GenAI4ELab/papercli-papers huggingface dataset:
R4: Retrieval-Augmented Reasoning for Vision-Language Models in 4D Spatio-Temporal Space
Authors: Tin Stribor Sohn; Maximilian Dillitzer; Jason J. Corso; Eric Sax
Venue: CVPR 2026 (main)

Abstract: Humans perceive and reason about their surroundings in four dimensions - three spatial and one temporal axis - by building persistent, structured internal representations that encode semantic meaning, spatial layout, and temporal dynamics. These multimodal memories enable them to recall past events, infer unobserved states, and integrate new information into context-dependent reasoning. Inspired by this capability, we introduce R4, a training-free framework for retrieval-augmented reasoning in 4D spatio-temporal space that equips vision-language models (VLMs) with structured, lifelong memory. R4 continuously constructs a 4D knowledge database by anchoring object-level semantic descriptions in metric space and time, yielding a persistent world model that can be shared across agents. At inference, natural language queries are decomposed into semantic, spatial, and temporal keys to retrieve relevant observations, which are integrated into the VLM's reasoning through an iterative retrieval-reasoning loop. Unlike classical retrieval-augmented generation methods, retrieval in R4 operates directly in structured 4D space, enabling episodic and collaborative reasoning without training. Experiments on embodied question answering and navigation benchmarks demonstrate that R4 substantially improves retrieval and reasoning over spatio-temporal information compared to baselines, advancing a new paradigm for embodied 4D reasoning in dynamic environments.
WildCap: Facial Albedo Capture in the Wild via Hybrid Inverse Rendering
Authors: Yuxuan Han; Xin Ming; Tianxiao Li; Zhuofan Shen; Qixuan Zhang; Lan Xu; Feng Xu
Venue: CVPR 2026 (main)

Abstract: Existing methods achieve high-quality facial albedo capture under controllable lighting, which increases capture cost and limits usability. We propose WildCap, a novel method for high-quality facial albedo capture from a smartphone video recorded in the wild. To disentangle high-quality albedo from complex lighting effects in in-the-wild captures, we propose a novel hybrid inverse rendering framework. We first apply a data-driven method, i.e., SwitchLight, to convert the captured images into more constrained conditions and then adopt model-based inverse rendering. However, unavoidable local artifacts in network predictions, such as shadow-baking, are non-physical and thus hinder accurate inverse rendering of lighting and material. To address this, we propose a novel texel grid lighting model to explain non-physical effects as clean albedo illuminated by local physical lighting. During optimization, we jointly sample a diffusion prior for the albedo map and optimize the lighting, effectively resolving scale ambiguity between local lights and albedo. Other reflectance maps are then predicted from the albedo. Our method achieves significantly better results than prior arts in the same capture setup, closing the quality gap between in-the-wild and controllable recordings by a large margin.
SAVA-X: Ego-to-Exo Imitation Error Detection via Scene-Adaptive View Alignment and Bidirectional Cross View Fusion
Authors: Xiang Li; Heqian Qiu; Lanxiao Wang; Benliu Qiu; Fanman Meng; Linfeng Xu; Hongliang Li
Venue: CVPR 2026 (main)

Abstract: Error detection is crucial in industrial training, healthcare, and assembly quality control. Most existing work assumes a single-view setting and cannot handle the practical case where a third-person (exo) demonstration is used to assess a first-person (ego) imitation. We formalize Ego->Exo Imitation Error Detection: given asynchronous, length-mismatched ego and exo videos, the model must localize procedural steps on the ego timeline and decide whether each is erroneous. This setting introduces cross-view domain shift, temporal misalignment, and heavy redundancy. Under a unified protocol, we adapt strong baselines from dense video captioning and temporal action detection and show that they struggle in this cross-view regime. We then propose SAVA-X, an Align-Fuse-Detect framework with (i) view-conditioned adaptive sampling, (ii) scene-adaptive view embeddings, and (iii) bidirectional cross-attention fusion. On the EgoMe benchmark, SAVA-X consistently improves AUPRC and mean tIoU over all baselines, and ablations confirm the complementary benefits of its components.
SemanticVLA: Towards Semantic Reasoning over Action Memorization via Synergistic Explicit Trace and Latent Action Planning
Authors: Fei Ni; Zhuo Chen; Yifu Yuan; Zibin Dong; Xianze Yao; Shan Luo; Jianye Hao; Jiankang Deng; Stefanos Zafeiriou
Venue: CVPR 2026 (main)

Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm where pretrained Vision-Language Models (VLMs) serve as System 2 for high-level reasoning, connected to action experts as System 1 for low-level motor control.However, current works fail to genuinely leverage VLM capabilities: VLMs produce latent embeddings that lack semantic interpretability, providing ambiguous and unstable guidance to downstream policies, while solely action supervision further causes VLMs to degenerate into parameter-heavy fusion encoders that memorize action patterns rather than perform generalized reasoning.To bridge this gap, we introduce SemanticVLA, which leverages VLM reasoning through synergistic dual-path design. Explicit trace reasoning generates interpretable spatial waypoints as textual coordinate sequences through the VLM's native language interface, directly reusing its pretrained spatial grounding to provide a thinking process for task planning. Latent action tokens complement trace reasoning by learning compact visuomotor primitives grounded in visual observations, providing more fine-grained action representations beyond pure coordinate prediction. This synergy enables trace reasoning to leverage VLM's multimodal understanding for refining latent token prediction, while latent tokens provide stable and grounded guidance that compensates for trace's numerical sensitivity.SemanticVLA achieves 97.0% average success rate on LIBERO and 65.1% on SimplerEnv WidowX, substantially outperforming strong baselines. More importantly, SemanticVLA maintains significantly more stable performance under instruction rephrasing in both simulation suites, and demonstrates strong advantages on real-world long-horizon and reasoning-intensive tasks.By bridging VLM reasoning and action expert through semantically explicit trace and visually grounded latent action tokens, our approach enables genuine reasoning rather than action memorization.
AERGS-SLAM: Auto-Exposure-Robust Stereo 3D Gaussian Splatting SLAM
Authors: Zhiyu Zhou; Feng Hui; Yu Liu
Venue: CVPR 2026 (main)

Abstract: 3D Gaussian splatting (3DGS) has emerged as a revolutionary scene representation in simultaneous localization and mapping (SLAM) research. However, existing research on 3DGS-based SLAM fails to accurately address the appearance variations induced by camera auto-exposure in prevalent real-world scenarios, resulting in reduced localization and photorealistic mapping accuracy. To address this issue, we propose a stereo auto-exposure-robust Gaussian splatting SLAM (AERGS-SLAM), a framework robust to such variations and enables both reliable localization and exposure-controlled photorealistic mapping. Our key contributions are two fold. Firstly, we propose a camera exposure network to model the camera exposure process, which we integrate with Gaussian splatting to achieve exposure-controlled novel view synthesis. Secondly, we exploit an illumination-robust geometric feature for localization and Gaussian map initialization, enhancing localization accuracy under exposure-varying scenarios. Extensive experiments on public datasets and our self-collected real-world dataset demonstrate that AERGS-SLAM outperforms baselines in both localization performance and photorealistic mapping quality. Our code is available at: https://github.com/zzy-2021/AERGS-SLAM.
Photo3D: Advancing Photorealistic 3D Generation through Structure-Aligned Detail Enhancement
Authors: Xinyue Liang; Zhiyuan Ma; Lingchen Sun; Yanjun Guo; Lei Zhang
Venue: CVPR 2026 (main)

Abstract: Although recent 3D-native generators have made great progress in synthesizing reliable geometry, they still fall short in achieving realistic appearances. A key obstacle lies in the lack of diverse and high-quality real-world 3D assets with rich surface details, since capturing such data is intrinsically difficult due to the diverse scales of scenes, non-rigid motions of objects, and the limited precision of scanners.We introduce Photo3D, a framework for advancing photorealistic 3D generation, which is driven by the image data generated by the GPT-4o-Image model.Considering that the generated images can distort 3D structures due to their lack of multi-view consistency, we design a structure-aligned multi-view synthesis pipeline and construct a detail-enhanced multi-view dataset paired with 3D geometry. Building on it, we present a realistic detail enhancement scheme that leverages perceptual feature adaptation and semantic structure matching to enforce appearance consistency with the realistic detail priors while preserving the structural consistency with the 3D-native geometry. While our scheme is general to different 3D-native generators, we present dedicated training strategies to facilitate the optimization of geometry-texture coupled and decoupled 3D-native generation paradigms. Experiments demonstrate that Photo3D generalizes well across diverse 3D-native generation paradigms and achieves state-of-the-art photorealistic 3D generation performance.  Project Page: https://liangsanzhu.github.io/photo3d-page/
Rosetta Stone For Unified MLLMs: A Unified Tokenizer to Decipher Understanding and Generation
Authors: Wenyu Sun; Hufei Li; Ruijin Jin; Xiangheng Kong; Yuning Jiang
Venue: CVPR 2026 (main)

Abstract: Major state-of-the-art unified tokenizers predominantly adopt pixel reconstruction and feature alignment as pretext tasks, they leave key domains largely unexplored such as architecture, supervised objectives and tasks interaction, potentially resulting in limited performance. We systematically investigate the critical factors of a unified visual tokenizer and propose a novel framework that strengthens synergy between understanding and generation in various aspects. Our initial analysis focus on properties of frontier vision models, confirming inherent conflict in contrastive learning style models for unifying generation and understanding, and demonstrate distinct convergence behavior of codebooks. To address the above bottleneck, we hierarchically decouple the conflicting proxy tasks, enriching the diversity of semantic features supervision to enhance thesemantic and low-level capabilities. Subsequently, we further introduce attention-prioritized mapping strategy, which guides fine-grained generation with powerful semantic prior. Our method achieves rFID of 0.33 and zero-shot accuracy of 80.9% on ImageNet at 256x256 resolution, surpassing VILA-U by 7.6% and outperforms continuous embedding of SigLIP. When applied to discrete unified MLLMs, our 7B model exceeds TokenFlow-13B by 3.1% in understanding and achieve SOTA performance in GenAI-Bench and MJHQ-30K.
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization
Authors: Jungwook Seo; Minjeong Kim; Younkwan Lee; Seungho Shin; Sungyong Baik
Venue: CVPR 2026 (main)

Abstract: Detecting subtle visual anomalies in images remains challenging, particularly when only normal samples are available a priori. Such unsupervised anomaly detection is typically solved by measuring feature similarity of a query patch to a memory of normal patches. However, similarity alone does not reveal how strongly a query patch violates the structure of the normal feature manifold. We propose a training-free Laplacian graph energy optimization formulation, named ANoCo that scores Anomaly by the cost of Non-Conformity of a query patch to align with a fixed normal manifold. For each query patch, we construct a bipartite query to normal graph weighted by cosine affinity, explicitly removing query-query and normal-normal edges to prevent evidence dilution. We formulate anomaly scoring as a convex Laplacian energy with anchored normal nodes, and solve in closed form. In particular, we do not use the optimized features themselves--the anomaly score is the magnitude of the update required to satisfy normality constraints, reframing the graph Laplacian as a non-conformity operator rather than a smoothing prior. The proposed method introduces no learnable parameters, message passing, or sampling, and has complexity comparable to a single linear solve. Across standard benchmarks, it delivers strong image-level AUROC, stable localization maps, and improved robustness over prior methods, demonstrating the effectiveness of using optimization-induced feature drift as anomaly measure.
TTL: Test-time Textual Learning for OOD Detection with Pretrained Vision-Language Models
Authors: Jinlun Ye; Jiang Liao; Runhe Lai; Xinhua Lu; Jiaxin Zhuang; Zhiyong Gan; Ruixuan Wang
Venue: CVPR 2026 (main)

Abstract: Vision-language models (VLMs) such as CLIP exhibit strong Out-of-distribution (OOD) detection capabilities by aligning visual and textual representations. Recent CLIP-based test-time adaptation methods further improve detection performance by incorporating external OOD labels. However, such labels are finite and fixed, while the real OOD semantic space is inherently open-ended. Consequently, fixed labels fail to represent the diverse and evolving OOD semantics encountered in test streams.To address this limitation, we introduce Test-time Textual Learning (TTL), a framework that dynamically learns OOD textual semantics from unlabeled test streams, without relying on external OOD labels.TTL updates learnable prompts using pseudo-labeled test samples to capture emerging OOD knowledge. To suppress noise introduced by pseudo-labels, we introduce an OOD knowledge purification strategy that selects reliable OOD samples for adaptation while suppressing noise.  In addition, TTL maintains an OOD Textual Knowledge Bank that stores high-quality textual features, providing stable score calibration across batches. Extensive experiments on two standard benchmarks with nine OOD datasets demonstrate that TTL consistently achieves state-of-the-art performance, highlighting the value of textual adaptation for robust test-time OOD detection. Our code is available at https://github.com/figec/TTL.
Towards Stable Self-Supervised Object Representations in Unconstrained Egocentric Video
Authors: Yuting Tan; Xilong Cheng; Yunxiao Qin; Zhengnan Li; Jingjing Zhang
Venue: CVPR 2026 (main)

Abstract: Humans develop visual intelligence through perceiving and interacting with their environment--a self-supervised learning process grounded in egocentric experience. Inspired by this, we ask how can artificial systems learn stable object representations from continuous, uncurated first-person videos without relying on manual annotations. This setting poses challenges of separating, recognizing, and persistently tracking objects amid clutter, occlusion, and ego-motion. We propose EgoViT, a unified vision Transformer framework designed to learn stable object representations from unlabeled egocentric video. EgoViT bootstraps this learning process by jointly discovering and stabilizing "proto-objects" through three synergistic mechanisms: (1) Proto-object Learning, which uses intra-frame distillation to form discriminative representations; (2) Depth Regularization, which grounds these representations in geometric structure; and (3) Teacher-Filtered Temporal Consistency, which enforces identity over time. This creates a virtuous cycle where initial object hypotheses are progressively refined into stable, persistent representations. The framework is trained end-to-end on unlabeled first-person videos and exhibits robustness to geometric priors of varied origin and quality. On standard benchmarks, EgoViT achieves +8.0% CorLoc improvement in unsupervised object discovery and +4.8% mIoU improvement in semantic segmentation, demonstrating its potential to lay a foundation for robust visual abstraction in embodied intelligence.
Ultra-Low Bitrate Perceptual Image Compression with Shallow Encoder
Authors: Tianyu Zhang; Dong Liu; Chang Wen Chen
Venue: CVPR 2026 (main)

Abstract: Ultra-low bitrate image compression (below 0.05 bits per pixel) is increasingly critical for bandwidth-constrained and computation-limited encoding scenarios such as edge devices. Existing frameworks typically rely on large pretrained encoders (e.g., VAEs or tokenizer-based models) and perform transform coding within their generative latent space. While these approaches achieve impressive perceptual fidelity, their reliance on heavy encoder networks makes them unsuitable for deployment on weak sender devices. In this work, we explore the feasibility of applying shallow encoders for ultra-low bitrate compression and propose a novel Asymmetric Extreme Image Compression (AEIC) framework that pursues simultaneously encoding simplicity and decoding quality. Specifically, AEIC employs moderate or even shallow encoder networks, while leveraging an one-step diffusion decoder to maintain high-fidelity and high-realism reconstructions under extreme bitrates. To further enhance the efficiency of shallow encoders, we design a dual-side feature distillation scheme that transfers knowledge from AEIC with moderate encoders to its shallow encoder variants. Experiments show that AEIC not only outperforms existing methods on rate-distortion-perception performance at ultra-low bitrates, but also delivers exceptional encoding efficiency for 35.8 FPS on 1080P images, while maintaining competitive decoding speed compared to existing methods. Code is available at https://github.com/LuizScarlet/AEIC.
FreqSIC: Frequency-aware Stereo Image Compression with Bi-directional Checkerboard Context Model
Authors: Shiyu Qin; Yongkang Lu; Yimin Zhou; Jiawei Li; Yifan Ren; Yuerong Xue; Shu-Tao Xia; Bin Chen
Venue: CVPR 2026 (main)

Abstract: Stereo image compression is essential for a wide range of 3D vision. Recent methods have demonstrated strong capabilities in eliminating inter-view redundancy and enabling compact entropy coding via spatial-domain stereo transformation and advanced autoregressive entropy models. However, these approaches often suffer from high-frequency information loss and incur considerable coding latency. To overcome these limitations, we propose a novel frequency stereo context transfer (FSCT) module. Unlike spatial-domain methods, the FSCT module separately captures inter-view redundancy in high- and low-frequency components and dynamically balances their contributions to preserve reconstruction quality. In addition, we replace the conventional autoregressive framework with a checkerboard strategy and integrate the FSCT module to model inter-view priors, enabling faster and more efficient entropy coding. Extensive experiments demonstrate that our method achieves state-of-the-art rate-distortion performance among existing stereo image compression approaches, while also attaining the lowest coding latency.
T2SGrid: Temporal-to-Spatial Gridification for Video Temporal Grounding
Authors: Chaohong Guo; Yihan He; Yongwei Nie; Fei Ma; Xuemiao Xu; Chengjiang Long
Venue: CVPR 2026 (main)

Abstract: Video Temporal Grounding (VTG) aims to localize the video segment that corresponds to a natural language query, which requires a comprehensive understanding of complex temporal dynamics. Existing Vision-LMMs typically perceive temporal dynamics via positional encoding, text-based timestamps, or visual frame numbering. However, these approaches exhibit notable limitations: assigning each frame a text-based timestamp token introduces additional computational overhead and leads to sparsity in visual attention, positional encoding struggles to capture absolute temporal information, and visual frame numbering often compromises spatial detail. To address these issues, we propose Temporal to Spatial Gridification (T2SGrid), a novel framework that reformulates video temporal understanding as a spatial understanding task. The core idea of T2SGrid is to process video content in clips rather than individual frames. we employ a overlapping sliding windows mechanism to segment the video into temporal clips. Within each window, frames are arranged chronologically in a row-major order into a composite grid image, effectively transforming temporal sequences into structured 2D layouts. The gridification not only encodes temporal information but also enhances local attention within each grid. Furthermore, T2SGrid enables the use of composite text timestamps to establish global temporal awareness. Experiments on standard VTG benchmarks demonstrate that T2SGrid achieves superior performance.
LiREC-Net: A Target-Free and Learning-Based Network for LiDAR, RGB, and Event Calibration
Authors: Aditya Ranjan Dash; Ramy Battrawy; René Schuster; Didier Stricker
Venue: CVPR 2026 (main)

Abstract: Advanced autonomous systems rely on multi-sensor fusion for safer and more robust perception. To enable effective fusion, calibrating directly from natural driving scenes (i.e., target-free) with high accuracy is crucial for precise multi-sensor alignment. Existing learning-based calibration methods are typically designed for only a single pair of sensor modalities (i.e., a bi-modal setup). Unlike these methods, we propose LiREC-Net, a target-free, learning-based calibration network that jointly calibrates multiple sensor modality pairs, including LiDAR, RGB, and event data, within a unified framework. To reduce redundant computation and improve efficiency, we introduce a shared LiDAR representation that leverages features from both its 3D nature and projected depth map, ensuring better consistency across modalities. Trained and evaluated on established datasets, such as KITTI and DSEC, our LiREC-Net achieves competitive performance to bi-modal models and sets a new strong baseline for the tri-modal use case.
OVI-MAP: Open-Vocabulary Instance-Semantic Mapping
Authors: Zilong Deng; Federico Tombari; Marc Pollefeys; Johanna Wald; Daniel Barath
Venue: CVPR 2026 (main)

Abstract: Incremental open-vocabulary 3D instance-semantic mapping is essential for autonomous agents operating in complex everyday environments. However, it remains challenging due to the need for robust instance segmentation, real-time processing, and flexible open-set reasoning. Existing methods often rely on the closed-set assumption or dense per-pixel language fusion, which limits scalability and temporal consistency. We introduce OVI-MAP that decouples instance reconstruction from semantic inference. We propose to build a class-agnostic 3D instance map that is incrementally constructed from RGB-D input, while semantic features are extracted only from a small set of automatically selected views using vision-language models. This design enables stable instance tracking and zero-shot semantic labeling throughout online exploration. Our system operates in real time and outperforms state-of-the-art open-vocabulary mapping baselines on standard benchmarks.