GenAI4ELab/papercli-papers huggingface dataset:
Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning
Authors: Zhiyuan Yan; Yandan Zhao; Shen Chen; Mingyi Guo; Xinghe Fu; Taiping Yao; Shouhong Ding; Yunsheng Wu; Li Yuan
Venue: CVPR 2025 (main)

Abstract: Three key challenges hinder the development of current deepfake video detection: (1) Temporal features can be complex and diverse: how can we identify general temporal artifacts to enhance model generalization? (2) Spatiotemporal models often lean heavily on one type of artifact and ignore the other: how can we ensure balanced learning from both? (3) Videos are naturally resource-intensive: how can we tackle efficiency without compromising accuracy? This paper attempts to tackle the three challenges jointly. First, inspired by the notable generality of using image-level blending data for image forgery detection, we investigate whether and how video-level blending can be effective in video. We then perform a thorough analysis and identify a previously underexplored temporal forgery artifact: Facial Feature Drift (FFD), which commonly exists across different forgeries. To reproduce FFD, we then propose a novel Video-level Blending data (VB), where VB is implemented by blending the original image and its warped version frame-by-frame, serving as a hard negative sample to mine more general artifacts. Second, we carefully design a lightweight Spatiotemporal Adapter (StA) to equip a pre-trained image model with the ability to capture both spatial and temporal features jointly and efficiently. StA is designed with two-stream 3D-Conv with varying kernel sizes, allowing it to process spatial and temporal features separately. This eliminates the need to design a new deepfake-specific video architecture from scratch. Extensive experiments validate the effectiveness of the proposed methods; and show our approach can generalize well to previously unseen forgery videos.
ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning
Authors: Kailin Li; Puhao Li; Tengyu Liu; Yuyang Li; Siyuan Huang
Venue: CVPR 2025 (main)

Abstract: Human hands play a central role in interacting, motivating increasing research in dexterous robotic manipulation. Data-driven embodied AI algorithms demand precise, large-scale, human-like manipulation sequences, which are challenging to obtain with conventional reinforcement learning or real-world teleoperation. To address this, we introduce ManipTrans, a novel two-stage method for efficiently transferring human bimanual skills to dexterous robotic hands in simulation. ManipTrans first pre-trains a generalist trajectory imitator to mimic hand motion, then fine-tunes a specific residual module under interaction constraints, enabling efficient learning and accurate execution of complex bimanual tasks. Experiments show that ManipTrans surpasses state-of-the-art methods in success rate, fidelity, and efficiency. Leveraging ManipTrans, we transfer multiple hand-object datasets to robotic hands, creating DexManipNet, a large-scale dataset featuring previously unexplored tasks like pen capping and bottle unscrewing. DexManipNet comprises 3.3K episodes of robotic manipulation and is easily extensible, facilitating further policy training for dexterous hands and enabling real-world deployments.
Precise, Fast, and Low-cost Concept Erasure in Value Space:  Orthogonal Complement Matters
Authors: Yuan Wang; Ouxiang Li; Tingting Mu; Yanbin Hao; Kuien Liu; Xiang Wang; Xiangnan He
Venue: CVPR 2025 (main)

Abstract: The success of text-to-image generation enabled by diffusion models has imposed an urgent need to erase unwanted concepts, e.g., copyrighted, offensive, and unsafe ones, from the pre-trained models in a precise, timely, and low-cost manner. The twofold demand of concept erasure requires a precise removal of the target concept during generation (i.e., erasure efficacy), while a minimal impact on non-target content generation (i.e., prior preservation). Existing methods are either computationally costly or face challenges in maintaining an effective balance between erasure efficacy and prior preservation. To improve, we propose a precise, fast, and low-cost concept erasure method, called Adaptive Vaule Decomposer (AdaVD), which is training-free. This method is grounded in a classical linear algebraic orthogonal complement operation, implemented in the value space of each cross-attention layer within the UNet of diffusion models. An effective shift factor is designed to adaptively navigate the erasure strength, enhancing prior preservation without sacrificing erasure efficacy. Extensive experimental results show that the proposed AdaVD is effective at both single and multiple concept erasure, showing a 2- to 10-fold improvement in prior preservation as compared to the second best, meanwhile achieving the best or near best erasure efficacy, when comparing with both training-based and training-free state of the arts. AdaVD supports a series of diffusion models and downstream image generation tasks, with code to be publicly available.
HOIGen-1M: A Large-scale Dataset for Human-Object Interaction Video Generation
Authors: Kun Liu; Qi Liu; Xinchen Liu; Jie Li; Yongdong Zhang; Jiebo Luo; Xiaodong He; Wu Liu
Venue: CVPR 2025 (main)

Abstract: Text-to-video (T2V) generation has made tremendous progress in generating complicated scenes based on texts. However, human-object interaction (HOI) often cannot be precisely generated by current T2V models due to the lack of large-scale videos with accurate captions for HOI. To address this issue, we introduce HOIGen-1M, the first large-scale dataset for HOI Generation, consisting of over one million high-quality videos collected from diverse sources. In particular, to guarantee the high quality of videos, we first design an efficient framework to automatically curate HOI videos using the powerful multimodal large language models (MLLMs), and then the videos are further cleaned by human annotators. Moreover, to obtain accurate textual captions for HOI videos, we design a novel video description method based on a Mixture-of-Multimodal-Experts (MoME) strategy that not only generates expressive captions but also eliminates the hallucination by individual MLLM. Furthermore, due to the lack of an evaluation framework for generated HOI videos, we propose two new metrics to assess the quality of generated videos in a coarse-to-fine manner. Extensive experiments reveal that current T2V models struggle to generate high-quality HOI videos and confirm that our HOIGen-1M dataset is instrumental for improving HOI video generation.
T2ISafety: Benchmark for Assessing Fairness, Toxicity, and Privacy in Image Generation
Authors: Lijun Li; Zhelun Shi; Xuhao Hu; Bowen Dong; Yiran Qin; Xihui Liu; Lu Sheng; Jing Shao
Venue: CVPR 2025 (main)

Abstract: Text-to-image (T2I) models have rapidly advanced, enabling the generation of high-quality images from text prompts across various domains. However, these models present notable  safety concerns, including the risk of generating harmful, biased, or private content. Current research on assessing T2I safety remains in its early stages. While some efforts have been made to evaluate models on specific safety dimensions, many critical risks remain unexplored. To address this gap, we introduce T2ISafety, a safety benchmark that evaluates T2I models across three key domains: toxicity, fairness, and bias. We build a detailed hierarchy of 12 tasks and 44 categories based on these three domains, and meticulously collect 70K corresponding prompts. Based on this taxonomy and prompt set, we build a large-scale T2I dataset with 68K manually annotated images and train an evaluator capable of detecting critical risks that previous work has failed to identify, including risks that even ultra-large proprietary models like GPTs cannot correctly detect. We evaluate 15 prominent diffusion models on T2ISafety and reveal several concerns including persistent issues with racial fairness, a tendency to generate toxic content, and significant variation in privacy protection across the models, even with defense methods like concept erasing.
Order-One Rolling Shutter Cameras
Authors: Marvin Anas Hahn; Kathlén Kohn; Orlando Marigliano; Tomas Pajdla
Venue: CVPR 2025 (main)

Abstract: Rolling shutter (RS) cameras dominate consumer and smartphone markets. Several methods for computing the absolute pose of RS cameras have appeared in the last 20 years, but the relative pose problem has not been fully solved yet. We provide a unified theory for the important class of order-one rolling shutter (RS_1) cameras. These cameras generalize the perspective projection to RS cameras, projecting a generic space point to exactly one image point via a rational map. We introduce a new back-projection RS camera model, characterize RS_1 cameras, construct explicit parameterizations of such cameras, and determine the image of a space line. We classify all minimal problems for solving the relative camera pose problem with linear RS_1 cameras and discover new practical cases. Finally, we show how the theory can be used to explain RS models previously used for absolute pose computation.
Animate and Sound an Image
Authors: Xihua Wang; Ruihua Song; Chongxuan Li; Xin Cheng; Boyuan Li; Yihan Wu; Yuyue Wang; Hongteng Xu; Yunfeng Wang
Venue: CVPR 2025 (main)

Abstract: This paper addresses a promising yet underexplored task, Image-to-Sounding-Video (I2SV) generation, which animates a static image and generates synchronized sound simultaneously. Despite advances in video and audio generation models, challenges remain to develop a unified model for generating naturally sounding videos. In this work, we propose a novel approach that leverages two separate pretrained diffusion models and makes vision and audio influence each other during generation based on the Diffusion Transformer (DiT) architecture. First, the individual video and audio pretrained generation models are decomposed into input, output, and expert sub-modules. We propose using a unified joint DiT block to integrate the expert sub-modules to effectively model the interaction between the two modalities, resulting in high-quality I2SV generation. Then, we introduce a joint classifier-free guidance technique to boost the performance during joint generation. Finally, we conduct extensive experiments on three popular benchmark datasets, and in both objective and subjective evaluation our method surpass all the baseline methods in almost all metrics. Case studies show our generated sounding videos are high quality and synchronized between video and audio.
Shining Yourself: High-Fidelity Ornaments Virtual Try-on with Diffusion Model
Authors: Yingmao Miao; Zhanpeng Huang; Rui Han; Zibin Wang; Chenhao Lin; Chao Shen
Venue: CVPR 2025 (main)

Abstract: While virtual try-on for clothes and shoes with diffusion models has gained attraction, virtual try-on for ornaments, such as bracelets, rings, earrings, and necklaces, remains largely unexplored. Due to the intricate tiny patterns and repeated geometric sub-structures in most ornaments, it is much more difficult to guarantee identity and appearance consistency under large pose and scale variances between ornaments and models. This paper proposes the task of virtual try-on for ornaments and presents a method to improve the geometric and appearance preservation of ornament virtual try-ons. Specifically, we estimate an accurate wearing mask to improve the alignments between ornaments and models in an iterative scheme alongside the denoising process. To preserve structure details, we further regularize attention layers to map the reference ornament mask to the wearing mask in an implicit way. Experiment results demonstrate that our method successfully wears ornaments from reference images onto target models, handling substantial differences in scale and pose while preserving identity and achieving realistic visual effects.
Foveated Instance Segmentation
Authors: Hongyi Zeng; Wenxuan Liu; Tianhua Xia; Jinhui Chen; Ziyun Li; Sai Qian Zhang
Venue: CVPR 2025 (main)

Abstract: Instance segmentation is essential for augmented reality and virtual reality (AR/VR) as it enables precise object recognition and interaction, enhancing the integration of virtual and real-world elements for an immersive experience. However, the high computational overhead of segmentation limits its application on resource-constrained AR/VR devices, causing large processing latency and degrading user experience.  In contrast to conventional scenarios, AR/VR users typically focus on only a few regions within their field of view before shifting perspective, allowing segmentation to be concentrated on gaze-specific areas. This insight drives the need for efficient segmentation methods that prioritize processing instance of interest, reducing computational load and enhancing real-time performance. In this paper, we present a foveated instance segmentation(FovealSeg) framework that leverages real-time user gaze data to perform instance segmentation exclusively on instance of interest, resulting in substantial computational savings. Evaluation results show that FSNet achieves an IoU of 0.56 on ADE20K and 0.54 on LVIS, notably outperforming the baseline. The code is available at https://github.com/SAI-Lab-NYU/Foveated-Instance-Segmentation.
Make It Count: Text-to-Image Generation with an Accurate Number of Objects
Authors: Lital Binyamin; Yoad Tewel; Hilit Segev; Eran Hirsch; Royi Rassin; Gal Chechik
Venue: CVPR 2025 (main)

Abstract: Despite the unprecedented success of text-to-image diffusion models, controlling the number of depicted objects using text is surprisingly hard. This is important for various applications from technical documents, to children's books to illustrating cooking recipes. Generating object-correct counts is fundamentally challenging because the generative model needs to keep a sense of separate identity for every instance of the object, even if several objects look identical or overlap, and then carry out a global computation implicitly during generation. It is still unknown if such representations exist. To address count-correct generation, we first identify features within the diffusion model that can carry the object identity information. We then use them to separate and count instances of objects during the denoising process and detect over-generation and under-generation. We fix the latter by training a model that predicts both the shape and location of a missing object, based on the layout of existing ones, and show how it can be used to guide denoising with correct object count. Our approach, CountGen, does not depend on external source to determine object layout, but rather uses the prior from the diffusion model itself, creating prompt-dependent and seed-dependent layouts. Evaluated on two benchmark datasets, we find that CountGen strongly outperforms the count-accuracy of existing baselines.
Universal Domain Adaptation for Semantic Segmentation
Authors: Seun-An Choe; Keon-Hee Park; Jinwoo Choi; Gyeong-Moon Park
Venue: CVPR 2025 (main)

Abstract: Unsupervised domain adaptation for semantic segmentation (UDA-SS) aims to transfer knowledge from labeled synthetic data (source) to unlabeled real-world data (target). Traditional UDA-SS methods work on the assumption that the category settings between the source and target domains are known in advance. However, in real-world scenarios, the category settings of the source and target are unknown due to the lack of labels in the target, resulting in the existence of target-private or source-private classes. Traditional UDA-SS methods struggle with this change, leading to negative transfer and performance degradation. To address these issues, we propose Universal Domain Adaptation for Semantic Segmentation (UniDA-SS) for the first time to achieve good performance even when the category settings of source and target are unknown. We defined the problem in the UniDA-SS scenario as that the confidence score of common Unsupervised domain adaptation for semantic segmentation (UDA-SS) aims to transfer knowledge from labeled source data to unlabeled target data. However, traditional UDA-SS methods assume that category settings between source and target domains are known, which is unrealistic in real-world scenarios. This leads to performance degradation if private private classes exist. To address this limitation, we propose Universal Domain Adaptation for Semantic Segmentation (UniDA-SS), achieving robust adaptation even without prior knowledge of category settings. We define the problem in the UniDA-SS scenario as low confidence scores of common classes in the target domain, which leads to confusion with private classes. To solve this problem, we propose UniMAP: UniDA-SS with Image Ma tching and Prototype-based Distinction, a novel framework composed of two key components. First, Domain-Specific Prototype-based Distinction (DSPD) divides each class into two domain-specific prototypes, enabling finer separation of domain-specific features and enhancing the identification of common classes across domains. Second, Target-based Image Matching (TIM) selects a source image containing the most common-class pixels based on the target pseudo-label and pairs it in a batch to promote effective learning of common classes. We also introduce a new UniDA-SS benchmark and demonstrate through various experiments that UniMAP significantly outperforms baselines. The code is available at https://github.com/KU-VGI/UniDA-SS.
HyperGS: Hyperspectral 3D Gaussian Splatting
Authors: Christopher Thirgood; Oscar Mendez; Erin Ling; Jon Storey; Simon Hadfield
Venue: CVPR 2025 (main)

Abstract: We introduce HyperGS, a novel framework for Hyperspectral Novel View Synthesis (HNVS), based on a new latent 3D Gaussian Splatting (3DGS) technique. Our approach enables simultaneous spatial and spectral renderings by encoding material properties from multi-view 3D hyperspectral datasets. HyperGS reconstructs high-fidelity views from arbitrary perspectives with improved accuracy and speed, outperforming currently existing methods. To address the challenges of high-dimensional data, we perform view synthesis in a learned latent space, incorporating a pixel-wise adaptive density function and a pruning technique for increased training stability and efficiency. Additionally, we introduce the first HNVS benchmark, implementing a number of new baselines based on recent SOTA RGB-NVS techniques, alongside the small number of prior works on HNVS. We demonstrate HyperGS's robustness through extensive evaluation of real and simulated hyperspectral scenes with a 14dB accuracy improvement upon previously published models.
Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios
Authors: Kai Wang; Zekai Li; Zhi-Qi Cheng; Samir Khaki; Ahmad Sajedi; Ramakrishna Vedantam; Konstantinos N Plataniotis; Alexander Hauptmann; Yang You
Venue: CVPR 2025 (main)

Abstract: Dataset distillation has demonstrated strong performance on simple datasets like CIFAR, MNIST, and TinyImageNet but struggles to achieve similar results in more complex scenarios. In this paper, we propose EDF (emphasizes the discriminative features), a dataset distillation method that enhances key discriminative regions in synthetic images using Grad-CAM activation maps. Our approach is inspired by a key observation: in simple datasets, high-activation areas typically occupy most of the image, whereas in complex scenarios, the size of these areas is much smaller. Unlike previous methods that treat all pixels equally when synthesizing images, EDF uses Grad-CAM activation maps to enhance high-activation areas. From a supervision perspective, we downplay supervision signals produced by lower trajectory-matching losses, as they contain common patterns. Additionally, to help the DD community better explore complex scenarios, we build the Complex Dataset Distillation (Comp-DD) benchmark by meticulously selecting sixteen subsets, eight easy and eight hard, from ImageNet-1K. In particular, EDF consistently outperforms SOTA results in complex scenarios, such as ImageNet-1K subsets. Hopefully, more researchers will be inspired and encouraged to improve the practicality and efficacy of DD. Our code and benchmark have been made public at NUS-HPC-AI-Lab/EDF.
LMO: Linear Mamba Operator for MRI Reconstruction
Authors: Wei Li; Jiawei Jiang; Jie Wu; Kaihao Yu; Jianwei Zheng
Venue: CVPR 2025 (main)

Abstract: Interpretability and consistency have long been crucial factors in MRI reconstruction. While interpretability has been significantly innovated with the emerging deep unfolding networks, current solutions still suffer from inconsistency issues and produce inferior anatomical structures. Especially in out-of-distribution cases, e.g., when the acceleration rate (AR) varies, the generalization performance is often catastrophic. To counteract the dilemma, we propose an innovative Linear Mamba Operator (LMO) to ensure consistency and generalization, while still enjoying desirable interpretability. Theoretically, we argue that mapping between function spaces, rather than between signal instances, provides a solid foundation of high generalization. Technically, LMO achieves a good balance between global integration facilitated by a state space model that scans the whole function domain, and local integration engaged with an appealing property of continuous-discrete equivalence. On that basis, learning holistic features can be guaranteed, tapping the potential of maximizing data consistency. Quantitative and qualitative results demonstrate that LMO significantly outperforms other state-of-the-arts. More importantly, LMO is the unique model that, with AR changed, achieves retraining performance without retraining steps. Codes are available at https://github.com/ZhengJianwei2/LMO.
AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios
Authors: Ziming Huang; Xurui Li; Haotian Liu; Feng Xue; Yuzhe Wang; Yu Zhou
Venue: CVPR 2025 (main)

Abstract: Recently, multi-class anomaly classification has garnered increasing attention. Previous methods directly cluster anomalies but often struggle due to the lack of anomaly-prior knowledge. Acquiring this knowledge faces two issues: the non-prominent and weak-semantics anomalies. In this paper, we propose AnomalyNCD, a multi-class anomaly classification network compatible with different anomaly detection  methods. To address the non-prominence of anomalies, we design main element binarization (MEBin) to obtain anomaly-centered images, ensuring anomalies are learned while avoiding the impact of incorrect detections. Next, to learn anomalies with weak semantics, we design mask-guided representation learning, which focuses on isolated anomalies guided by masks and reduces confusion from erroneous inputs through corrected pseudo labels. Finally, to enable flexible classification at both region and image levels, we develop a region merging strategy that determines the overall image category based on the classified anomaly regions. Our method outperforms the state-of-the-art works on the MVTec AD and MTD datasets. Compared with the current methods, AnomalyNCD combined with zero-shot anomaly detection method achieves a 10.8% F1 gain, 8.8% NMI gain, and 9.5% ARI gain on MVTec AD, and 12.8% F1 gain, 5.7% NMI gain, and 10.8% ARI gain on MTD. Code is available at https://github.com/HUST-SLOW/AnomalyNCD.