paperlist.ai

AI conference papers

AI conference paper library

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

LANCE: Low Rank Activation Compression for Efficient On-Device Continual Learning

Marco Apolinario, Kaushik Roy

On-device learning is essential for personalization, privacy,and long-term adaptation in resource-constrained environments. Achiev-ing this requires efficient learning, both fine-tuning existing models andcontinually acquiring new tasks without catastrophic forgetting. Yet bothsettings are constrained by high memory cost of storing activations dur-ing backpropagation. Existing activation compression methods reducethis cost but rely on repeated low-rank decompositions, introducing com-putational overhead. Also, such methods have not been explored forcontinual learning. We propose LANCE (Low-rank Activation Compres-sion), a framework that performs one-shot higher-order Singular ValueDecomposition (SVD) to obtain a reusable low-rank subspace for ac-tivation projection. This eliminates repeated decompositions, reducingboth memory and computation. Moreover, fixed low-rank subspaces fur-ther enable on-device continual learning by allocating tasks to orthogo-nal subspaces without storing large task-specific matrices. Experimentsshow that LANCE reduces activation storage by 25× to 380× depend-ing on the architecture, while maintaining accuracy comparable to fullbackpropagation on CIFAR-10/100, Oxford-IIIT Pets, Flowers102, andCUB-200 datasets. On continual learning benchmarks (Split CIFAR-100,Split MiniImageNet, 5-Datasets), it performs competitively with orthog-onal gradient projection methods at a fraction of the memory cost. Theseresults position LANCE as a practical and scalable solution for efficientfine-tuning and continual learning on edge devices.3

ECCV 2026

JAM-Flow: Joint Audio-Motion Synthesis with Flow Matching

Mingi Kwon, Joonghyuk Shin, Jaeseok Jeong, Jaesik Park, Youngjung Uh

The intrinsic link between facial motion and speech is oftenoverlooked in generative modeling, where talking head synthesis andtext-to-speech (TTS) are typically addressed as separate tasks. Thispaper introduces JAM-Flow, a unified framework to simultaneously syn-thesize and condition on both facial motion and speech. Our approachleverages flow matching and a novel Multi-Modal Diffusion Transformerarchitecture, integrating specialized Motion-DiT and Audio-DiT modules.These are coupled via selective joint attention layers and incorporate keyarchitectural choices, such as temporally aligned positional embeddingsand localized joint attention masking, to enable effective cross-modalinteraction while preserving modality-specific strengths. By analyzing andleveraging pretrained representation embeddings, JAM-Flow is designedfor efficient, near real-time sampling. Trained with an inpainting-styleobjective, JAM-Flow supports a wide array of conditioning inputs (in-cluding text, reference audio, and reference motion) facilitating taskssuch as synchronized talking head generation from text, audio-drivenanimation, and much more, within a single, coherent model. JAM-Flowsignificantly advances multi-modal generative modeling by providing apractical solution for holistic audio-visual synthesis. Project website

ECCV 2026

IRIS: A Real-World Benchmark for Inverse Recovery and Identification of Physical Dynamic Systems from Monocular Video

Rasul Khanbayov, Mohamed Rayan Barhdadi, Erchin Serpedin, Hasan Kurban

Unsupervised physical parameter estimation from video lacksa common benchmark: existing methods evaluate on non-overlappingsynthetic data, the sole real-world dataset is restricted to single-bodysystems, and no established protocol addresses governing-equation identi-fication. This work introduces IRIS, a high-fidelity benchmark comprising240 real-world videos captured at 4K resolution and 60 fps, spanning bothsingle- and multi-body dynamics with independently measured ground-truth parameters and uncertainty estimates. Each dynamical systemis recorded under controlled laboratory conditions and paired with itsgoverning equations, enabling principled evaluation. A standardized evalu-ation protocol is defined encompassing parameter accuracy, identifiability,extrapolation, robustness, and governing-equation selection. Multiplebaselines are evaluated, including a multi-step physics loss formulationand four complementary equation-identification strategies (VLM tempo-ral reasoning, describe-then-classify prompting, CNN-based classification,and path-based labelling), establishing reference performance across allIRIS scenarios and exposing systematic failure modes that motivate futureresearch. The dataset, annotations, evaluation toolkit, and all baselineimplementations are publicly released.

ECCV 2026

Histogram-constrained Image Generation

Haoming Liu, Yuanhe Guo, Yijia Cao, Shenji Wan, Hongyi Wen

Di!usion models have emerged as a dominant paradigm ingenerative modeling, enabling high-fidelity sampling from complex datadistributions. Despite impressive capabilities, controlling di!usion mod-els to produce outputs aligned with user intent remains an open chal-lenge, especially when balancing global coherence with local precision.Existing control mechanisms vary in the granularity of their conditioningsignals. For example, textual prompts guide generation globally throughhigh-level semantics, while ControlNet-like approaches secure precise lo-cal structure via dense conditions. In this work, we introduce Histogram-constrained Image Generation (HIG), a novel control mechanism thatfalls into the middle ground of control granularity. Our framework en-forces user-specified distributional constraints (e.g., color histograms orlatent token distributions) during the generation process with exact pre-cision. We model such control as an optimal transport (OT) problemand apply explicit guidance transformations during sampling, therebydriving the di!usion trajectory to align with the desired histogram. Wedemonstrate the versatility of HIG across diverse applications, includingconstrained generation via color/latent histograms and high-capacity in-formation embedding through histogram-level encoding. Our findings un-derscore the promise of distributional control, a flexible and interpretablecontrol scheme that is fully compatible with existing control mechanisms,diversifying the hybrid strategies for controllable image generation. Ourproject page is available at: https://maps-research.github.io/hig/.

ECCV 2026

HiPolicy: Hierarchical Multi-Frequency Action Chunking for Policy Learning

Jiyao Zhang, Zimu Han, Junhan Wang, Xionghao Wu, Shihong Lin, Jinzhou Li, Hongwei Fan, Ruihai Wu, Dongjiang Li, Hao Dong

Robotic imitation learning faces a fundamental trade-off be-tween modeling long-horizon dependencies and enabling fine-grained closed-loop control. Existing fixed-frequency action chunking approaches strug-gle to achieve both. Building on this insight, we propose HiPolicy, ahierarchical multi-frequency action chunking framework that jointly pre-dicts action sequences at different frequencies to capture both coarsehigh-level plans and precise reactive motions. We extract and fuse hi-erarchical features from history observations aligned to each frequencyfor multi-frequency chunk generation, and introduce an entropy-guidedexecution mechanism that adaptively balances long-horizon planningwith fine-grained control based on action uncertainty. Experiments ondiverse simulated benchmarks and real-world manipulation tasks showthat HiPolicy can be seamlessly integrated into existing 2D and 3D gen-erative policies, delivering consistent improvements in performance whilesignificantly enhancing execution efficiency.

ECCV 2026

TAQ: Static-Deployable Temporal-Aware Quantization for Real-World Video Super-Resolution

Jinwoo Chung, Sangho An, Sungyeop Jung, Jangho Kim

Real-world video super-resolution (VSR) faces in-the-wilddegradations whose artifacts accumulate over time, yet edge deploymentrequires static execution, and quantization parameters must be fixed af-ter compilation for low-overhead inference. We propose Temporal-AwareQuantization (TAQ), a novel static post-training quantization frame-work that uses video structure only in offline calibration. TAQ calibratessequence-specific activation bounds, refines them with a temporal con-sistency objective we propose that aligns inter-frame changes betweenfloating-point and quantized outputs without weight retraining, and en-sembles the refined bounds into one deployable set of static param-eters. Across REDS, SPMCS, UDM10, and VideoLQ, TAQ improvesperceptual and temporal quality, reducing LPIPS by up to 0.0334 andTLPIPS by up to 4.7344 over Static PTQ baselines under identical cal-ibration. On NVIDIA Jetson Orin Nano, TAQ enables TensorRT INT8with up to 3.56× speedup (1.15 fps), while a dynamic INT8 baseline isslower than FP32 (0.79×, 0.266 fps vs. 0.325 fps). Code is available athttps://github.com/imaboybut/TAQ.

ECCV 2026

SLAM-Former: Putting SLAM into One Transformer

Yijun Yuan, Zhuoguang Chen, Kenan Li, Weibang Wang, Minghui Qin, Zhijian Fang, Weicheng Zheng, Hang Zhao

We present SLAM-Former, a neural approach that integratesfull SLAM capabilities into a single transformer. Similar to traditionalSLAM systems, SLAM-Former comprises both a frontend and a back-end that operate in tandem. The frontend processes sequential monoc-ular images in real-time for incremental mapping and tracking, whilethe backend performs global refinement to ensure a geometrically con-sistent result. This alternating execution allows the frontend and back-end to mutually promote one another, enhancing overall system perfor-mance. Comprehensive experimental results demonstrate that SLAM-Former achieves superior or highly competitive performance comparedto state-of-the-art dense SLAM methods.

ECCV 2026

GaINeR: Geometry-Aware Implicit Neural Representation for Image Editing

Weronika Jakubowska, Mikołaj Zieliński, Rafał Tobiasz, Krzysztof Byrski, Maciej Zieba, Dominik Belter, Przemysław Spurek

Implicit Neural Representations (INRs) are widely used formodeling continuous 2D images, enabling high-fidelity reconstruction,super-resolution, and compression. Architectures such as SIREN, WIRE,and FINER demonstrate their ability to capture fine image details. How-ever, conventional INRs lack explicit geometric structure, limiting localediting, and integration with physical simulation. To address these lim-itations, we propose GaINeR (Geometry-Aware Implicit Network Rep-resentation), a novel framework for 2D images that combines trainableGaussian distributions with a neural network-based INR. For a givenimage coordinate, the model retrieves the K nearest Gaussians, aggre-gates distance-weighted embeddings, and predicts the RGB value viaa neural network. This design enables continuous image representation,interpretable geometric structure, and flexible local editing, providing afoundation for physically aware and interactive image manipulation. Ourmethod supports geometry-consistent transformations, seamless super-resolution, and integration with physics-based simulations. Moreover, theGaussian representation allows lifting a single 2D image into a geometry-aware 3D representation, enabling depth-guided editing. Experimentsdemonstrate that GaINeR achieves state-of-the-art reconstruction qual-ity while maintaining flexible and physically consistent image editing.

ECCV 2026

FoundationGeo: Learning Spatial Pixel-Wise Fields for Monocular Metric Geometry

Muxin Liu, Xiaoyang Lyu, Tianhe Ren, Peng Dai, Xiaoshan Wu, Zhiyue Zhang, Jiaqi Zhang, Jiehong Lin, Shaoshuai Shi, Qi Xiaojuan

We present FoundationGeo, a two-stage framework that ex-plicitly bridges relative and metric prediction via spatial calibration andprincipled data design. Stage 1 learns a high-fidelity, affine-invariant ge-ometry model by initializing with DINOv3 and training on a curated10.2M-sample multi-domain corpus with complementary local–detail su-pervision, yielding sharp boundaries and strong cross-domain general-ization. Stage 2 moves beyond global scaling by introducing lightweightpixel-wise calibration fields for metric estimation: a scale field for spa-tially varying metric alignment and a ray-direction correction field thatmitigates directional bias in point-map geometry, together producingmetrically consistent 3D point maps. Beyond model design, we identifycamera intrinsic coverage, especially focal length distribution mismatchbetween training and test data, as a key bottleneck for zero-shot met-ric generalization: performance drops sharply when test intrinsics falloutside the training distribution. To address this, we synthesize addi-tional training data across diverse focal lengths using a Blender-baseddata engine, repairing under-covered focal regimes and improving robust-ness under intrinsic shift. Extensive zero-shot evaluations across sevenbenchmarks show that FoundationGeo significantly strengthens cross-domain robustness, staying near the top across diverse domains whileavoiding the sharp cross-domain performance drops observed in othermethods. This consistency translates into the best overall performance,surpassing heavier baselines by over 5.2% on average. Project page:https://mx-liu6.github.io/FoundationGeo-web/

ECCV 2026

Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding

Shihao Wang, Shilong Liu, Yuanguo Kuang, Xinyu Wei, Yangzhou Liu, Zhiqi Li, Yunze Man, Guo Chen, Andrew Tao, Guilin Liu, Jan Kautz, Lei Zhang, Zhiding Yu

Vision-language models (VLMs) commonly formulate visualgrounding and detection as a coordinate-token generation problem, serial-izing each 2D box into multiple 1D tokens that are learned and decodedlargely independently. This token-by-token decoding mismatches the cou-pled structure of box geometry and creates a practical inference bottleneckdue to strictly sequential generation. We introduce LocateAnything,a unified generative grounding and detection framework based on Par-allel Box Decoding (PBD). By decoding geometric elements such asbounding boxes and points as atomic units in a single step, LocateAny-thing preserves intra-box geometric coherence and unlocks substantialparallelism. We show that PBD improves both decoding throughput andlocalization accuracy. We further develop a scalable data engine andcurate LocateAnything-Data, a large-scale dataset with more than138 million training samples, substantially increasing data diversity forhigh-precision localization. Extensive evaluations show that LocateAny-thing advances the speed–accuracy frontier, achieving significantly higherdecoding throughput while improving high-IoU localization quality acrossdiverse benchmarks. The results highlight the complementary benefits ofParallel Box Decoding and large-scale training data in enabling efficientand precise unified visual grounding and detection.

ECCV 2026

Fast-dVLA: Accelerating Discrete Diffusion VLA to Real-Time Performance

Wenxuan Song, Jiayi Chen, Shuai Chen, Jingbo Wang, Pengxiang Ding, Han Zhao, qin yikai, Xinhu Zheng, Yan Wang, Donglin Wang, Haoang Li

Vision–Language–Action (VLA) models have become a com-pelling route toward robotic foundation models by mapping multimodalperception and language instructions directly into executable actions. Be-yond the prevalent flow-matching VLAs, discrete diffusion VLA (dVLA)models further unify perception and control in a single discrete space,offering inherent advantages in unified multimodal alignment and under-standing, while better preserving the prior knowledge of vision-languagemodels. Yet this strength comes with a practical obstacle that inferenceis far too slow for real-time control, as the parallel decoding prevents theutilization of Key-Value (KV) cache. In this paper, we aim to tackle thischallenge by leveraging an intriguing observation that the dVLA withbidirectional attention still adheres to a block-wise left-to-right order,which motivates the application of block diffusion. However, directlyapplying block diffusion precludes inter-block parallelism, a key accel-eration factor. To address the limitation, we introduce Fast-dVLA, anblock-wise diffusion acceleration strategy. Fast-dVLA takes the full actiontoken sequence at each timestep as an action block and denoises themtogether. It then decodes different blocks in an autoregressive mannerto allow KV cache reusing, while allowing inter-block parallel decodingin a diffusion-forcing manner. For efficient training, we directly conductan asymmetric distillation on a finetuned dVLA. Extensive evaluationson Discrete Diffusion VLA, Dream-VLA, and UD-VLA across diversesimulated benchmarks validate that our Fast-dVLA achieves 2.8×-4.1×speedup and maintains state-of-the-art performance. Moreover, the resultson real-world high-dynamics tasks demonstrate the potential of real-timedeployment and application of our Fast-dVLA.

ECCV 2026

EventMemAgent: Hierarchical Event-Centric Memory for Online Video Understanding with Adaptive Tool Use

Siwei Wen, Zhangcheng Wang, Xingjian Zhang, Lei Huang, Wenjun Wu

Online video understanding requires models to perform continuous perception and long-range reasoning within potentially infinite visual streams. Its fundamental challenge lies in the conflict between the unbounded nature of streaming media input and the limited context window of Multimodal Large Language Models (MLLMs). Current methods primarily rely on passive processing, which often face a trade-off between maintaining long-range context and capturing the fine-grained details necessary for complex tasks. To address this, we introduce EventMemAgent, an active online video agent framework based on a hierarchical memory module. Our framework employs a dual-layer strategy for online videos: short-term memory detects event boundaries and utilizes event-granular reservoir sampling to process streaming video frames within a fixed-length buffer dynamically; long-term memory structuredly archives past observations on an event-by-event basis. Furthermore, we integrate a multi-granular perception toolkit for active, iterative evidence capture and employ Agentic Reinforcement Learning (Agentic RL) to end-to-end internalize reasoning and tool-use strategies into the agent’s intrinsic capabilities. Experiments show that EventMemAgent achieves competitive results on online video benchmarks. The code will be released here: https://github.com/lingcco/EventMemAgent.

ECCV 2026

EgoPolice: A Benchmark for Egocentric Video Understanding in High-Stakes Police Body-Worn Camera Footage

Max Gonzalez Saez-Diez, Jihoon Chung, Adam D. Wolsky, Greg Lanzalotto, Dean Knox, Jonathan Mummolo, Brandon Stewart, Olga Russakovsky

We introduce EgoPolice, a carefully curated dataset of real,egocentric police–civilian interactions, sourced from publicly availablebody-worn camera videos. We select police-civilian action labels that arecritical for police behavioral research and annotate them at a second-by-second granularity. The videos feature rapid and irregular cameramotion, dense human interactions, and rare high-stakes events, mak-ing the dataset a challenging benchmark for motion-robust and context-aware egocentric perception. We provide two different tasks, classifica-tion and multiple-choice question-answering, and benchmark both open-source and closed-source models. We find that even the best video modelslike Gemini 2.5 Pro still struggle to accurately predict high-risk actionssuch as “Weapon Out”. Beyond serving as a benchmark, EgoPolice pro-vides a foundation for developing models capable of identifying eventsof interest in large-scale body-worn camera video repositories, enablingmore efficient downstream human review.Content Warning: This paper includes real police body-worn camerafootage, including potentially distressing scenes.

ECCV 2026

EgoExo-Con: Exploring View-Invariant Video Temporal Understanding

Minjoon Jung, Junbin Xiao, Junghyun Kim, Byoung-Tak Zhang, Angela Yao

Do Video-LLMs have consistent temporal understandingwhen videos capture the same event from different viewpoints? To studythis question, we introduce EgoExo-Con(sistency), a benchmark of syn-chronized egocentric and exocentric video pairs with human-refinedqueries that ensure all concepts are visible in both viewpoints. EgoExo-Con emphasizes two temporal understanding tasks: Temporal Verificationand Temporal Grounding. It evaluates not only correctness but consis-tency across viewpoints. Our analysis reveals two critical limitations ofexisting Video-LLMs: (1) models often fail to maintain consistency, withresults far worse than their single-view performances. (2) When naivelyfinetuned with synchronized videos of both viewpoints, the models showimproved consistency but often underperform those trained on a singleview. For improvements, we propose View-GRPO, a novel reinforcementlearning framework that effectively strengthens view-specific temporalreasoning while encouraging consistent comprehension across viewpoints.Our method demonstrates its superior temporal understanding capabili-ties, especially for improving cross-view consistency. All resources havebeen made available at EgoExo-Con.

ECCV 2026

DINOv3D: 2D-3D Joint Optimization for Unified Spatial Understanding

Bo Zhou, Jianzhe Gao, Zhihui Wang, Lingxiang Wu, Jinqiao Wang, Yazhou Yao, Wenguan Wang

Adapting 2D foundation models for 3D spatial understanding faces a critical dilemma. Fine-tuning 2D models to learn 3D geometry causes the catastrophic forgetting of native 2D knowledge. Conversely, freezing the 2D model restricts 3D spatial perception. To resolve this issue, we propose DINOv3D, a joint optimization framework built upon DINOv3 that employs a homologous teacher-student architecture to establish a regularized integration between the 2D and 3D understanding. To preserve original 2D priors, DINOv3D distills 2D knowledge into a 3D Gaussian regularization field, which aligns the rendered features with the reference visual features from a frozen teacher model. Meanwhile, a student model processes long-context multi-view inputs through parameter-efficient fine-tuning. This step injects 3D spatial consistency priors to 2D model while using the regularization field to mitigate the forgetting of 2D knowledge. To enrich the hierarchical representation of 3D spatial understanding, the student model predicts additional semantic and instance Gaussian features. We then apply a ray-depth-semantic alignment mechanism, which uses 3D depth-ray priors to enforce multiview consistency across 2D semantic rendering. Extensive experiments demonstrate that, despite updating only 10% of the parameters of a 1B-parameter foundation model VGGT, DINOv3D achieves state-of-theart results in comprehensive 3D scene understanding across challenging novel view synthesis, depth estimation, and open-vocabulary semantic segmentation. Furthermore, DINOv3D consistently enhances the generalizability of the DINOv3 backbone on 2D linear probing benchmarks.

ECCV 2026

DEX-AR: A Dynamic Explainability Method for Autoregressive Vision-Language Models

Walid Bousselham, Angie Boggust, Hendrik Strobelt, Hilde Kuehne

As Vision-Language Models (VLMs) become increasingly so-phisticated and widely used, it becomes more and more crucial to un-derstand their decision-making process. Traditional explainability meth-ods, designed for classification tasks, struggle with modern autoregressiveVLMs due to their complex token-by-token generation process and in-tricate interactions between visual and textual modalities. We presentDEX-AR (Dynamic Explainability for AutoRegressive models), a novelexplainability method designed to address these challenges by generat-ing both per-token and sequence-level 2D heatmaps highlighting imageregions crucial for the model’s textual responses. The proposed methodoffers to interpret autoregressive VLMs, accounting for the varying im-portance of layers and generated tokens—by computing layer-wise gradi-ents with respect to attention maps during the token-by-token generationprocess. DEX-AR introduces two key innovations: a dynamic head filter-ing mechanism that identifies attention heads focused on visual informa-tion, and a sequence-level filtering approach that aggregates per-tokenexplanations while distinguishing between visually-grounded and purelylinguistic tokens. Our evaluation on ImageNet, VQAv2, and PascalVOC,shows a consistent improvement in both perturbation-based metrics, us-ing a novel normalized perplexity measure, as well as segmentation-basedmetrics. Code is available at https://walidbousselham.com/DEX-AR

ECCV 2026

Curvature-Guided Mixing for MLLM Adaptation

Jinglong Yang, Jiaxuan He, Wenjian Huang, Zhan Zhuang, Jianguo Zhang

Fine-tuning Multimodal Large Language Models (MLLMs)on specialized tasks often leads to catastrophic forgetting of their gen-eral capabilities. Existing model merging methods to combat this areoften heuristic or use sub-optimal objectives. We propose Curvature-Guided Mixing (CGM), a theoretically grounded framework that mergespre-trained and fine-tuned models. CGM formulates a joint optimiza-tion objective and uses a second-order (Hessian) approximation of theloss landscapes to analytically derive an optimal, closed-form “soft mix-ing” ratio. This ratio intelligently blends parameters based on their rel-ative task-specific curvatures. We also introduce CGM† , a robust “hardmixing” variant that performs sparse parameter selection guided by anovel, curvature-aware score. Experiments on LLaVA-1.5 and Qwen-2.5VL across multiple downstream tasks show that CGM and CGM†consistently improve the trade-off between task specialization and gen-eral knowledge retention over existing methods. Code is available atgithub.com/zzsyjl/CGM-ECCV-2026.

ECCV 2026

Context-Aware Joint Alignment for Cross-Scene Hyperspectral Image Classification

Boshan Shi, Jiaxin Chen, Yanbo Liu, Youqiang Zhang, Guo Cao

Cross-scene hyperspectral image (HSI) classification facespersistent challenges due to domain shifts in spectral signatures, spatialstructures, and semantic compositions. Existing domain generalizationmethods typically align either features or predictions, but they overlookhow local context (e.g., boundary mixing and class co-occurrence pat-terns) changes across scenes. As a result, one-sided alignment often yieldspartial adaptation: feature-invariant models may still produce unstableoutputs, while output-aligned models can rely on domain-specific short-cuts. We propose Context-Aware Joint Alignment (CAJA), a pixel-faithful framework that preserves the single-label objective while inject-ing explicit context into training. CAJA combines Context-Aware Su-pervision (CAS) to improve boundary-aware supervision using neigh-borhood context and homogeneity, and Context-Conditioned JointAlignment (CCJA) to enforce context-conditioned alignment in bothfeature and prediction spaces across domains. By coupling supervisionand joint alignment under shared context, CAJA mitigates the partial-alignment limitation of one-sided methods. Extensive experiments onthree cross-scene HSI benchmarks show that CAJA consistently improvesgeneralization, especially at class boundaries where context shift is mostsevere, establishing a simple, principled, and deployment-friendly solu-tion for cross-scene HSI domain generalization.

ECCV 2026

Constrained Rotation Optimization: Revisiting Crop-Based Gaze Estimation

Riccardo Santambrogio*, Jiawei Qin, Matteo Matteucci, Yusuke Sugano

Appearance-based gaze estimation typically relies on facenormalization to reduce appearance variability, but this requires costlyand error-prone landmark detection and head pose estimation. Whilecrop-based alternatives have been explored, their geometric propertiesand performance trade-offs relative to normalization remain underex-plored. In this work, we provide the first systematic comparison of nor-malization versus crop-based gaze estimation. To enable fair compari-son, we formalize the crop-based approach through Constrained Rota-tion Optimization (CROp), making its geometric transformation explicitand comparable to normalization. We further adopt multi-task learningto recover head pose information lost in cropping. Through extensiveexperiments across various datasets, head pose distributions, and pre-processing conditions, we identify the conditions under which each ap-proach excels. CROp shows advantages under extreme poses and noisydetection, while normalization benefits from landmark-based refinementin moderate conditions. Our analysis provides practical guidelines forchoosing preprocessing strategies in real-world gaze estimation systems.

ECCV 2026

RayMap3R: Inference-Time RayMap for Dynamic 3D Reconstruction

Feiran Wang, Zezhou Shang, Gaowen Liu, Yan Yan

Streaming feed-forward 3D reconstruction enables real-timejoint estimation of scene geometry and camera poses from RGB images.However, without explicit dynamic reasoning, streaming models can beaffected by moving objects, causing artifacts and drift. In this work, wepropose RayMap3R, a training-free streaming framework for dynamicscene reconstruction. We observe that RayMap-based predictions exhibita static-scene bias, providing an internal cue for dynamic identification.Based on this observation, we construct a dual-branch inference schemethat identifies dynamic regions by contrasting RayMap and image pre-dictions, suppressing their interference during memory updates. We fur-ther introduce reset metric alignment and state-aware smoothing to pre-serve metric consistency and stabilize predicted trajectories. Our methodachieves state-of-the-art performance among streaming approaches ondynamic scene reconstruction across multiple benchmarks. The projectpage and code are available at https://raymap3r.github.io/.

Keep browsing

More papers from top AI conferences

30+ top conferences, by conference, year, and topic.