GenAI4ELab/papercli-papers huggingface dataset:
Fusion of Depth and Semantics for Probabilistic Floorplan Localization
Authors: Kecheng Ye; Mao Chen; Xiangkai Zhang; Xu Yang
Venue: CVPR 2026 (main)

Abstract: Floorplan localization aims to estimate the camera pose of a query image with respect to a 2D floorplan, providing a lightweight and long-term stable alternative to localization based on 3D maps or large image databases for indoor robotics and AR. Recent methods frame the problem as ray-based matching, representing the image as a set of rays annotated with depth or semantic labels and aligning them with the floorplan. However, they still face challenges in addressing the complexity of indoor environments, which can be decomposed into environmental, geometric, and semantic ambiguities.To address these ambiguities, we propose a floorplan-aware probabilistic fusion framework that models both depth and semantic information within a unified architecture. Our framework also combines a distribution-based ray confidence estimator, which down-weights uncertain geometric hypotheses, with a probabilistic semantic matching scheme based on Jensen-Shannon divergence (JSD), which preserves and leverages informative semantic ambiguity instead of collapsing it into hard labels. Experiments on challenging benchmarks demonstrate that our approach significantly outperforms prior methods in both robustness and accuracy.
FVBench: Benchmarking Deepfake Video Detection Capability of Large Multimodal Models
Authors: Jiarui Wang; Huiyu Duan; Juntong Wang; Xiongkuo Min
Venue: CVPR 2026 (main)

Abstract: As generative models rapidly evolve, the realism of AI-generated videos has reached new levels, posing significant challenges for detecting the authenticity of videos. Existing deepfake detection techniques generally rely on training datasets with limited generation methods and content diversity, which limits their generalization ability on more realistic content, particularly that produced by the latest generative models. Recently, large multimodal models (LMMs) have demonstrated remarkable zero-shot performance across a variety of vision tasks. Yet, their ability to discern deepfake videos remains largely untested. To this end, we propose **FVBench**, a comprehensive deep\underline f ake \underline v ideo \underline bench mark designed to advance video deepfake detection. It includes: (i) extensive content diversity, with over 120K videos covering real, AI-edited, and fully AI-generated categories, (ii) comprehensive model coverage, with fake videos generated and edited by 42 of the state-of-the-art video synthesis and editing models, and (iii) deepfake video detection benchmark for LMMs, which is a comprehensive benchmark for exploring the deepfake video detection capabilities of LMMs. The dataset and code are released at https://github.com/IntMeGroup/FVBench.
FFP-300K: Scaling First-Frame Propagation for Generalizable Video Editing
Authors: Xijie Huang; Chengming Xu; Donghao Luo; Xiaobin Hu; Peng Tang; Xu Peng; Jiangning Zhang; Chengjie Wang; Yanwei Fu
Venue: CVPR 2026 (main)

Abstract: First-Frame Propagation (FFP) offers a promising paradigm for controllable video editing, but existing methods are hampered by a reliance on cumbersome run-time guidance. We identify the root cause of this limitation as the inadequacy of current training datasets, which are often too short, low-resolution, and lack the task diversity required to teach robust temporal priors. To address this foundational data gap, we first introduce FFP-300K, a new large-scale dataset comprising 300K high-fidelity video pairs at 720p resolution and 81 frames in length, constructed via a principled two-track pipeline for diverse local and global edits. Building on this dataset, we propose a novel framework designed for true guidance-free FFP that resolves the critical tension between maintaining first-frame appearance and preserving source video motion. Architecturally, we introduce Adaptive Spatio-Temporal RoPE (AST-RoPE), which dynamically remaps positional encodings to disentangle appearance and motion references. At the objective level, we employ a self-distillation strategy where an identity propagation task acts as a powerful regularizer, ensuring long-term temporal stability and preventing semantic drift. Comprehensive experiments on the EditVerseBench benchmark demonstrate that our method significantly outperforming existing academic and commercial models by receiving about 0.2 PickScore and 0.3 VLM score improvement against these competitors. The project is at ffp-300k.github.io.
OpenMarcie: Dataset for Multimodal Action Recognition in Industrial Environments
Authors: Hymalai Bello; Lala Ray; Joanna Sorysz; Sungho Suh; Paul Lukowicz
Venue: CVPR 2026 (main)

Abstract: Smart factories use advanced technologies to optimize production and increase efficiency. To this end, the recognition of worker activity allows for accurate quantification of performance metrics, improving efficiency holistically while contributing to worker safety. OpenMarcie is, to the best of our knowledge, the biggest multimodal dataset designed for human action monitoring in manufacturing environments. It includes data from wearables sensing modalities and cameras distributed in the surroundings. The dataset is structured around two experimental settings, involving a total of 36 participants. In the first setting, twelve participants perform a bicycle assembly and disassembly task under semi-realistic conditions without a fixed protocol, promoting divergent and goal-oriented problem-solving. The second experiment involves twenty-five volunteers (24 valid data) engaged in a 3D printer assembly task, with the 3D printer manufacturer's instructions provided to guide the volunteers in acquiring procedural knowledge. This setting also includes sequential collaborative assembly, where participants assess and correct each other's progress, reflecting real-world manufacturing dynamics. OpenMarcie includes over 37 hours of egocentric and exocentric, multimodal, and multipositional data, featuring eight distinct data types and more than 200 independent information channels. The dataset is benchmarked across three human activity recognition tasks: activity classification, open vocabulary captioning, and cross-modal alignment.
MTA: Multimodal Task Alignment for BEV Perception and Captioning
Authors: Yunsheng Ma; Burhaneddin Yaman; Xin Ye; Jingru Luo; Feng Tao; Abhirup Mallik; Ziran Wang; Liu Ren
Venue: CVPR 2026 (main)

Abstract: Bird's eye view (BEV)-based 3D perception plays a crucial role in autonomous driving applications. The rise of large language models has spurred interest in BEV-based captioning to understand object behavior in the surrounding environment. However, existing approaches treat perception and captioning as separate tasks, focusing on the performance of only one task and overlooking the potential benefits of multimodal alignment. To bridge this gap between modalities, we introduce MTA, a novel multimodal task alignment framework that boosts both BEV perception and captioning. MTA consists of two key components: (1) BEV-Language Alignment (BLA), a contextual learning mechanism that aligns the BEV scene representations with ground-truth language representations, and (2) Detection-Captioning Alignment (DCA), a cross-modal prompting mechanism that aligns detection and captioning outputs. MTA seamlessly integrates into state-of-the-art baselines during training, adding no extra computational complexity at runtime. Extensive experiments on the nuScenes and TOD3Cap datasets show that MTA significantly outperforms state-of-the-art baselines in both tasks, achieving a 10.7% improvement in challenging rare perception scenarios and a 9.2% improvement in captioning. These results underscore the effectiveness of unified alignment in reconciling BEV-based perception and captioning.
GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation
Authors: Ken Deng; Yunhan Yang; Jingxiang Sun; Xihui Liu; Yebin Liu; Ding Liang; Yan-Pei Cao
Venue: CVPR 2026 (main)

Abstract: We introduce GeoSAM2, a prompt-controllable framework for 3D part segmentation that casts the task as multi-view 2D mask prediction. Given a textureless object, we render normal and point maps from predefined viewpoints and accept simple 2D prompts--clicks or boxes--to guide part selection. These prompts are processed by a shared SAM2 backbone augmented with LoRA and residual geometry fusion, enabling view-specific reasoning while preserving pretrained priors. The predicted masks are back-projected to the object, aggregated across views.Our method enables fine-grained, part-specific control without requiring text prompts, per-shape optimization, or full 3D labels. In contrast to global clustering or scale-based methods, prompts are explicit, spatially grounded, and interpretable. We achieve state-of-the-art class-agnostic performance on PartObjaverse-Tiny and PartNetE, outperforming both slow optimization-based pipelines and fast but coarse feedforward approaches. Our results highlight a new paradigm: aligning the paradigm of 3D segmentation with SAM2, leveraging interactive 2D inputs to unlock controllability and precision in object-level part understanding.
Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation
Authors: Taekyung Ki; Sangwon Jang; Jaehyeong Jo; Jaehong Yoon; Sung Ju Hwang
Venue: CVPR 2026 (main)

Abstract: Talking head generation creates lifelike avatars from static portraits for virtual communication and content creation. However, current models do not yet convey the feeling of truly interactive communication, often generating one-way responses that lack emotional engagement. We identify two key challenges toward truly interactive avatars: generating motion in real-time under causal constraints and learning expressive, vibrant reactions without additional labeled data. To address these challenges, we propose Avatar Forcing, a new framework for interactive head avatar generation that models real-time user-avatar interactions through diffusion forcing. This design allows the avatar to process real-time multimodal inputs, including the user's audio and motion, with low latency for instant reactions to both verbal and non-verbal cues such as speech, nods, and laughter. Furthermore, we introduce a direct preference optimization method that leverages synthetic losing samples constructed by dropping user conditions, enabling label-free learning of expressive interaction. Experimental results demonstrate that our framework enables real-time interaction with low latency (about 500ms), achieving 6.8x speedup compared to the baseline, and produces reactive and expressive avatar motion, which is preferred over 80% against the baseline.
Omni-Attack: Adversarial Attacks on Open-Ended VQA in Black-Box Multimodal LLMs
Authors: Kai Hu; Weichen Yu; Li Zhang; Alexander Robey; Andy Zou; Haoqi Hu; Chengming Xu; Matt Fredrikson
Venue: CVPR 2026 (main)

Abstract: Multimodal large language models (MLLMs) have achieved remarkable success across diverse applications, from autonomous driving to document understanding. As these models are deployed in safety-critical contexts, understanding their adversarial robustness becomes crucial. However, current evaluations focus primarily on simple tasks like coarse-grained classification, and employ inconsistent evaluation protocols, hindering rigorous comparison of attack methods. We introduce AdvRobustBench, a comprehensive adversarial robustness benchmark for MLLMs comprising 1,000 examples across visual question answering (VQA) and optical character recognition (OCR) tasks, drawn from widely-used MLLM benchmarks (MMBench, MMStar, OCRBench-v2). We further propose Omni-Attack, a novel transfer-based black-box attack method that addresses key challenges in attacking open-ended question-answering systems. Our approach introduces (i) a target-construction pipeline that generates question-conditioned textual and visual targets to provide stronger optimization signals, and (ii) a location-aware attack strategy for OCR that enables spatially-precise perturbations. Extensive experiments demonstrate that Omni-Attack achieves strong targeted attack success rates (up to 71.8% on GPT-4.1 at \varepsilon=8/255) across both proprietary models (GPT-4.1, Claude 3.7, Gemini 2.0) and open-source MLLMs, revealing significant vulnerabilities in current multimodal systems. Our benchmark and findings establish a foundation for developing more robust MLLMs. Codes are available at https://github.com/hukkai/transferable_mllm_attack
Time Without Time: Pseudo-Temporal Representation for Space-Time Super-Resolution
Authors: Hee Min Choi; Hyoa Kang; Suji Kim; Dokwan Oh; Nam Ik Cho
Venue: CVPR 2026 (main)

Abstract: Space-time video super-resolution (STVSR) is a task aimed at simultaneously upsampling a video in both spatial and temporal dimensions. Previous studies on STVSR have primarily focused on task-specific architectures and modeling paradigms, while effective pretraining strategies remain underexplored. In this paper, we propose a pseudo-temporal space-time reconstruction pretraining framework for STVSR networks that enables effective use of image datasets, which naturally provide strong spatial cues. Each training sample is constructed by duplicating a single image into a pseudo-temporal video and independently zero-filling random pixel regions across its frames. Instead of designing a separate pretraining module, we pretrain the STVSR network on a task aligned with its core objectives of spatial restoration and cross-frame aggregation. The model learns to reconstruct clean, higher-spatio-temporal-resolution outputs from degraded, pseudo-temporal inputs, with a modulation factor encouraging greater focus on difficult regions. Extensive experiments show that our simple pretraining significantly improves STVSR performance and outperforms existing video representation learning approaches. We note our method is effective even when pretraining and finetuning with a limited quantity of data.
Unsafe2Safe: Controllable Image Anonymization for Downstream Utility
Authors: Minh Dinh; SouYoung Jin
Venue: CVPR 2026 (main)

Abstract: Large-scale image datasets frequently contain identifiable or sensitive content, raising privacy risks when training models that may memorize and leak such information. We present Unsafe2Safe, a fully automated pipeline that detects privacy-prone images and rewrites only their sensitive regions using multimodally guided diffusion editing. Unsafe2Safe operates in two stages. Stage 1 uses a vision-language model to (i) inspect images for privacy risks, (ii) generate paired private and public captions that respectively include and omit sensitive attributes, and (iii) prompt a large language model to produce structured, identity-neutral edit instructions conditioned on the public caption. Stage 2 employs instruction-driven diffusion editors to apply these dual textual prompts, producing privacy-safe images that preserve global structure and task-relevant semantics while neutralizing private content. To measure anonymization quality, we introduce a unified evaluation suite covering Quality, Cheating, Privacy, and Utility dimensions. Across MS-COCO, Caltech101 and MIT Indoor67, Unsafe2Safe reduces face similarity, text similarity, and demographic predictability by large margins, while maintaining downstream model accuracy comparable to training on raw data. Fine-tuning diffusion editors on our automatically generated triplets (private caption, public caption, edit instruction) further improves both privacy protection and semantic fidelity. Unsafe2Safe provides a scalable, principled solution for constructing large, privacy-safe datasets without sacrificing visual consistency or downstream utility.
No Way To Steal My Face: Proactive Defense Against Identity-Preserving Personalized Generation
Authors: Lizhi Xiong; Jun Li; Ziqiang Li; Weiwei Jiang; Zhangjie Fu
Venue: CVPR 2026 (main)

Abstract: Recent advances in diffusion models have enabled high-fidelity, identity-preserving image generation for personalized applications such as digital avatars and virtual try-on systems. However, their reliance on sensitive facial reference images raises growing privacy concerns. Existing defense mechanisms are primarily designed for training-based personalization and struggle to generalize to emerging training-free approaches, due to fundamental differences in their identity integration paradigms. To bridge this gap, we propose IDGuardian--the first generalizable and model-agnostic identity protection framework capable of defending against both training-based and training-free personalization methods. IDGuardian abstracts the personalization process into two critical stages: identity extraction and identity injection. It then introduces crafted adversarial perturbations to simultaneously disrupt both stages. Specifically, it degrades the identity features extracted by external encoders and establishes an adversarial conceptual bridge that misdirects the generative trajectory away from the target identity. Extensive experiments show that IDGuardian effectively protects identity across various personalization pipelines and model architectures, while remaining robust to post-processing, adaptive attacks, and cross-dataset generalization.
From Intuition to Investigation: A Tool-Augmented Reasoning MLLM Framework for Generalizable Face Anti-Spoofing
Authors: Haoyuan Zhang; Keyao Wang; Guosheng Zhang; Haixiao Yue; Zhiwen Tan; Siran Peng; Tianshuo Zhang; Xiao Tan; Kunbin Chen; Wei He; Jingdong Wang; Ajian Liu; Xiangyu Zhu; Zhen Lei
Venue: CVPR 2026 (main)

Abstract: Face recognition remains vulnerable to presentation attacks, calling for robust Face Anti-Spoofing (FAS) solutions. Recent MLLM-based FAS methods reformulate the binary classification task as the generation of brief textual descriptions to improve cross-domain generalization. However, their generalizability is still limited, as such descriptions mainly capture intuitive semantic cues (e.g., mask contours) while struggling to perceive fine-grained visual patterns. To address this limitation, we incorporate external visual tools into MLLMs to encourage deeper investigation of subtle spoof clues. Specifically, we propose the Tool-Augmented Reasoning FAS (TAR-FAS) framework, which reformulates the FAS task as a Chain-of-Thought with Visual Tools (CoT-VT) paradigm, allowing MLLMs to begin with intuitive observations and adaptively invoke external visual tools for fine-grained investigation. To this end, we design a tool-augmented data annotation pipeline and construct the ToolFAS-16K dataset, which contains multi-turn tool-use reasoning trajectories. Furthermore, we introduce a tool-aware FAS training pipeline, where Diverse-Tool Group Relative Policy Optimization (DT-GRPO) enables the model to autonomously learn efficient tool use. Extensive experiments under a challenging one-to-eleven cross-domain protocol demonstrate that TAR-FAS achieves SOTA performance while providing fine-grained visual investigation for trustworthy spoof detection.
GaussianVision: Vision-Language Alignment from Compressed Image Representations using 2D Gaussian Splatting
Authors: Yasmine Omri; Connor Ding; Tsachy Weissman; Thierry Tambe
Venue: CVPR 2026 (main)

Abstract: Modern vision-language pipelines are driven by RGB vision encoders trained on massive image-text corpora. While these pipelines have enabled impressive zero-shot capabilities and strong transfer across tasks, they still inherit two structural inefficiencies from the pixel domain: (i) transmitting dense RGB images from edge devices to the cloud is energy-intensive and costly, and (ii) patch-based tokenization explodes sequence length, stressing attention budgets and context limits. We explore 2D Gaussian Splatting (2DGS) as an alternative visual substrate for alignment: a compact, spatially adaptive representation that parameterizes images by a set of colored anisotropic Gaussians. We develop a scalable 2DGS pipeline with structured initialization, luminance-aware pruning, and batched CUDA kernels, achieving over 90x faster fitting and   97% GPU utilization compared to prior implementations. We further adapt contrastive language-image pre-training (CLIP) to 2DGS by reusing a frozen RGB-based transformer backbone with a lightweight splat-aware input stem and a perceiver resampler, training only   9.7 - 13.8% of the total parameters. On a 12.8M dataset from DataComp, GS encoders yield competitive zero-shot performance on 38 datasets from the CLIP benchmark while compressing inputs 3-23.5x relative to pixels. When integrated into a full end-to-end LLaVA-style visual-language model (VLM), GS encoders consistently outperform the RGB VLM baseline across six VQA benchmarks, often with reduced token counts, demonstrating that a transmission-efficient, structured Gaussian representation can match or surpass pixel-based models in downstream multimodal reasoning. Our codebase can be found at https://github.com/ Tambe-Lab/GaussianVision.
Learning 3D Reconstruction with Priors in Test Time
Authors: Lei Zhou; Haoyu Wu; Akshat Dave; Dimitris Samaras
Venue: CVPR 2026 (main)

Abstract: We introduce a test-time framework for multiview Transformers (MVTs) that incorporates priors (e.g., camera poses, intrinsics, and depth) to improve 3D tasks without retraining or modifying pre-trained image-only networks. Rather than feeding priors into the architecture, we cast them as constraints on the predictions and optimize the network at inference time. The optimization loss consists of a self-supervised objective and prior penalty terms. The self-supervised objective captures the compatibility among multi-view predictions and is implemented using photometric or geometric loss between renderings from other views and each view itself. Any available priors are converted into penalty terms on the corresponding output modalities. Across a series of 3D vision benchmarks, including point map estimation and camera pose estimation, our method consistently improves performance over base MVTs by a large margin. On the ETH3D, 7-Scenes, and NRGBD datasets, our method reduces the point-map distance error by more than half compared with the base image-only models. Our method also outperforms retrained prior-aware feed-forward methods, demonstrating the effectiveness of our test-time constrained optimization (TCO) framework for incorporating priors into 3D vision tasks. The code is available at https://github.com/cvlab-stonybrook/TCO.
Domain Sensitive Federated Learning with Fisher-Informed Pruning
Authors: Chenchen Lin; Wenhao Yuan; Zhengji Xu; Xuehe Wang
Venue: CVPR 2026 (main)

Abstract: Federated Learning (FL) serves as a prominent distributed training paradigm, enabling devices to collaboratively train a shared model with local data. However, in practice, clients generally possess data from multiple distributional domains, posing significant challenges to efficiency and generalization. In this paper, we propose a domain-sensitive federated pruning framework, FedFIP, that preserves domain-invariant structures while retaining domain-specific representations. The Domain-Sensitive Fisher Pruning (DSFP) module estimates channel importance per domain via Fisher information, and uploads this signal to the server to obtain a globally shared pruning mask. Given local domain heterogeneity, each client reuses its Fisher information to selectively reactivate domain-specific channels, yielding personalized sparse models that remain structurally aligned yet adapt to local heterogeneity. To further enhance performance, we adopt a Domain-Sensitive Regularization (DSR) module: the server builds domain prototypes from uploaded importance signals and broadcasts them back. Guided by the domain prototypes, we adopt a structure-contrastive loss to strengthen intra-domain consistency and inter-domain discriminability. We propose a structure-aware aggregation algorithm that fuses heterogeneous personalized architectures into a domain-generalized global model. Experiments on multi-domain benchmarks demonstrate that FedFIP surpasses state-of-the-art FL baselines while substantially shrinking model size.