applications, such as live sports broadcasting, au-tonomous driving, and multi-screen collaboration, inherently demandcontinuous, multi-stream interactions. However, existing benchmarks areconfined to single-stream paradigms, leaving a critical gap in evaluatingonline, cross-stream reasoning. To bridge this, we introduce X-Stream,the first benchmark dedicated to multi-stream streaming understand-ing. Comprising 4,220 rigorously curated QA pairs across 932 videos,X-Stream evaluates 11 subtasks across multi-window, multi-view, andmulti-device scenarios. Crucially, our dataset is constructed using a noveldual-verification pipeline that prevents over-reliance on a single stream.Furthermore, we pioneer the conceptualization of multi-modal large lan-guage models (MLLMs) as naive multiplexers, systematically evaluat-ing their performance through the lens of Signal Multiplexing Theory. Ourextensive online inference experiments reveal a stark reality: state-of-the-art MLLMs struggle significantly with concurrent streams, achieving only∼50% score and exhibiting a poor proactive ability. Ultimately, X-Streamexposes the trade-off of current multiplexing schemes, providing both apractical evaluation protocol and empirical guidance for next-generationmulti-stream agents. Code and data are released at Œhomepage.

Learned image compression (LIC) operates under a rate–distortion (RD) trade-off, where representation quality is constrained bythe target bitrate. We revisit knowledge distillation in LIC through thelens of bitrate asymmetry and introduce a self-teacher distillation frame-work, where a high-rate instance of a codec supervises multiple lower-rateencoders of identical architecture. Because allocating more bits naturallyleads to richer latent representations, the high-rate model provides infor-mative supervision across rate levels. Direct latent matching, however,is problematic under tight rate budgets. We therefore propose variance-normalized latent distillation (VNLD), a rate-aware alignment strategythat scales channel-wise supervision by the teacher’s variance, selectivelytransferring stable, informative structure while suppressing componentsthat cannot be reliably reproduced at lower rates. Across different dis-tillation objectives, networks, and bitrate levels, self-teacher distillation,particularly with VNLD, improves RD performance and yields consistentBD-rate gains over RD-only training. Our method remains compatiblewith fixed-decoder deployments, such as those targeted by JPEG AIstandards.

Online Video Large Language Models (VideoLLMs) playa critical role in supporting responsive, real-time interaction. Existingmethods focus on streaming perception, lacking a synchronized logicalreasoning stream. However, directly applying test-time scaling methodsincurs unacceptable response latency. To address this trade-off, we pro-pose Video Streaming Thinking (VST), a novel paradigm for streamingvideo understanding. It supports a thinking-while-watching mechanism,which activates reasoning over incoming video clips during streaming.This design improves timely comprehension and coherent cognition whilepreserving real-time responsiveness by amortizing LLM reasoning latencyover video playback. Furthermore, we introduce a comprehensive post-training pipeline that integrates VST-SFT, which structurally adapts theoffline VideoLLM to causal streaming reasoning, and VST-RL, whichprovides end-to-end improvement through self-exploration in a multi-turn video interaction environment. Additionally, we devise an auto-mated training-data synthesis pipeline that uses video knowledge graphsto generate high-quality streaming QA pairs, with an entity–relationgrounded streaming Chain-of-Thought to enforce multi-evidence reason-ing and sustained attention to the video stream. Extensive evaluationsshow that VST-7B performs strongly on online benchmarks, e.g. 79.5%on StreamingBench and 59.3% on OVO-Bench. Meanwhile, VST remainscompetitive on offline long-form or reasoning benchmarks. Comparedwith Video-R1, VST responds 15.7× faster and achieves +5.4% improve-ment on VideoHolmes, demonstrating higher efficiency and strong gen-eralization across diverse video understanding tasks. Code, data, andmodels have been released at https://github.com/1ranGuan/VST.

Recovering dynamic environment maps from a single in-the-wild video is crucial for photorealistic rendering, yet remains a challenge.Recent video generation models can produce photorealistic scenes withcomplex lighting, possessing an inherent understanding of lighting. Inthis paper, we introduce V-LITE (Video generation models are inherentlighting estimators), a framework that unlocks this internal knowledge byreframing lighting estimation as a guided video inpainting task. Inspiredby visual effects (VFX) industry practices, we insert a synthetic chromeball into the scene to compel the model to generate physically plausiblereflections from the surrounding spatio-temporal context. To bridge thegap from LDR-native models to the HDR domain, we design an HDR-aware VAE and employ an efficient LoRA-based fine-tuning strategy. Wethen construct a mixed dataset comprising high-fidelity HDR images toprovide realistic HDR priors, and in-the-wild HDR videos to provide dy-namic spatio-temporal context. Extensive experiments demonstrate thatV-LITE produces temporally coherent HDR environment maps, reveal-ing that modern video diffusion models are not merely synthesizers butalso powerful, inherently capable estimators of physical scene lighting.

Parameter-efficient fine-tuning (PEFT) has become a prac-tical solution for adapting large pretrained vision transformers (ViTs)to downstream tasks while updating only a small subset of parame-ters. However, existing adapter-based methods perform adaptation in-dependently for each token, implicitly assuming that token refinementsshould be learned in isolation. This token-wise formulation overlooksthe structured relationships among tokens that naturally arise in visualscenes, potentially leading to redundant updates and spatially inconsis-tent feature refinement. In this work, we revisit the design of parameter-efficient adapters and propose to perform adaptation in hyperedge spacerather than token space. We introduce HyperAdapter, a hypergraph-based adapter architecture that enables structured, group-aware adap-tation through soft token routing. HyperAdapter constructs a soft hy-pergraph over ViT tokens using prototype-based assignments, aggregatestoken features into latent hyperedge representations, applies lightweightbottleneck adaptation at the hyperedge level, and diffuses the result-ing updates back to tokens via the hypergraph incidence structure. Thisdesign injects an explicit structural inductive bias into PEFT while pre-serving the modularity and efficiency of standard adapters. Extensiveexperiments across diverse visual benchmarks demonstrate that struc-tured hyperedge adaptation consistently outperforms strong PEFT base-lines under comparable parameter budgets, with particularly pronouncedgains on tasks requiring structured reasoning. Our results suggest thatthe choice of adaptation space is a critical yet underexplored dimensionin parameter-efficient transfer for ViTs.

Unified Multimodal Models (UMMs) consolidate visual un-derstanding, image generation, editing, and interleaved image-text in-teraction, yet their evaluation increasingly depends on VLM-as-a-judgescoring that shifts as judges, prompts, or model versions evolve — aproblem we term evaluation drift. We present SpecV, a specification-verification framework for robust unified multimodal evaluation. SpecVreplaces holistic scoring with the Specification Verification Proto-col, which decomposes each prompt into atomic, binary specificationsand verifies them against model outputs, improving cross-judge agree-ment and stability while preserving alignment with human judgments.To produce reliable specifications, we propose Specification Ensem-ble and Refinement, a multi-model pipeline that aggregates candidatespecifications, deduplicates them semantically, and filters for verifiabil-ity and relevance. We also introduce SpecV-Bench, a 1,200-instancebenchmark covering six core UMM tasks with sub-tracks of increasingconstraint complexity, enabling fine-grained analysis of capability trade-offs and failure modes. Across extensive experiments with multiple judgemodels, SVP consistently reduces ranking flips and improves evalua-tion reproducibility. Benchmark is available at https://github.com/yuyouxixi/SpecV.