GenAI4ELab/papercli-papers huggingface dataset:
Thinking in Dynamics: How Multimodal Large Language Models Perceive, Track, and Reason Dynamics in Physical 4D World
Authors: Yuzhi Huang; Kairun Wen; Rongxin Gao; Dongxuan Liu; Yibin Lou; Jie Wu; Jing Xu; Jian Zhang; Zheng Yang; Yunlong Lin; Chenxin Li; Panwang Pan; Junbin Lu; Jingyan Jiang; Xinghao Ding; Yue Huang; Zhi Wang
Venue: CVPR 2026 (main)

Abstract: Humans inhabit a physical 4D world, where spatial geometry and semantic content evolve over time, forming a dynamic reality. While current Multimodal Large Language Models (MLLMs) demonstrate strong capabilities in understanding static visual inputs, it remains unclear whether they can effectively "think in dynamics," i.e., perceive, track, and reason about spatio-temporal evolution in complex scenes.To systematically evaluate these abilities, we introduce \texttt Dyn-Bench , a large-scale benchmark designed to assess spatio-temporal reasoning and localized dynamics perception. Constructed through multi-stage filtering over massive 2D and 4D data sources, \texttt Dyn-Bench  provides a high-quality collection of diverse dynamic scenes, consisting of 1k videos, 7k visual question answering (VQA) pairs, and 3k dynamic object grounding samples.We comprehensively study general-purpose, spatial-aware, and region-level MLLMs to understand how they "think in dynamics" from both linguistic and visual perspectives. Our results reveal that existing models struggle to jointly excel in both spatio-temporal reasoning and dynamic object grounding, often producing inconsistent interpretations of motion and interaction. Conventional prompting strategies i.e., chain-of-thought or caption-based hints) provide only limited improvements.In contrast, structured integration approaches, including Mask-Guided Fusion and the Spatio-Temporal Textual Cognitive Map (ST-TCM), substantially enhance MLLMs' dynamic perception and spatio-temporal reasoning in an evolving 4D world. These findings underscore the importance of explicit spatio-temporal structural cues to bridge the gap between static perception and dynamic reasoning in MLLMs.
3DReflecNet: A Large-Scale Dataset for 3D Reconstruction of Reflective, Transparent, and Low-Texture Objects
Authors: Zhicheng Liang; Haoyi Yu; Boyan Li; Dayou Zhang; Zijian Cao; Tianyi Gong; Junhua Liu; Shuguang Cui; Fangxin Wang
Venue: CVPR 2026 (main)

Abstract: Accurate 3D reconstruction of objects with reflective, transparent, or low-texture surfaces still remains notoriously challenging. Such materials often violate key assumptions in multi-view reconstruction pipelines, such as photometric consistency and the availability on distinct geometric texture cues. Existing datasets primarily focus on diffuse, textured objects, and therefore provide limited insight into performance under real-world material complexities.  We introduce 3DReflecNet, a large-scale hybrid dataset exceeding 22 TB that is specifically designed to benchmark and advance 3D vision methods for these challenging materials. 3DReflecNet combines two types of data: over 120,000 synthetic instances generated via physically-based rendering of more than 12,000 shapes, and over 1,000 real-world objects captured using consumer devices. Together, these data consist of more than 7 million multi-view frames. The dataset spans diverse materials, complex lighting conditions, and a wide range of geometric forms--including shapes generated from both real and LLM-synthesized 2D images using diffusion-based pipelines. To support robust evaluation, we design benchmarks for five core tasks: image matching, structure-from-motion, novel view synthesis, reflection removal, and relighting. Extensive experiments demonstrate that state-of-the-art methods struggle to maintain accuracy across these settings, highlighting the need for more resilient 3D vision models.
PHAC: Promptable Human Amodal Completion
Authors: Seung Young Noh; Ju Yong Chang
Venue: CVPR 2026 (main)

Abstract: Conditional image generation methods are increasingly used in human-centric applications, yet existing human amodal completion (HAC) models offer users limited control over the completed content. Given an occluded person image, they hallucinate invisible regions while preserving visible ones, but cannot reliably incorporate user-specified constraints such as a desired pose or spatial extent. As a result, users often resort to repeatedly sampling the model to obtain a satisfactory output. Pose-guided person image synthesis (PGPIS) methods allow explicit pose conditioning, but frequently fail to preserve the instance-specific visible appearance and tend to be biased toward the training distribution, even when built on strong diffusion model priors. To address these limitations, we introduce promptable human amodal completion (PHAC), a new task that completes occluded human images while satisfying both visible appearance constraints and multiple user prompts. Users provide simple point-based prompts, such as additional joints for the target pose or bounding boxes for desired regions; these prompts are encoded using ControlNet modules specialized for each prompt type. These modules inject the prompt signals into a pre-trained diffusion model, and we fine-tune only the cross-attention blocks to obtain strong prompt alignment without degrading the underlying generative prior. To further preserve visible content, we propose an inpainting-based refinement module that starts from a slightly noised coarse completion, faithfully preserves the visible regions, and ensures seamless blending at occlusion boundaries. Extensive experiments on standard HAC and PGPIS benchmarks show that our approach produces more physically plausible, higher-quality completions with significantly improved prompt alignment compared to existing amodal completion and pose-guided synthesis methods.
Spherical Leech Quantization for Visual Tokenization and Generation
Authors: Yue Zhao; Hanwen Jiang; Zhenlin Xu; Chutong Yang; Ehsan Adeli; Philipp Kraehenbuehl
Venue: CVPR 2026 (main)

Abstract: Lookup-free quantization has received much attention due to its efficiency on parameters and scalability to a large codebook. In this paper, we present a unified formulation of different non-parametric quantization methods through the lens of lattice coding. The geometry of lattice codes explains the necessity of auxiliary loss terms when training auto-encoders with certain existing lookup-free quantization variants such as BSQ. As a step forward, we explore a few possible candidates, including random lattices, generalized Fibonacci lattices, and densest sphere packing lattices. Among all, we find the Leech lattice-based quantization method, which is dubbed as Spherical Leech Quantization (\Lambda_ 24 -SQ), leads to both a simplified training recipe and an improved reconstruction-compression tradeoff thanks to its high symmetry and even distribution on the hypersphere. In image tokenization and compression tasks, this quantization approach achieves better reconstruction quality across all metrics than BSQ, the best prior art, while consuming slightly fewer bits. The improvement also extends to state-of-the-art auto-regressive image generation frameworks.
Outlier-Robust Diffusion Solvers for Inverse Problems
Authors: Yang Zheng; Jiahua Liu; Tongyao Pang; Wen Li; Zhaoqiang Liu
Venue: CVPR 2026 (main)

Abstract: Methods based on diffusion models (DMs) for solving inverse problems (IPs) have recently achieved remarkable performance. However, DM-based methods typically struggle against outliers, which are common in real-world measurements. In this work, to tackle IPs with outliers, we first refine the measurement via explicit noise estimation to mitigate the effect of noise. Subsequently, we formulate an iteratively reweighted least squares objective based on the Huber loss to address the outliers. We propose a method utilizing gradient descent to approximately solve the corresponding optimization problem for the robust objective. To avoid delicate tuning of the learning rate required by the gradient descent method, we further employ the conjugate gradient method with an efficient strategy for updating. Extensive experiments on multiple image datasets for linear and nonlinear tasks under various conditions demonstrate that our proposed methods exhibit robustness to outliers and outperform recent DM-based methods in most cases.
C^2FG: Control Classifier-Free Guidance via Score Discrepancy Analysis
Authors: Jiayang Gao; Tianyi Zheng; Jiayang Zou; Fengxiang Yang; Shice Liu; Luyao Fan; Zheyu Zhang; Hao Zhang; Jinwei Chen; Peng-Tao Jiang; Bo Li; Jia Wang
Venue: CVPR 2026 (main)

Abstract: Classifier-Free Guidance (CFG) is a cornerstone of modern conditional diffusion models, yet its reliance on the fixed or heuristic dynamic guidance weight is predominantly empirical and overlooks the inherent dynamics of the diffusion process. In this paper, we provide a rigorous theoretical analysis of the Classifier-Free Guidance. Specifically, we establish strict upper bounds on the score discrepancy between conditional and unconditional distributions at different timesteps based on the diffusion process.This finding explains the limitations of fixed-weight strategies and establishes a principled foundation for time-dependent guidance. Motivated by this insight, we introduce **Control Classifier-Free Guidance (C^2FG)**, a novel, training-free, and plug-in method that aligns the guidance strength with the diffusion dynamics via an exponential decay control function. Extensive experiments demonstrate that C^2FG is effective and broadly applicable across diverse generative tasks, while also exhibiting orthogonality to existing strategies.
MetaSpectra+: A Compact Broadband Metasurface Camera for Snapshot Hyperspectral+ Imaging
Authors: Yuxuan Liu; Wei Xu; Qi Guo
Venue: CVPR 2026 (main)

Abstract: We present MetaSpectra+, a compact multifunctional camera that supports two operating modes: (1) snapshot HDR + hyperspectral or (2) snapshot polarization + hyperspectral imaging. It utilizes a novel metasurface-refractive assembly that splits the incident beam into multiple channels and independently controls each channel's dispersion, exposure, and polarization. Unlike prior multifunctional metasurface imagers restricted to narrow (10--100 nm) bands, MetaSpectra+ operates over nearly the entire visible spectrum (250 nm). Relative to snapshot hyperspectral imagers, it achieves the shortest total track length and the highest reconstruction accuracy on benchmark datasets. The demonstrated prototype reconstructs high-quality hyperspectral datacubes and either an HDR image or two orthogonal polarization channels from a snapshot measurement.
Thermal is Always Wild: Characterizing and Addressing Challenges in Thermal-Only Novel View Synthesis
Authors: M. Kerem Aydin; Vishwanath Saragadam; Emma Alexander
Venue: CVPR 2026 (main)

Abstract: Thermal cameras provide reliable visibility in darkness and adverse conditions, but thermal imagery remains significantly harder to use for novel view synthesis (NVS) than visible-light images. This difficulty stems primarily from two characteristics of affordable thermal sensors. First, thermal images have extremely low dynamic range, which weakens appearance cues and limits the gradients available for optimization. Second, thermal data exhibit rapid frame-to-frame photometric fluctuations together with slow radiometric drift, both of which destabilize correspondence estimation and create high-frequency floater artifacts during view synthesis, particularly when no RGB guidance (beyond camera pose) is available. Guided by these observations, we introduce a lightweight preprocessing and splatting pipeline that expands usable dynamic range and stabilizes per-frame photometry. Our approach achieves state-of-the-art performance across thermal-only NVS benchmarks, without requiring any dataset-specific tuning.
RHO: Robust Holistic OSM-Based Metric Cross-View Geo-Localization
Authors: Junwei Zheng; Ruize Dai; Ruiping Liu; Zichao Zeng; Yufan Chen; Fangjinhua Wang; Kunyu Peng; Kailun Yang; Jiaming Zhang; Rainer Stiefelhagen
Venue: CVPR 2026 (main)

Abstract: Metric Cross-View Geo-Localization (MCVGL) aims to estimate the 3-DoF camera pose (position and heading) by matching ground and satellite images. In this work, instead of pinhole and satellite images, we study robust MCVGL using holistic panoramas and OpenStreetMap (OSM). To this end, we establish a large-scale MCVGL benchmark dataset, CV-RHO, with over 2.7M images under different weather and lighting conditions, as well as sensor noise. Furthermore, we propose a model termed RHO with a two-branch Pin-Pan architecture for accurate visual localization. A Split-Undistort-Merge (SUM) module is introduced to address the panoramic distortion, and a Position-Orientation Fusion (POF) mechanism is designed to enhance the localization accuracy. Extensive experiments prove the value of our CV-RHO dataset and the effectiveness of the RHO model, with a significant performance gain up to 20% compared with the state-of-the-art baselines. Project page: https://github.com/InSAI-Lab/RHO.
OASIS: On-Demand Hierarchical Event Memory for Streaming Video Reasoning
Authors: Zhijia Liang; Jiaming Li; Weikai Chen; Yanhao Zhang; Haonan Lu; Guanbin Li
Venue: CVPR 2026 (main)

Abstract: Streaming video reasoning requires models to operate in a setting where history grows without bound while meaningful evidence remains scarce. In such a landscape, relevant signal is like an oasis -- small, critical, and easily lost in a desert of redundancy. Enlarging memory only widens the desert; aggressive compression dries up the oasis. The real difficulty lies in discovering where to look, not how much to remember. We therefore introduce OASIS, a novel framework for streaming video reasoning that tackles this challenge through structured, on-demand retrieval. It organizes streaming history into hierarchical events and performs reasoning as controlled refinement -- short-context inference first, followed by semantically grounded retrieval only when uncertainty arises. As the retrieval is driven by high-level intent rather than embedding similarity, the retrieved memory is substantially more accurate and less noisy. Additionally, the mechanism is plug-and-play, training-free, and readily attaches to different streaming MLLM backbones. Experiments across multiple benchmarks and backbones show that OASIS achieves strong gains in long-horizon accuracy and compositional reasoning with bounded token cost and low request delay.
Beyond What's Shared: Recovering Lost Unique Information from Intermediate Layers to Boost Multimodal Geo-Foundation Models
Authors: JangHyeon Lee; Philipe Ambrozio Dias; Yao-Yi Chiang; Dalton Lunga
Venue: CVPR 2026 (main)

Abstract: Learning general-purpose representations of geographic locations has become essential to geospatial tasks such as population estimation and environmental monitoring. To obtain such representations, multimodal geo-foundation models often use contrastive learning (CL) to align satellite imagery with geo-coordinates, implicitly assuming that cross-modal (shared) information suffices for downstream tasks. However, given the breadth of tasks, task-relevant information may lie beyond the shared space, so retaining modality-specific (unique) features can improve task performance. Prior methods retain unique information through extra training objectives or external models, increasing training complexity. Motivated by the conventional wisdom that earlier layers capture general input features while later layers become task-specific, we hypothesize that intermediate layers in CL models retain more modality-specific structure than the alignment-optimized final layer. Through a trifecta layerwise analysis of modality gap, representation similarity, and mutual information, we validate this trend and find that fusing intermediate (more unique) and final (more shared) representations yields consistent gains on diverse geospatial tasks. Our findings reveal underutilized information diversity in CL models and show that simple layerwise fusion is an efficient path to richer geo-embeddings.
SceneMaker: Open-set 3D Scene Generation with Decoupled De-occlusion and Pose Estimation Model
Authors: Yukai Shi; Weiyu Li; Zihao Wang; Hongyang Li; Xingyu Chen; Ping Tan; Lei Zhang
Venue: CVPR 2026 (main)

Abstract: We propose a decoupled 3D scene generation framework called SceneMaker in this work. Due to the lack of sufficient open-set de-occlusion and pose estimation priors, existing methods struggle to simultaneously produce high-quality geometry and accurate poses under severe occlusion and open-set settings. To address these issues, we first decouple the de-occlusion model from 3D object generation, and enhance it by leveraging image datasets and collected de-occlusion datasets for much more diverse open-set occlusion patterns. Then, we propose a unified pose estimation model that integrates global and local mechanisms for both self-attention and cross-attention to improve accuracy. Besides, we construct an open-set 3D scene dataset to further extend the generalization of the pose estimation model. Comprehensive experiments demonstrate the superiority of our decoupled framework on both indoor and open-set scenes. Our codes and datasets is released at https://idea-research.github.io/SceneMaker/.
VAD-GS: Visibility-Aware Densification for 3D Gaussian Splatting in Dynamic Urban Scenes
Authors: Yikang Zhang; Rui Fan
Venue: CVPR 2026 (main)

Abstract: 3D Gaussian splatting (3DGS) has demonstrated impressive performance in synthesizing high-fidelity novel views. Nonetheless, its effectiveness critically depends on the quality of the initialized point cloud. Specifically, achieving uniform and complete point coverage over the underlying scene structure requires overlapping observation frustums, an assumption that is often violated in unbounded, dynamic urban environments. Training Gaussian models with partially initialized point clouds often leads to distortions and artifacts, as camera rays may fail to intersect valid surfaces, resulting in incorrect gradient propagation to Gaussian primitives associated with occluded or invisible geometry. Additionally, existing densification strategies simply clone and split Gaussian primitives from existing ones, incapable of reconstructing geometry from missing structures. To address these limitations, we propose VAD-GS, a 3DGS framework tailored for geometry recovery in challenging urban scenes. Our method identifies unreliable geometry structures via voxel-based visibility reasoning, selects informative supporting views through diversity-aware view selection, and recovers missing structures via multi-view stereo reconstruction. This design enables the generation of new Gaussian primitives guided by reliable geometric priors, even in regions lacking initial points. Extensive experiments on the Waymo and nuScenes datasets demonstrate that VAD-GS outperforms state-of-the-art 3DGS approaches and significantly improves the quality of reconstructed geometry for both static and dynamic objects.Source code will be released upon publication.
Uncertainty-guided Compositional Alignment with Part-to-Whole Semantic Representativeness in Hyperbolic Vision-Language Models
Authors: Hayeon Kim; Ji Ha Jang; Junghun James Kim; Se Young Chun
Venue: CVPR 2026 (main)

Abstract: While Vision-Language Models (VLMs) have achieved remarkable performance, their Euclidean embeddings remain limited in capturing hierarchical relationships such as part-to-whole or parent-child structures, and often face challenges in multi-object compositional scenarios. Hyperbolic VLMs mitigate this issue by better preserving hierarchical structures and modeling part-whole relations (i.e., whole scene and its part images) through entailment. However, existing approaches do not model that each part has a different level of semantic representativeness to the whole. We propose UNcertainty-guided Compositional Hyperbolic Alignment (UNCHA) for enhancing hyperbolic VLMs. UNCHA models part-to-whole semantic representativeness with hyperbolic uncertainty, by assigning lower uncertainty to more representative parts and higher uncertainty to less representative ones for the whole scene. This representativeness is then incorporated into the contrastive objective with uncertainty-guided weights. Finally, the uncertainty is further calibrated with an entailment loss regularized with entropy-based term. With the proposed losses, UNCHA learns hyperbolic embeddings with more accurate part-whole ordering, capturing the underlying compositional structure in an image and improving its understanding of complex multi-object scenes. UNCHA achieves state-of-the-art performance on zero-shot classification, retrieval, and multi-label classification benchmarks. Our code and models are available at: https://github.com/jeeit17/UNCHA.git.
Self-Critical Distillation Network for Video-based Commonsense Captioning
Authors: Mengqi Yuan; Gengyun Jia; Bing-Kun Bao
Venue: CVPR 2026 (main)

Abstract: Video-based commonsense captioning aims to generate captions for the video content while providing multiple commonsense about the underlying events. Existing approaches rely on constructing a "video -> content caption -> commonsense" reasoning chain, which generates visually ungrounded commonsense and neglects inter-category commonsense correlations. Firstly, the existing reasoning chain induces the model's excessive reliance on content caption when generating commonsense, resulting in generic outputs with limited visual relevance. Secondly, the reasoning chain adopts multiple isolated decoders for commonsense generation, which fails to leverage the correlations between different categories of commonsense. To address these limitations, we introduce a novel self-critical distillation network (SCD-Net), which optimizes the reasoning chain by enhancing visual reasoning and establishing inter-category commonsense correlations. Specifically, on the one hand, we introduce self-critical learning and design a reward function to allow the model to refine its output. This mechanism incentivizes the model to maximize the utilization of visual information, thus improving the model's capacity for visual comprehension. On the other hand, we propose a joint reasoning distillation framework that fosters mutual inference among diverse commonsense categories. In this framework, we incorporate the cascaded decoder and knowledge distillation strategy to facilitate inter-category commonsense knowledge transfer while maintaining the fairness of the testing. Our experiments on the large-scale Video-to-Commonsense dataset demonstrate that our approach performs favorably against state-of-the-art methods. The code will be released soon.
Block-Sparse Global Attention for Efficient Multi-View Geometry Transformers
Authors: Chung-Shien Brian Wang; Christian Schmidt; Jens Piekenbrinck; Bastian Leibe
Venue: CVPR 2026 (main)

Abstract: Efficient and accurate feed-forward multi-view reconstruction has long been an important task in computer vision. Recent transformer-based models like VGGT, \pi^3 and MapAnything have demonstrated remarkable performance with relatively simple architectures. However, their scalability is fundamentally constrained by the quadratic complexity of global attention, which imposes a significant runtime bottleneck when processing large image sets. In this work, we empirically analyze the global attention matrix of these models and observe that the probability mass concentrates on a small subset of patch-patch interactions corresponding to cross-view geometric correspondences. Building on this insight and inspired by recent advances in large language models, we propose a training-free, block-sparse replacement for dense global attention, implemented with highly optimized kernels. Our method accelerates inference by more than 3xwhile maintaining comparable task performance. Evaluations on a comprehensive suite of multi-view benchmarks demonstrate that our approach seamlessly integrates into existing global attention-based architectures such as VGGT, \pi^3, and MapAnything, while substantially improving scalability to large image collections.