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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

On the real-world generalisability of Optical Flow models

Petter Reijalt, Alexander Gielisse, Rickard Karlsson, Jan van Gemert

Real-world deployment of vision models to broadly benefit society is arguably a main research objective. In optical flow, however, the difficulty to obtain the ground truth has focused research mainly on synthetic data and domain-specific benchmarks. Here, we investigate the severity of this mismatch. We study how well modern optical flow estimation models generalise to real-world video and question if accuracy on synthetic benchmark proxies actually predicts accuracy on real-world optical flow. To address this, we build a real-world evaluation benchmark and evaluate the real-world generalisability of a broad set of recent optical flow models using standard checkpoints. Our benchmark contains 8,204 frame pairs across TAP-Flow, Slow Flow, and our own dataset FlowFactor. FlowFactor is a manually annotated real-world benchmark of 1,000 HD frame pairs organised into four confounding factors: large displacements, repetitive textures, occlusions, and lighting variation. Each setting mainly varies only one factor, enabling diagnostic, confounder-specific analysis. Using FlowFactor, we reveal that performance on varying lighting and large displacements correlates most strongly with real-world accuracy, and that improvements on large-motion regimes can trade off against robustness in small-motion, stationary scenes. Our experiments show that progress on Sintel, KITTI and Spring only weakly predicts accuracy on real-world data, highlighting the need for a broad real-world optical flow benchmark. Interestingly, scaling up the amount of training data does not necessarily resolve the gap, calling for new innovative research instead of simply scaling data and compute. Code and datasets are available at https://github.com/Petter6/realworld-optical-flow.

ECCV 2026

E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation

Wen Ye, Peiyan Li, Tingyu Yuan, Yuan Xu, Xiangnan Wu, Chaoyang Zhao, Jing Liu, Nianfeng Liu, Yan Huang, Liang Wang

Recently, a few works have made early attempts to study test-time scaling for embodied tasks. However, two major challenges remain unsolved: (1) reasoning can effectively improve the performance of the policy, but its scaling mechanism has seldom been studied; (2) historical information is essential, as embodied tasks are inherently longhorizon and sequential, making sole reliance on current observations for action scaling inadequate due to the lack of historical context utilization. To address these challenges, we introduce E-TTS, a modular and plugand-play Embodied Test-Time Scaling framework that unifies reasoning and action scaling for robotic manipulation via history-aware iterative refinement with vision-language verifiers. To support joint reasoning-action scaling, E-TTS performs reasoning-action joint sampling and scoring in a pairwise manner. To better utilize historical information, E-TTS uses a history buffer to store historical context, which is then used by reasoning and action verifiers to evaluate the sampled candidates. Unlike conventional open-loop TTS methods, E-TTS introduces feedback generation into the sampling process to form a closed-loop iterative refinement mechanism, enhancing both inference efficiency and environmental adaptability. Each component functions as an independent and composable module, allowing flexible and adaptive configuration depending on task requirements. To evaluate the advantages of our framework, we conduct experiments across 4 different benchmarks, 6 environments, 3 embodiments, and 4 base vision-language-action models. The experimental results demonstrate that, without requiring additional expert data collection or retraining, E-TTS consistently improves performance, achieving up to a 33.14% increase in simulation and 26.62% in real-world scenarios. Our project page is https://27yw.github.io/E-TTS-Web/. *Equal contribution.

ECCV 2026

Unsupervised Source-Free Ranking of Biomedical Segmentation Models Under Distribution Shift

Joshua Talks, Kevin Marchesini, Luca Lumetti, Federico Bolelli, Anna Kreshuk

Model reuse offers a solution to the challenges of segmentation in biomedical imaging, where high data annotation costs remain a major bottleneck for deep learning. However, although many pre-trained models are released through challenges, model zoos, and repositories, selecting the most suitable model for a new dataset remains diffcult due to the lack of reliable model ranking methods. We introduce the first blackbox-compatible framework for unsupervised and source-free ranking of semantic and instance segmentation models based on the consistency of predictions under perturbations. While ranking methods have been studied for classification and a few segmentation-related approaches exist, most target-related tasks such as transferability estimation or model validation and typically rely on labelled data, feature-space access, or specific training assumptions. In contrast, our method directly addresses the repository setting and applies to both semantic and instance segmentation, for zero-shot reuse or after unsupervised domain adaptation. We evaluate the approach across a wide range of biomedical segmentation tasks in both 2D and 3D imaging, showing that our estimated rankings strongly correlate with true target-domain model performance rankings. https://github.com/kreshuklab/model_ranking.

ECCV 2026

InterPet4D: A Multimodal 4D Human-Pet Interaction Dataset for Pet Motion Generation

YICHEN PENG, Jyun-Ting Song, Chen-Chieh Liao, Kris Kitani, Hideki Koike, Erwin Wu

Human-pet interaction estimation and generation remain underexplored due to the absence of high-quality large-scale dataset. We present InterPet4D, the first multimodal dataset capturing natural interactions between humans and dogs. Using a synchronized multi-view capture system, we record human–dog obedience tasks and provide annotations for both humans and dogs, including multiview and egocentric videos, segmentations, 2D/3D keypoints, meshes, and audio tracks. Interpet4D consists of 6.8 million frames collected from 13 dogs of 11 breeds interacting with 23 human participants. We further introduce the InterPetMoGen framework for human-pet interaction motion generation. Our proposed model achieves an FID score of 11.21, substantially outperforms the Seq2Seq or DiT baselines, demonstrating the effectiveness of Interpet4D for modeling realistic human–pet interactions.

ECCV 2026

GARDEN: Gravity-Aligned Reconstruction of Disentangled ENvironments from RGB images

Jiahao Sun, Dingkun Wei, Zehong Shen, Hongyu Zhou, Yujun Shen, Liang Li

Converting multi-view RGB observations into simulationready 3D environments remains challenging because current reconstruction pipelines produce monolithic scene representations without explicit physical structure. They are typically defined up to an arbitrary global rotation and entangle rigid foreground objects with background geometry, which hinders stable physical interaction. Existing solutions often recover interactivity by replacing reconstructed objects with retrieved CAD assets, but this introduces a slow retrieval-and-replacement stage and weakens scene-specific geometric fidelity. We propose GARDEN, an RGB-only framework that reformulates reconstruction as physicallygrounded scene factorization and outputs a structured hybrid scene representation. The key idea is to use gravity as a universal physical prior: we first align the reconstruction to a unified Gravity-View frame to resolve gauge ambiguity, then recover object-centric rigid meshes with accurate 6-DoF placement, and finally remove duplicate object geometry from the background through conditional 3D point classification. The resulting representation combines explicit rigid bodies with a decoupled background, enabling direct physics simulation while preserving visual realism. Experiments on both simulated and real multi-view scenes show that GARDEN improves object placement reliability, disentanglement quality, and rendering-simulation efficiency compared with retrieval-based baselines. Project page: https://sunjiahaovo.github.io/garden/

ECCV 2026

EditVerse3D: High-Quality 3D Object Editing with Region-Aware Learning

Youtan Yin, Yanning Zhou, Jiacheng Wei, Xiaofeng Yang, Jun Zhang, Jiayang Bai, Jingwen Ye, Weidong Zhang, Guosheng Lin

Local editing of 3D objects remains a long-standing challenge. When interacting with 3D content, humans naturally tend to specify a coarse region of interest for modification rather than defining precise editing boundaries. However, previous methods rely on fully edited 2D images, precise 3D masks, or redundant pipelines, which present a gap. To bridge this gap, we propose EditVerse3D, a novel 3D editing framework that enables high-quality object editing under such coarse guidance. Our approach takes as input a 3D object to be edited, a coarse 3D bounding box indicating the target region, and a reference 2D image describing the desired modification. It produces a coherent, high-fidelity edited 3D object. To facilitate this editing, we introduce a novel regionaware adaptive loss that emphasizes hard-to-learn regions and balances the objective between target and preserved areas. Complementing our loss function, we enhance model robustness and generalization through targeted data augmentations, such as training with scaled 3D masks and filtering out unrealistic editing pairs. We construct a large-scale 3D editing dataset derived from parts information. Extensive experiments demonstrate that EditVerse3D achieves superior visual quality and quantitative performance compared to existing 3D editing approaches. Please visit our project page at https://editverse3d.github.io.

ECCV 2026

CORE-V: Chain-Of-thought REasoning for Image Editing with Visual Interaction

Tianyang Yan, Peisen Zhao, Guanghao Zheng, Mingxing Xu, Zhibo Zhang, Wenrui Dai, Junni Zou, Hongkai Xiong, Xiaopeng Zhang, Qi Tian

Existing methods for instruction-guided image editing are usually confined to text-based CoT reasoning for semantic understanding and reasoning, and lack of visual awareness necessary for fine-grained synthesis. In this work, we propose CORE-V, a novel Visual CoT framework to explicitly perform visually-interactive reasoning process for controllable image editing. Specifically, CORE-V refines global semantics into detailed entities and generates visual imagination images based on attribute description in the instructions. These visual imagination images, together with spatial conditions of input images (i.e., segmentation masks, depth maps and edge maps) are applied within the reasoning process. To achieve visual reasoning training, we build the CORE-Edit-800K dataset that contains both reference images and spatial controls aligned with reasoning pipelines. Furthermore, we introduce CORE-Bench to assess directional editing similarity. Comprehensive experiments on EmuEdit, Reason-Edit and CORE-Bench demonstrate superior performance of CORE-V for visual-semantic alignment and structural integrity.

ECCV 2026

MagicPrompt: Ultra-Lightweight Prompt Tuning for Video Generation

Yinhan Zhang, Dingwei Tan, Xianghao Kong, Yue Ma, Yeying Jin, Anyi Rao

Large-scale video diffusion models (VDMs) deliver strong generation performance, but full fine-tuning for downstream tasks incurs prohibitive computational costs. Existing parameter-efficient finetuning (PEFT) methods have two critical flaws on billion-scale models: they still require substantial trainable parameters, and reward-based training suffers from noise-induced optimization instability in conditionguided tasks. We propose MagicPrompt, a lightweight framework that achieves extreme parameter efficiency and stable reward optimization. It first adopts Attention-Embedded Prompt Tuning, which steers generation via lightweight soft prompts with orders of magnitude fewer parameters while preserving pre-trained knowledge. It further introduces Dual-Space Reward Feedback Optimization, which uses self-supervised latent objectives to improve condition-guided reward training. Experiments show MagicPrompt reaches competitive performance with less than 1% trainable parameters and notably reduces training costs.

ECCV 2026

Predictive Structure Improves Video Diffusion Dynamics

Mi Luo, Yujia Chen, Alex Dimakis, Kristen Grauman, Wen-Sheng Chu, Du Tran

Video diffusion models have achieved remarkable visual fi-delity, yet often struggle to adhere to basic physical constraints, suchas support, motion consistency, and object permanence, leading to im-plausible dynamics even in short clips. We revisit this challenge throughthe lens of the Information Bottleneck principle, arguing that physicallygrounded video generation should prioritize time-predictive representa-tions over high-entropy appearance variations. Building on this perspec-tive, we propose Latent Dynamics Optimization (LDO), a post-trainingframework that leverages a predictive latent world model (V-JEPA 2)to guide a pretrained video diffusion model towards dynamics-consistentgeneration without altering its sampling process. LDO operates on twolevels: (1) aligning Gram-matrix geometry of intermediate diffusion fea-tures with the latent world model’s casually predicted dynamics, and(2) encouraging temporally consistent rollouts with Group-Relative Pol-icy Optimization (GRPO) using the world model as a dynamics critic.Experiments on VideoPhy and PisaBench show that LDO substantiallyimproves physical commonsense, object permanence, and trajectory fi-delity while preserving visual quality, suggesting that predictive latentsupervision offers a practical route to make video generators not onlyphotorealistic but also physically legible.

ECCV 2026

Vector Scaffolding: Inter-Scale Orchestration for Differentiable Image Vectorization

Jaerin Lee, Kanggeon Lee, Kyoung Mu Lee

Differentiable vector graphics have enabled powerful gradientbased optimization of vector primitives directly from raster images. However, existing frameworks formulate this as a flat optimization problem, forcing hundreds to thousands of randomly initialized curves to blindly compete for pixel-level error reduction. This disordered optimization leads to topology collapse, where macroscopic structures are distorted by internal high-frequency noise, resulting in a redundant and uneditable “polygon soup” that limits practical editability. To address this limitation, we propose Vector Scaffolding, a novel hierarchical optimization framework that shifts from flat pixel-matching to structured topological construction tailored for vector graphics. By identifying a key cause of topology collapse as the mathematical imbalance between area and boundary gradients, we introduce Interior Gradient Aggregation to stabilize the learning dynamics of multi-scale curve mixtures. Upon this stabilized landscape, we employ Progressive Stratification and Rapid Inflation Scheduling to progressively densify vector primitives with extremely high learning rates (×50). Experiments demonstrate that our approach accelerates optimization by 2.5× while simultaneously improving PSNR by up to 1.4dB over the previous state of the art.

ECCV 2026

Reasoning with Memory: A Temporal Granularity-Adaptive Framework for Training-Free Long Video Understanding

Linghao Meng, Qiankun Li, Junyuan Mao, Pujin Liao, Zhicheng He, Enbo Zhang, Kun Wang, Yang Liu, Huazhu Fu, Yueming Jin

While Multimodal Large Language Models (MLLMs) demonstrate superior generalization in fundamental video tasks, restricted context windows limit their long video understanding. To accommodate this constraint, models typically resort to keyframe selection. However, uniform sampling or static query-guided selection often overlooks critical temporal context, failing to adapt to the varying query temporal granularities. In this paper, we propose ReMem, a temporal granularityadaptive keyframe selection framework for training-free LongVideoQA. ReMem introduces a dual-level memory-augmented adaptation. At the query level, Memory-Driven Question Parsing leverages LLM longterm memory to decode question temporal granularity and extract semantic entities. At the video level, Synergistic Dual-Semantic Frame Alignment exploits intrinsic structural memory to align frames with query semantics, guiding Structure-Aware Dynamic Frame Routing to cluster events and optimally distribute sampling budgets. By explicitly preserving temporal information with memory mechanisms, ReMem suppresses redundancy and empowers MLLMs to perform robust multi-granular video reasoning. Evaluations across four popular LongVideoQA benchmarks using three MLLMs demonstrate highly efficient, state-of-the-art zero-shot performance; notably, LLaVA-Video with ReMem reaches 54.5% (+12.3%) on LVBench and 67.1% (+8.2%) on LongVideoBench. Code is available at ReMem.

ECCV 2026

ISAC: Training-Free Instance-to-Semantic Attention Control for Multi-Instance Generation

Sanghyun Jo, Wooyeol Lee, Ziseok Lee, Jonghyun Choi, Jaesik Park, Kyungsu Kim

Recent open-weight text-to-image (T2I) diffusion models still struggle with multi-instance prompts, often omitting or merging instances and mixing semantics among similar objects. We trace these failures to early denoising steps, before instance boundaries are reliably stabilized. Existing training-free guidance is largely driven by cross-attention or other token-conditioned semantic signals. Such guidance can separate concepts at the token level, but largely assumes that distinct instance regions have already emerged. In early denoising steps, it cannot reliably carve out these regions, so count failures and semantic mixing persist. By contrast, self-attention exposes class-agnostic instance layouts during early denoising. To exploit this asymmetry, we propose ISAC (Instance-to-Semantic Attention Control), a training-free, model-agnostic objective that first stabilizes self-attention layouts and then binds cross-attention semantics within them, without fine-tuning or external vision models. Across T2I-CompBench, HRS-Bench, and our newly curated IntraCompBench, ISAC consistently outperforms prior training-free methods. Furthermore, ISAC enhances layout-to-image controllers by refining coarse, overlapping bounding boxes into dense instance masks. Code and IntraCompBench are available at https://shjo-april.github.io/ISAC.

ECCV 2026

DR-GS: Physically-Based Deformable and Relightable 2D Gaussians

Jiaxin LI, Tong Wu, Yi Wei, Tailin Wu, Li Zhang

Gaussian splatting (GS) has garnered significant attention in VR/AR and digital content creation due to its explicit parameterization and efficient rendering capabilities. However, existing GS-based methods for deformable objects face two key limitations: (i) illumination is erroneously baked into textures, causing physically inconsistent responses under dynamic deformations and lighting changes; (ii) snapshot-based reconstruction restricts post-reconstruction material editing. To address these challenges, we propose Deformable and Relightable GS (DR-GS), a unified Gaussian framework that integrates physically-based inverse rendering, relighting, and deformation-aware manipulation. Through explicitly disentangling geometry, illumination, and material representations, DR-GS overcomes the limitations of static snapshots, resolving unrealistic appearance under varying conditions while enabling postreconstruction parameter editing. Extensive experiments show that DRGS achieves leading visual quality across static reconstruction, dynamic deformation, and relighting, reliably preserving reflections and specular highlights on glossy surfaces. It further establishes a fully decoupled geometry-illumination-material pipeline, enabling high-quality 3D asset creation and comprehensive post-editing.

ECCV 2026

Multi-Block-Attention-based Color Constancy

Oguzhan Ulucan, Diclehan Ulucan, Marc Ebner

Color constancy algorithms estimate and remove the effect of the illuminant from a captured image, enabling consistent color perception under varying illumination conditions. Learning-based methods achieve high accuracy but tend to be biased toward the spectral characteristics of training cameras. Traditional algorithms such as white-patch Retinex are computationally efficient and camera-independent, but underperform when scene statistics deviate from their assumptions. Therefore, in this paper, we propose a hybrid approach that combines classical assumptions with learned features. Our model processes images in a block-based manner through two complementary branches. The backbone branch extracts scene features and identify reliable regions for illuminant estimation. Guided by these saliency maps, the statistics branch computes per-block illumination priors using a soft maximum formulation that provides a differentiable approximation to the classical white-patch Retinex. Features from both branches are fused and refined through multi-head self-attention, which enables blocks to exchange information across the image, addressing the aggregation bias common to block-based methods. Experiments on five benchmarks demonstrate that our model achieves state-of-the-art performance with a compact architecture (∼1.1 MB) that requires no camera-specific calibration or metadata.

ECCV 2026

ExpertEdit: Learning Skill-Aware Motion Editing from Expert Videos

Arjun Somayazulu, Kristen Grauman

Visual feedback is critical for motor skill acquisition in sports and rehabilitation, and psychological studies show that observing nearperfect versions of one’s own performance accelerates learning more effectively than watching expert demonstrations alone. We propose to enable such personalized feedback by automatically editing a person’s motion to reflect higher skill. Existing motion editing approaches are poorly suited for this setting because they assume paired input-output data—rare and expensive to curate for skill-driven tasks—and explicit edit guidance at inference. We introduce ExpertEdit, a framework for skill-driven motion editing trained exclusively on unpaired expert video demonstrations. ExpertEdit learns an expert motion prior with a masked language modeling objective that infills masked motion spans with expert-level refinements. At inference, novice motion is masked at skill-critical moments and projected into the learned expert manifold, producing localized skill improvements without paired supervision or manual edit guidance. Across eight diverse techniques and three sports from Ego-Exo4D [15] and Karate Kyokushin [52], ExpertEdit outperforms state-of-the-art supervised motion editing methods on multiple metrics of motion realism and expert quality. Project page and benchmark available at: https://vision.cs.utexas.edu/projects/expert_edit/

ECCV 2026

DualCamCtrl: Dual-Branch Diffusion Model for Geometry-Aware Camera-Controlled Video Generation

Hongfei Zhang, Kanghao Chen, Zixin Zhang, Harold Haodong Chen, Yuanhuiyi Lyu, Kun Zhou, Yuqi Zhang, Shuai Yang, Yingcong Chen

This paper presents DualCamCtrl, a novel architecture forcamera-controlled video generation that internalizes geometric reason-ing into the diffusion process. Existing methods rely solely on ray-basedcamera conditioning, which entangles appearance and geometry model-ing within a single representation, or resort to external 3D cache andmulti-stage pipelines that are hard to jointly optimize. DualCamCtrladdresses this by generating depth as a co-evolving modality alongsideRGB through a dual-branch framework, enabling geometry and appear-ance to inform each other throughout denoising. To govern this cross-modal interaction, we propose the SemantIc Guided Mutual Alignment(SIGMA) mechanism, which schedules RGB–depth fusion in a stage-aware manner based on our empirical finding that depth and cameraposes exert asymmetric influence across the denoising trajectory. To-gether with a two-stage training strategy, these designs enable DualCam-Ctrl to disentangle and jointly evolve appearance and geometry, gener-ating videos that faithfully adhere to specified camera trajectories. Ex-tensive experiments demonstrate that DualCamCtrl achieves more con-sistent camera-controlled video generation with over 40% reductionon camera rotation errors compared with prior methods. Project Page:https://soyouthinkyoucantell.github.io/dualcamctrl-page.

ECCV 2026

Seeing Through the Weights: Privacy Leakage in Scene Coordinate Regression

Oleksii Nasypanyi, Jaemin Cho, Utku Ozbulak, Byungkon Kang, Francois Rameau

Scene Coordinate Regression (SCR) methods are increasingly adopted for visual localization. In these approaches, the scene is implicitly encoded within a neural network that regresses a 3D world coordinate for each image pixel. Because the scene is represented only through the network parameters and not stored explicitly as images or maps, such methods are often assumed to be privacy-preserving. In this work, we show that this assumption is incorrect in practice. Specifically, we introduce a query-based attack that reconstructs the 3D geometry of the training environment from an SCR model under different levels of model access. To do so, we repeatedly query the model with batches of proxy images unrelated to the target scene to obtain dense pixel-wise 3D coordinates. Reliable points are identified through their stability under small input perturbations and can be further refined in a white-box setting. These stable points are accumulated across independent query batches to recover the scene geometry. From the recovered 3D representation, we also invert the network features to synthesize images from arbitrary viewpoints, revealing additional appearance information. Experiments on indoor and outdoor datasets demonstrate that substantial portions of training environments can be reconstructed with high geometric fidelity. Beyond geometry, we also recover an approximate color appearance, which exposes recognizable layout and potentially sensitive scene elements. This directly contradicts claims in the literature that SCR representations are privacy-preserving by design, and reveals a real risk when such systems are deployed in private or security-critical spaces. The project page is available here.

ECCV 2026

LineGraph2Road: Structural Graph Reasoning on Line Graphs for Road Network Extraction

Zhengyang Wei, Renzhi Jing, Yiyi He, Jenny Suckale

Extracting routable road networks from satellite imagery requires accurate topology recovery beyond pixel-level segmentation. Recent methods decompose the task into keypoint detection and connectivity prediction, but reliably inferring road connectivity under structural ambiguity remains challenging. Overpasses introduce non-planar crossings that can create false shortcuts, and occlusions break visual continuity, requiring long-range structural reasoning. We formulate road extraction as fully unobserved connectivity inference and construct a global but sparse Euclidean graph from detected keypoints to better leverage the long-term context. To improve link prediction, we transform this graph into its line graph and perform reasoning using a Graph Transformer. We propose an end-to-end pipeline that integrates vision-based segmentation, sparse graph construction, and structured inference. Our method explicitly models overpasses and uses topology-preserving vertex extraction to mitigate routing-critical errors. This approach achieves state-of-the-art performance on City-scale, SpaceNet, and Global-scale benchmarks in topology metrics including TOPO-F1 and APLS. In addition to quantitative gains, our method improves reconstruction of multi-level road structures crucial for real-world routing reliability. The code is available at: https://github.com/wzzzzzzy/LineGraph2Road.

ECCV 2026

Geometric Probing for Isotropic Optimization Manifold in Sparse-View 3D Gaussian Splatting

yunlong zhao, Xiaoheng Deng, Hongyan Xu, Yichao Cao, Keke Huang, Shuo Yang, Xiangjian He, Lei Fan, Zhuohua Qiu, Xiu Su

3D Gaussian Splatting (3DGS) achieves impressive rendering quality but degrades drastically under sparse views. Through optimization manifold analysis, we reveal that sparse supervision produces anisotropic loss surfaces where rendering quality collapses sharply along three geometric fragility axes: position shifts, rotation/scale ill-conditioning, and opacity instability. Guided by this analysis, we propose Stable 3DGS (StableGS), which steers optimization toward isotropic stability via geometric probing. Unlike the generic, uniform smoothing of methods like SAM, which cannot fit 3DGS, StableGS uses domain-specific geometric probes to target the splatting operator’s unique instabilities. Our approach comprises: (1) Attribute-Space Probing that varies each Gaussian’s attributes based on Hessian-informed sensitivity analysis to measure rendering fragility, then minimizes loss at probe points; (2) Viewpoint-Space Probing that samples geometrically critical poses and enforces consistency between base and probed configurations. StableGS significantly advances state-of-the-art (SOTA) performance across multiple benchmarks, achieving substantial improvements such as +0.64dB PSNR on DTU [14] with 74% anisotropy reduction in optimization manifold. Code is available at https://github.com/zyl123456aB/StableGS.

ECCV 2026

Open-Vocabulary Long Term Action Anticipation

Syed Talal Wasim, Jinhui Yi, Hamid Suleman, Ahmad Javed, Yanan Luo, Muhammad Muzammal Naseer, Juergen Gall

Action anticipation, i.e., predicting future actions from past video observations, is fundamental to intelligent systems that assist humans. Despite progress in model architectures, current evaluation practices exhibit critical limitations: methods evaluate exclusively in closedset settings where the training and test action vocabularies are identical, thereby preventing an understanding of generalization to novel action classes encountered in real-world deployment. We therefore introduce the first open-vocabulary evaluation framework for action anticipation, where models are trained on one egocentric dataset and tested on entirely different egocentric datasets with novel action vocabularies. Since our thorough evaluation shows that adapting existing approaches to this task is insufficient, we propose a novel approach that employs horizonspecific learnable queries and a lightweight text encoder adaptation for open-vocabulary long-term action anticipation. It substantially outperforms other approaches that we have adapted to this task.

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