GenAI4ELab/papercli-papers huggingface dataset:
Diffusion-Based sRGB Real Noise Generation via Prompt-Driven Noise Representation Learning
Authors: Jaekyun Ko; Dongjin Kim; Soomin Lee; Guanghui Wang; Tae Hyun Kim
Venue: CVPR 2026 (main)

Abstract: Denoising in the sRGB image space is challenging due to large noise variability. Although end-to-end methods perform well, their effectiveness in real-world scenarios is limited by the scarcity of real noisy-clean image pairs, which are expensive and difficult to collect. To address this limitation, several generative methods have been developed to synthesize realistic noisy images from limited data. These approaches often rely on camera metadata during both training and testing to synthesize real-world noise. However, the lack of metadata or inconsistencies between devices restricts their usability. Therefore, we propose a novel framework called Prompt-Driven Noise Generation (PNG). This model is capable of acquiring high-dimensional prompt features that capture the characteristics of real-world input noise and creating a variety of realistic noisy images consistent with the distribution of the input noise. By eliminating the dependency on explicit camera metadata, our approach significantly enhances the generalizability and applicability of noise synthesis. Comprehensive experiments reveal that our model effectively produces realistic noisy images and show the successful application of these generated images in removing real-world noise across various benchmark datasets.
DiGraphHal-Bench: Evaluating Multimodal Large Language Models on Complex Directed Graphs
Authors: Yixin Fan; Zhao He; Yuxin Hou; Changhua Zhou; Zihao Liu; Peng Wang; Chenglong Lu; Xu Zhang; Wei Wang
Venue: CVPR 2026 (main)

Abstract: While prior research on Multimodal Large Language Model (MLLM) hallucinations has primarily examined cross-modal inconsistencies in natural images, hallucination over complex graph structures remains underexplored.Concurrently, there is a lack of robust evaluation for fine-grained reasoning integrating structural, visual, and semantic information.To address these gaps, we present DiGraphHal-Bench, the first large-scale Visual Question Answering (VQA) benchmark for evaluating both hallucination phenomena and fine-grained reasoning of MLLMs on real-world directed graphs. DiGraphHal-Bench comprises high-quality procedural graphs from over six distinct domains and is organized around a taxonomy of four high-level capabilities and twelve fine-grained tasks. To ensure benchmark fidelity, we propose a novel two-stage automatic data curation pipeline that reconciles the trade-off between data scale and quality, thereby guaranteeing reliable evaluation.Experiments reveal that state-of-the-art MLLMs hallucinate notably in fine-grained graph reasoning. Although SFT substantially mitigates these hallucinations and strengthens complex reasoning, performance remains far from optimal. Ablation studies highlight the importance of fundamental capabilities for integrative reasoning, and our benchmark provides a foundation for advancing robust multi-modal graph understanding.
Talking Together: Synthesizing Co-Located 3D Conversations from Audio
Authors: Mengyi Shan; Shouchieh Chang; Ziqian Bai; Shichen Liu; Yinda Zhang; Luchuan Song; Rohit Pandey; Sean Fanello; Zeng Huang
Venue: CVPR 2026 (main)

Abstract: We tackle the challenging task of generating complete 3D facial animations for two interacting, co-located participants from a mixed audio stream. While existing methods often produce disembodied "talking heads" akin to a video conference call, our work is the first to explicitly model the dynamic 3D spatial relationship--including relative position, orientation, and mutual gaze--that is crucial for realistic in-person dialogues. Our system synthesizes the full performance of both individuals, including precise lip-sync, and uniquely allows their relative head poses to be controlled via textual descriptions. To achieve this, we propose a dual-stream architecture where each stream is responsible for one participant's output. We employ speaker's role embeddings and inter-speaker cross-attention mechanisms are designed to disentangle the mixed audio and model the interaction. Furthermore, we introduce a novel eye gaze loss to promote natural, mutual eye contact. To power our data-hungry approach, we introduce a novel pipeline to curate a large-scale conversational dataset consisting of over 2 million dyadic pairs from in-the-wild videos. Our method generates fluid, controllable, and spatially aware dyadic animations suitable for immersive applications in VR and telepresence, significantly outperforming existing baselines in perceived realism and interaction coherence.
MERG3R: A Divide-and-Conquer Approach to Large-Scale Neural Visual Geometry
Authors: Leo Kaixuan Cheng; Abdus Shaikh; Ruofan Liang; Zhijie Wu; Yushi Guan; Nandita Vijaykumar
Venue: CVPR 2026 (main)

Abstract: Recent advancements in neural visual geometry, including transformer-based models such as VGGT and Pi3, have achieved impressive accuracy on 3D reconstruction tasks. However, their reliance on full attention makes them fundamentally limited by GPU memory capacity, preventing them from scaling to large, unordered image collections. We introduce MERG3R, a training-free divide-and-conquer framework that enables geometric foundation models to operate far beyond their native memory limits. MERG3R first reorders and partitions unordered images into overlapping, geometrically diverse subsets that can be reconstructed independently. It then merges the resulting local reconstructions through an efficient global alignment and confidence-weighted bundle adjustment procedure, producing a globally consistent 3D model. Our framework is model-agnostic and can be paired with existing neural geometry models. Across large-scale datasets--including 7-Scenes, NRGBD, Tanks & Temples, and Cambridge Landmarks--MERG3R consistently improves reconstruction accuracy, memory efficiency, and scalability, enabling high-quality reconstruction when the dataset exceeds memory capacity limits.
Alert-CLIP: Abnormality-aware Latent-Enhanced Representation Tuning of CLIP for Video Anomaly Detection
Authors: Yiyan Zhu; Menghao Zhang; Haifeng Sun; Pengfei Ren; Xianao Chu; Chenye Xu; Hong Tan; Jinghan Wang; Qi Qi; Jingyu Wang
Venue: CVPR 2026 (main)

Abstract: With the rise of pre-trained vision-language models such as CLIP, performing video anomaly detection (VAD) through cross-modal reasoning has become an emerging trend. However, we observe that CLIP still suffers from weak abnormality awareness: normal and abnormal descriptions are highly entangled in the text embedding space, causing video features to assign nearly indistinguishable similarity scores to both types of prompts. To address this issue, we propose Alert-CLIP, an abnormality-aware latent-enhanced tuning framework that tailors CLIP for VAD. Alert-CLIP introduces a multi-level alignment strategy: (1) video-label alignment, which reshapes the semantic space to establish a coarse-level foundation for abnormality awareness; (2) region-text alignment, which explicitly associates anomaly-related regions with their detailed descriptions to strengthen fine-grained perception; and (3) region-semantic alignment, which further contrasts anomalous regions against multiple hard negative samples, enhancing abnormality-aware discrimination.Extensive experiments on four benchmarks demonstrate that Alert-CLIP consistently surpasses vanilla CLIP across supervised, zero-shot, and open-vocabulary settings, providing a solid foundation for future CLIP-based VAD research.
Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting
Authors: Shuangkang Fang; I-Chao Shen; Xuanyang Zhang; Zesheng Wang; Yufeng Wang; Wenrui Ding; Gang YU; Takeo Igarashi
Venue: CVPR 2026 (main)

Abstract: Recent 3D Gaussian Splatting (3DGS) dropout methods address overfitting under sparse-view conditions by randomly nullifying Gaussian opacities. However, we identify a neighbor compensation effect in these approaches: dropped Gaussians are often compensated by their neighbors, weakening the intended regularization. Moreover, these methods overlook the contribution of high-degree spherical harmonic coefficients (SH) to overfitting. To address these issues, we propose DropAnSH-GS, a novel anchor-based dropout strategy. Rather than dropping Gaussians independently, our method randomly selects certain Gaussians as anchors and simultaneously removes their spatial neighbors. This effectively disrupts local redundancies and encourages the model to learn more robust, globally informed representations. Furthermore, we extend the dropout to color attributes by randomly dropping higher-degree SH coefficients to concentrate appearance information in lower-degree SH. This strategy further mitigates overfitting and enables flexible post-training model compression via SH truncation. Experimental results demonstrate that DropAnSH-GS substantially outperforms existing dropout methods with negligible computational overhead and can be readily integrated into various 3DGS variants to enhance their performances.
Neural-Centric Video Processing Pipeline for Unified Multi-Task Inference
Authors: Seyeon Lee; Juncheol Ye; Jaehong Kim; Dongsu Han
Venue: CVPR 2026 (main)

Abstract: Videos are increasingly used as inputs to machine learning systems, where repeated decoding and processing across diverse downstream tasks dominate computational cost. However, existing video pipelines remain inefficient. Traditional codecs such as H.264 and H.265 are optimized for human perception and require full pixel decoding for every query, compressed-domain methods are tied to specific codec structures with limited flexibility, and machine-oriented video coding approaches often rely on task-specific encoders and separate representations without supporting human visualization. We propose Neural Video Pipeline (NVP), a framework that leverages implicit neural representations to directly extract task-specific features from intermediate layers, eliminating pixel reconstruction overhead. NVP introduces lightweight micro adapters that map these features into the representation space of downstream models, bypassing both decoding and early-stage feature extraction. Across four representative tasks--image classification, object detection, action recognition, and segmentation--NVP reduces latency by up to 89.5% and inference FLOPs by up to 29.9%, while supporting multiple tasks using a single unified representation.
MICo-150K: A Comprehensive Dataset Advancing Multi-Image Composition
Authors: Xinyu Wei; Kangrui Cen; Hongyang Wei; Zhen Guo; Bairui Li; Zeqing Wang; Jinrui Zhang; Lei Zhang
Venue: CVPR 2026 (main)

Abstract: In controllable image generation, synthesizing coherent and consistent images from multiple reference inputs, i.e., **Multi-Image Composition** (MICo), remains a challenging problem, partly hindered by the lack of high-quality training data.To bridge this gap, we conduct a systematic study of MICo, categorizing it into 7 representative tasks and curate a large-scale collection of high-quality source images and construct diverse MICo prompts.Leveraging powerful proprietary models, we synthesize a rich amount of balanced composite images, followed by human-in-the-loop filtering and refinement, resulting in **MICo-150K**, a comprehensive dataset for MICo with identity consistency.We further build a Decomposition-and-Recomposition (**De&Re**) subset, where 11K real-world complex images are decomposed into components and recomposed, enabling both real and synthetic compositions.To enable comprehensive evaluation, we construct **MICo-Bench** with 100 cases per task and 300 challenging De&Re cases, and further introduce a new metric, **Weighted-Ref-VIEScore**, specifically tailored for MICo evaluation.Finally, we fine-tune multiple models on**MICo-150K** and evaluate them on **MICo-Bench**. The results show that MICo-150K effectively equips models without MICo capability and further enhances those with existing skills.Notably, **Qwen-MICo**, fine-tuned from Qwen-Image-Edit, matches **Qwen-Image-2509** in 3-image composition while supporting arbitrary multi-image inputs beyond the latter's limitation.Our dataset and benchmark will be valuable resources for advancing MICo research.
Scalable Multi-View Subspace Clustering with Tensorized Anchor Guidance
Authors: Miao Jia; Xingchen Hu; Jiyuan Liu; Siwei Wang; Min Wang; Zijian Chen
Venue: CVPR 2026 (main)

Abstract: Anchor-based multi-view clustering methods have gained significant attention for their effectiveness in handling large-scale datasets in recent years. The performance of these methods is highly dependent on anchor quality. However, current methods neglect the interactive relationships among cross-view anchors, failing to effectively discover and exploit consistent and complementary information, leading to noisy or suboptimal anchor representations. In this paper, we propose a novel scalable multi-view subspace clustering method with tensorized anchor guidance, which directly couples anchors across views to improve clustering performance. Specifically, we construct a third-order anchor tensor from view-specific anchors in a low-dimensional latent space. By imposing a tensor Schatten p-norm constraint on the anchor tensor, we can explicitly capture cross-view low-rank structure and jointly exploit consistent and complementary information among anchors. Moreover, the tensorized anchor regularizer is independent of the number of samples, which reduces both time and space complexity.  Experimental results on seven datasets demonstrate that SMVS-TAG achieves superior effectiveness and stability compared to state-of-the-art large-scale MVC methods. Our code is available at https://github.com/Jiamiao2024/SMVS-TAG.
MedTVT-R1: A Multimodal LLM Empowering Medical Reasoning and Diagnosis
Authors: Yuting Zhang; Kaishen Yuan; Hao Lu; Yutao Yue; Jintai Chen; Kaishun Wu
Venue: CVPR 2026 (main)

Abstract: Accurate and interpretable multi-disease diagnosis remains a critical challenge in medical research, particularly when leveraging heterogeneous multimodal medical data. Current approaches often rely on single-modal data, limiting their ability to comprehensively understand complex diseases. To address this, we propose MedTVT-R1, a novel Multimodal Large Language Model (MLLM) framework designed to integrate clinical multimodal data for reasoning and diagnosing multiple diseases. We construct MedTVT-QA, a curated instruction dataset that provides question-answer pairs for physiological-level interpretations and disease-level diagnoses with a Chain of Evidence approach. MedTVT-R1 incorporates a modality perception layer to capture inter-modal dependencies and adaptively weight modality contributions. Additionally, we employ Group Relative Policy Optimization (GRPO)-based Reinforcement Fine-Tuning with a Jaccard Reward function to enhance diagnostic reasoning. Experimental results demonstrate MedTVT-R1's superiority in multimodal feature utilization and multi-disease diagnosis, offering significant potential for clinical applications such as diagnostic report generation and comorbidity reasoning. The dataset and code will be available on GitHub.
Good Can Sometimes be Bad: A Unified Attack against 3D Point Cloud Classifier by a Flexible Isotropic Resampling
Authors: Linkun Fan; Jiahao Zhang; Juntao Zhang; Lei Zhang; Fazhi He; Daojun Han
Venue: CVPR 2026 (main)

Abstract: To ensure the robustness of 3D point cloud Deep Neural Network(3D DNN), 3D adversarial attack targeting the inference stage and backdoor attack targeting the training stage are well studied. The success of both attacks usually requires a specified permissions that attacker must have. However, the obtainable permissions are uncertain due to the deployment environment changes in practical scenarios. This renders existing separately designed adversarial attack or backdoor attack ineffective. To solve this issue, this paper proposes a unified attack that can adapt to both 3D point cloud backdoor attack and adversarial attack, named UAtt3D. Furthermore, by observing existing attacks, their way to promise attack stealthiness is to limit the undesirable perturbation. This strategy requires moving the point position as little as possible, which restricts the attack intensity and is not suitable for our unified attack. Meanwhile, this strategy will inevitably cause a quality decrease on 3D point cloud due to the remaining malicious perturbation. Therefore, our UAtt3D explores a new avenue to guarantee attack stealthiness which improves the quality of attacked 3D point cloud rather than decreasing it. In detail, to simultaneously consider feature movement of adversarial attack and backdoor feature learning of backdoor attack, a flexible isotropic resampling is designed. It realigns the position of most points based on surface approximation and rays sampling. By fine tuning the resampled point cloud, adversarial point cloud and backdoored point cloud are obtained. Several experiments suggest that the proposed UAtt3D achieves outstanding stealthiness comparing with existing adversarial attacks and backdoor attacks from the subjective and objective perspective. Meanwhile, its attack efficiency is competitive.
CryoHype: Reconstructing a thousand cryo-EM structures with transformer-based hypernetworks
Authors: Jeffrey Gu; Minkyu Jeon; Ambri Ma; Serena Yeung-Levy; Ellen D. Zhong
Venue: CVPR 2026 (main)

Abstract: Cryo-electron microscopy (cryo-EM) is an indispensable technique for determining the 3D structures of dynamic biomolecular complexes. While typically applied to image a single molecular species, cryo-EM holds great potential for structure determination of many targets simultaneously in a high-throughput fashion. However, existing methods typically focus on modeling conformational heterogeneity within a single or very few structures and are not designed to resolve compositional heterogeneity arising from mixtures of many distinct molecular species. To address this challenge, we propose CryoHype, a transformer-based hypernetwork for cryo-EM reconstruction that dynamically adjusts the weights of an implicit neural representation based on the input structure. Using CryoHype, we successfully reconstruct 1,000 distinct structures from cryo-EM imaging in the fixed-pose setting without any pre-existing knowledge of the structures present, which is beyond the capabilities of any existing algorithm.
Image-Guided Geometric Stylization of 3D Meshes
Authors: Changwoon Choi; Hyunsoo Lee; Clément Jambon; Yael Vinker; Young Min Kim
Venue: CVPR 2026 (main)

Abstract: Recent generative models can create visually plausible 3D representations of objects. However, the generation process often allows for implicit control signals, such as contextual descriptions, and rarely supports bold geometric distortions beyond existing data distributions. We propose a geometric stylization framework that deforms a 3D mesh, allowing it to express the style of an image. While style is inherently ambiguous, we utilize pre-trained diffusion models to extract an abstract representation of the provided image. Our coarse-to-fine stylization pipeline can drastically deform the input 3D model to express a diverse range of geometric variations while retaining the valid topology of the original mesh and part-level semantics. We also propose an approximate VAE encoder that provides efficient and reliable gradients from mesh renderings. Extensive experiments demonstrate that our method can create stylized 3D meshes that reflect unique geometric features of the pictured assets, such as expressive poses and silhouettes, thereby supporting the creation of distinctive artistic 3D creations. Project page: https://changwoonchoi.github.io/GeoStyle
PHANTOM: Physics-Infused Video Generation via Joint Modeling of Visual and Latent Physical Dynamics
Authors: Ying Shen; Jerry Xiong; Tianjiao Yu; Ismini Lourentzou
Venue: CVPR 2026 (main)

Abstract: Recent advances in generative video modeling, driven by large-scale datasets and powerful architectures, have yielded remarkable visual realism. However, emerging evidence suggests that simply scaling data and model size does not endow these systems with an understanding of the underlying physical laws that govern real-world dynamics. Existing approaches often fail to capture or enforce such physical consistency, resulting in unrealistic motion and dynamics.In this work, we investigate whether integrating the inference of latent physical properties directly into the video generation process can equip models with the ability to produce physically plausible videos. To this end, we propose PHANTOM, a Physics-Infused Video Generation model that jointly models the visual content and latent physical dynamics. Conditioned on observed video frames and inferred physical states, PHANTOM jointly predicts latent physical dynamics and generates future video frames.PHANTOM leverages a physics-aware video representation that serves as an abstract yet informative embedding of the underlying physics, facilitating the joint prediction of physical dynamics alongside video content without requiring an explicit specification of a complex set of physical dynamics and properties. By integrating the inference of physical-aware video representation directly into the video generation process, PHANTOM produces video sequences that are both visually realistic and physically consistent.Quantitative and qualitative results on both standard video generation and physics-aware benchmarks demonstrate that PHANTOM not only outperforms existing methods in terms of adherence to physical dynamics but also delivers competitive perceptual fidelity.
Learning Multi-View Spatial Reasoning from Cross-View Relations
Authors: Suchae Jeong; Jaehwi Song; Haeone Lee; Hanna Kim; Jian Kim; Dongjun Lee; Dong Kyu Shin; Changyeon Kim; Dongyoon Hahm; Woogyeol Jin; Juheon Choi; Kimin Lee
Venue: CVPR 2026 (main)

Abstract: Vision-language models (VLMs) have achieved impressive results on single-view vision tasks, but lack the multi-view spatial reasoning capabilities essential for embodied AI systems to understand 3D environments and manipulate objects across different viewpoints. In this work, we introduce Cross-View Relations (XVR), a large-scale dataset designed to teach VLMs spatial reasoning across multiple views. XVR comprises 100K vision-question-answer samples derived from 18K diverse 3D scenes and 70K robotic manipulation trajectories, spanning three fundamental spatial reasoning tasks: Correspondence (matching objects across views), Verification (validating spatial relationships), and Localization (identifying object positions). VLMs fine-tuned on XVR achieve substantial improvements on established multi-view and robotic spatial reasoning benchmarks (MindCube and RoboSpatial). When integrated as backbones in Vision-Language-Action models, XVR-trained representations improve success rates on RoboCasa. Our results demonstrate that explicit training on cross-view spatial relations significantly enhances multi-view reasoning and transfers effectively to real-world robotic manipulation.