GenAI4ELab/papercli-papers huggingface dataset:
Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features
Authors: Shangbo Wu; Yu-an Tan; Ruinan Ma; Wencong Ma; Dehua Zhu; Yuanzhang Li
Venue: ICCV 2025 (main)

Abstract: The ability of deep neural networks (DNNs) come from extracting and interpreting features from the data provided. By exploiting intermediate features in DNNs instead of relying on hard labels, we craft adversarial perturbation that generalize more effectively, boosting black-box transferability. These features ubiquitously come from supervised learning in previous work. Inspired by the exceptional synergy between self-supervised learning and the Transformer architecture, this paper explores whether exploiting self-supervised Vision Transformer (ViT) representations can improve adversarial transferability. We present dSVA---a generative dual self-supervised ViT features attack, that exploits both global structural features from contrastive learning (CL) and local textural features from masked image modeling (MIM), the self-supervised learning paradigm duo for ViTs. We design a novel generative training framework that incorporates a generator to create black-box adversarial examples, and strategies to train the generator by exploiting joint features and the attention mechanism of self-supervised ViTs. Our findings show that CL and MIM enable ViTs to attend to distinct feature tendencies, which, when exploited in tandem, boast great adversarial generalizability. By disrupting dual deep features distilled by self-supervised ViTs, we are rewarded with remarkable black-box transferability to models of various architectures that outperform state-of-the-arts. Code available at https://github.com/spencerwooo/dSVA.
Zero-Shot Compositional Video Learning with Coding Rate Reduction
Authors: Heeseok Jung; Jun-Hyeon Bak; Yujin Jeong; Gyugeun Lee; Jinwoo Ahn; Eun-Sol Kim
Venue: ICCV 2025 (main)

Abstract: In this paper, we propose a novel zero-shot compositional video understanding method inspired by how young children efficiently learn new concepts and flexibly expand their existing knowledge framework. While recent large-scale visual language models (VLMs) have achieved remarkable advancements and demonstrated impressive performance improvements across various tasks, they require massive amounts of data and computational resources. However, despite their strong benchmark performance, they often fail to solve simple zero-shot composition tasks. Moreover, VLMs designed for video data demand even greater computational resources. We introduce a new video representation learning method inspired by human compositional learning to address these challenges. Specifically, we demonstrate that achieving zero-shot compositional learning requires effective representation learning that disentangles given data into meaningful semantic units. We propose a novel method that learns such disentangled representations based on an information-theoretic measure. By optimizing coding rate reduction, we successfully learn spatio-temporally disentangled features from videos, one of the most challenging data. Our approach significantly enhances compositional generalizability, demonstrating its effectiveness in zero-shot learning scenarios.
AVTrustBench: Assessing and Enhancing Reliability and Robustness in Audio-Visual LLMs
Authors: Sanjoy Chowdhury; Sayan Nag; Subhrajyoti Dasgupta; Yaoting Wang; Mohamed Elhoseiny; Ruohan Gao; Dinesh Manocha
Venue: ICCV 2025 (main)

Abstract: With the rapid advancement of Multi-modal Large Language Models (MLLMs), several diagnostic benchmarks have recently been developed to assess these models' multimodal reasoning proficiency. However, these benchmarks are restricted to assessing primarily the visual aspect and do not examine the holistic audio-visual (AV) understanding. Moreover, currently, there are no benchmarks that investigate the capabilities of AVLLMs to calibrate their responses when presented with perturbed inputs. To this end, we introduce Audio-Visual Trustworthiness assessment Benchmark (AVTrustBench), comprising 600K samples spanning over 9 meticulously crafted tasks, evaluating the capabilities of AVLLMs across three distinct dimensions: Adversarial Attack, Compositional Reasoning, and Modality-specific Dependency. Using our benchmark, we extensively evaluate 16 state-of-the-art AVLLMs. The findings reveal that the majority of existing models fall significantly short of achieving human like comprehension, offering valuable insights for future research directions. To alleviate the limitations in the existing approaches, we further propose a robust, model agnostic calibrated audio-visual preference optimization based training strategy CAVPref, obtaining a gain up to 30.19% across all 9 tasks.
LOTA: Bit-Planes Guided AI-Generated Image Detection
Authors: Hongsong  Wang; Renxi  Cheng; Yang  Zhang; Chaolei  Han; Jie  Gui
Venue: ICCV 2025 (main)

Abstract: The rapid advancement of GAN and Diffusion models makes it more difficult to distinguish AI-generated images from real ones. Recent studies often use image-based reconstruction errors as an important feature for determining whether an image is AI-generated. However, these approaches typically incur high computational costs and also fail to capture intrinsic noisy features present in the raw images. To solve these problems, we innovatively refine error extraction by using bit-plane-based image processing, as lower bit planes indeed represent noise patterns in images. We introduce an effective bit-planes guided noisy image generation and exploit various image normalization strategies, including scaling and thresholding. Then, to amplify the noise signal for easier AI-generated image detection, we design a maximum gradient patch selection that applies multi-directional gradients to compute the noise score and selects the region with the highest score. Finally, we propose a lightweight and effective classification head and explore two different structures: noise-based classifier and noise-guided classifier. Extensive experiments on the GenImage benchmark demonstrate the outstanding performance of our method, which achieves an average accuracy of 98.9% and shows excellent cross-generator generalization capability. Particularly, our method achieves an accuracy of over 98.2% from GAN to Diffusion and over 99.2% from Diffusion to GAN. Moreover, it performs error extraction at the millisecond level, nearly a hundred times faster than existing methods. The code is at https://github.com/hongsong-wang/LOTA.
Benchmarking Egocentric Visual-Inertial SLAM at City Scale
Authors: Anusha Krishnan; Shaohui Liu; Paul-Edouard Sarlin; Oscar Gentilhomme; David Caruso; Maurizio Monge; Richard Newcombe; Jakob Engel; Marc Pollefeys
Venue: ICCV 2025 (main)

Abstract: Precise 6-DoF simultaneous localization and mapping (SLAM) from onboard sensors is critical for wearable devices capturing egocentric data, which exhibits specific challenges, such as a wider diversity of motions and viewpoints, prevalent dynamic visual content, or long sessions affected by time-varying sensor calibration. While recent progress on SLAM has been swift, academic research is still driven by benchmarks that do not reflect these challenges or do not offer sufficiently accurate ground truth poses. In this paper, we introduce a new dataset and benchmark for visual-inertial SLAM with egocentric, multi-modal data. We record hours and kilometers of trajectories through a city center with glasses-like devices equipped with various sensors. We leverage surveying tools to obtain control points as indirect pose annotations that are metric, centimeter-accurate, and available at city scale. This makes it possible to evaluate extreme trajectories that involve walking at night or traveling in a vehicle. We show that state-of-the-art systems developed by academia are not robust to these challenges and we identify components that are responsible for this. In addition, we design tracks with different levels of difficulty to ease in-depth analysis and evaluation of less mature approaches. The dataset and benchmark are available at www.lamaria.ethz.ch.
CATSplat: Context-Aware Transformer with Spatial Guidance for Generalizable 3D Gaussian Splatting from A Single-View Image
Authors: Wonseok Roh; Hwanhee Jung; Jong Wook Kim; Seunggwan Lee; Innfarn Yoo; Andreas Lugmayr; Seunggeun Chi; Karthik Ramani; Sangpil Kim
Venue: ICCV 2025 (main)

Abstract: Recently, generalizable feed-forward methods based on 3D Gaussian Splatting have gained significant attention for their potential to reconstruct 3D scenes using finite resources. These approaches create a 3D radiance field, parameterized by per-pixel 3D Gaussian primitives, from just a few images in a single forward pass. Unlike multi-view methods that benefit from cross-view correspondences, 3D scene reconstruction with a single-view image remains an underexplored area. In this work, we introduce CATSplat, a novel generalizable transformer-based framework designed to break through the inherent constraints in monocular settings. First, we propose leveraging textual guidance from a visual-language model to complement insufficient information from single-view image features. By incorporating scene-specific contextual details from text embeddings through cross-attention, we pave the way for context-aware 3D scene reconstruction beyond relying solely on visual cues. Moreover, we advocate utilizing spatial guidance from 3D point features toward comprehensive geometric understanding under monocular settings. With 3D priors, image features can capture rich structural insights for predicting 3D Gaussians without multi-view techniques. Extensive experiments on large-scale datasets demonstrate the state-of-the-art performance of CATSplat in single-view 3D scene reconstruction with high-quality novel view synthesis.
Supercharged One-step Text-to-Image Diffusion Models with Negative Prompts
Authors: Viet Nguyen; Anh Nguyen; Trung Dao; Khoi Nguyen; Cuong Pham; Toan Tran; Anh Tran
Venue: ICCV 2025 (main)

Abstract: The escalating demand for real-time image synthesis has driven significant advancements in one-step diffusion models, which inherently offer expedited generation speeds compared to traditional multi-step methods. However, this enhanced efficiency is frequently accompanied by a compromise in the controllability of image attributes. While negative prompting, typically implemented via classifier-free guidance (CFG), has proven effective for fine-grained control in multi-step models, its application to one-step generators remains largely unaddressed. Due to the lack of iterative refinement, as in multi-step diffusion, directly applying CFG to one-step generation leads to blending artifacts and diminished output quality. To fill this gap, we introduce Negative-Away Steer Attention (NASA), an efficient method that integrates negative prompts into one-step diffusion models. NASA operates within the intermediate representation space by leveraging cross-attention mechanisms to suppress undesired visual attributes. This strategy avoids the blending artifacts inherent in output-space guidance and achieves high efficiency, incurring only a minimal 1.89% increase in FLOPs compared to the computational doubling of CFG. Furthermore, NASA can be seamlessly integrated into existing timestep distillation frameworks, enhancing the student's output quality. Experimental results demonstrate that NASA substantially improves controllability and output quality, achieving an HPSv2 score of 31.21, setting a new state-of-the-art benchmark for one-step diffusion models.
Learning Normals of Noisy Points by Local Gradient-Aware Surface Filtering
Authors: Qing Li; Huifang Feng; Xun Gong; Yu-Shen Liu
Venue: ICCV 2025 (main)

Abstract: Estimating normals for noisy point clouds is a persistent challenge in 3D geometry processing, particularly for end-to-end oriented normal estimation. Existing methods generally address relatively clean data and rely on supervised priors to fit local surfaces within specific neighborhoods. In this paper, we propose a novel approach for learning normals from noisy point clouds through local gradient-aware surface filtering. Our method projects noisy points onto the underlying surface by utilizing normals and distances derived from an implicit function constrained by local gradients. We start by introducing a distance measurement operator for global surface fitting on noisy data, which integrates projected distances along normals. Following this, we develop an implicit field-based filtering approach for surface point construction, adding projection constraints on these points during filtering. To address issues of over-smoothing and gradient degradation, we further incorporate local gradient consistency constraints, as well as local gradient orientation and aggregation. Comprehensive experiments on normal estimation, surface reconstruction, and point cloud denoising demonstrate the state-of-the-art performance of our method. The source code and trained models are available at https://github.com/LeoQLi/LGSF.
CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos
Authors: Nikita Karaev; Yuri Makarov; Jianyuan Wang; Natalia Neverova; Andrea Vedaldi; Christian Rupprecht
Venue: ICCV 2025 (main)

Abstract: We introduce CoTracker3, a new state-of-the-art point tracker. With CoTracker3, we revisit the design of recent trackers, removing components and reducing the number of parameters while also improving performance. We also explore the interplay of synthetic and real data. Recent trackers are trained on synthetic videos due to the difficulty of collecting tracking annotations for real data. However, this can result in suboptimal performance due to the statistical gap between synthetic and real videos. We thus suggest using off-the-shelf trackers as teachers, annotating real videos with pseudo-labels. Compared to other recent attempts at using real data for learning trackers, this scheme is much simpler and achieves better results using 1,000 times less data. CoTracker3 is available in online (causal) and offline variants and is particularly robust to occlusions.
IMG: Calibrating Diffusion Models via Implicit Multimodal Guidance
Authors: Jiayi Guo; Chuanhao Yan; Xingqian Xu; Yulin Wang; Kai Wang; Gao Huang; Humphrey Shi
Venue: ICCV 2025 (main)

Abstract: Ensuring precise multimodal alignment between diffusion-generated images and input prompts has been a long-standing challenge. Earlier works finetune diffusion weight using high-quality preference data, which tends to be limited and difficult to scale up. Recent editing-based methods further refine local regions of generated images but may compromise overall image quality. In this work, we propose Implicit Multimodal Guidance (IMG), a novel re-generation-based multimodal alignment framework that requires no extra data or editing operations. Specifically, given a generated image and its prompt, IMG a) utilizes a multimodal large language model (MLLM) to identify misalignments; b) introduces an Implicit Aligner that manipulates diffusion conditioning features to reduce misalignments and enable re-generation; and c) formulates the re-alignment goal into a trainable objective, namely Iteratively Updated Preference Objective. Extensive qualitative and quantitative evaluations on SDXL, SDXL-DPO, and FLUX show that IMG outperforms existing alignment methods. Furthermore, IMG acts as a flexible plug-and-play adapter, seamlessly enhancing prior finetuning-based alignment methods. Our code is available at https://github.com/SHI-Labs/IMG-Multimodal-Diffusion-Alignment.
VRBench: A Benchmark for Multi-Step Reasoning in Long Narrative Videos
Authors: Jiashuo Yu; Yue Wu; Meng Chu; Zhifei Ren; Zizheng Huang; Pei Chu; Ruijie Zhang; Yinan He; Qirui Li; Songze Li; Zhenxiang Li; Zhongying Tu; Conghui He; Yu Qiao; Yali Wang; Yi Wang; Limin Wang
Venue: ICCV 2025 (main)

Abstract: We present VRBench, the first long narrative video benchmark crafted for evaluating large models' multi-step reasoning capabilities, addressing limitations in existing evaluations that overlook temporal reasoning and procedural validity. It comprises 960 long videos (with an average duration of 1.6 hours), along with 8,243 human-labeled multi-step question-answering pairs and 25,106 reasoning steps with timestamps. These videos are curated via a multi-stage filtering process including expert inter-rater reviewing to prioritize plot coherence. We develop a human-AI collaborative framework that generates coherent reasoning chains, each requiring multiple temporally grounded steps, spanning seven types (e.g., event attribution, implicit inference). VRBench designs a multi-phase evaluation pipeline that assesses models at both the outcome and process levels. Apart from the MCQs for the final results, we propose a progress-level LLM-guided scoring metric to evaluate the quality of the reasoning chain from multiple dimensions comprehensively. Through extensive evaluations of 12 LLMs and 19 VLMs on VRBench, we undertake a thorough analysis and provide valuable insights that advance the field of multi-step reasoning.
PUMPS: Skeleton-Agnostic Point-based Universal Motion Pre-Training for Synthesis in Human Motion Tasks
Authors: Clinton Ansun Mo; Kun Hu; Chengjiang Long; Dong Yuan; Wan-Chi Siu; Zhiyong Wang
Venue: ICCV 2025 (main)

Abstract: Motion skeletons drive 3D character animation by transforming bone hierarchies, but differences in proportions or structure make motion data hard to transfer across skeletons, posing challenges for data-driven motion synthesis. Temporal Point Clouds (TPCs) offer an unstructured, cross-compatible motion representation. Though reversible with skeletons, TPCs mainly serve for compatibility, not for direct motion task learning. Doing so would require data synthesis capabilities for the TPC format, which presents unexplored challenges regarding its unique temporal consistency and point identifiability. Therefore, we propose PUMPS, the primordial autoencoder architecture for TPC data. PUMPS independently reduces frame-wise point clouds into sampleable feature vectors, from which a decoder extracts distinct temporal points using latent Gaussian noise vectors as sampling identifiers. We introduce linear assignment-based point pairing to optimise the TPC reconstruction process, and negate the use of expensive point-wise attention mechanisms in the architecture. Using these latent features, we pre-train a motion synthesis model capable of performing motion prediction, transition generation, and keyframe interpolation. For these pre-training tasks, PUMPS performs remarkably well even without native dataset supervision, matching state-of-the-art performance. When fine-tuned for motion denoising or estimation, PUMPS outperforms many respective methods without deviating from its generalist architecture.
When and Where do Data Poisons Attack Textual Inversion?
Authors: Jeremy Styborski; Mingzhi Lyu; Jiayou Lu; Nupur Kapur; Adams Wai-Kin Kong
Venue: ICCV 2025 (main)

Abstract: Poisoning attacks pose significant challenges to the robustness of diffusion models (DMs). In this paper, we systematically analyze when and where poisoning attacks textual inversion (TI), a widely used personalization technique for DMs. We first introduce Semantic Sensitivity Maps, a novel method for visualizing the influence of poisoning on text embeddings. Second, we identify and experimentally verify that DMs exhibit non-uniform learning behavior across timesteps, focusing on lower-noise samples. Poisoning attacks inherit this bias and inject adversarial signals predominantly at lower timesteps. Lastly, we observe that adversarial signals distract learning away from relevant concept regions within training data, corrupting the TI process. Based on these insights, we propose Safe-Zone Training (SZT), a novel defense mechanism comprised of 3 key components: (1) JPEG compression to weaken high-frequency poison signals, (2) restriction to high timesteps during TI training to avoid adversarial signals at lower timesteps, and (3) loss masking to constrain learning to relevant regions. Extensive experiments across multiple poisoning methods demonstrate that SZT greatly enhances the robustness of TI against all poisoning attacks, improving generative quality beyond prior published defenses. Code: www.github.com/JStyborski/Diff_Lab Data: www.github.com/JStyborski/NC10
RAGD: Regional-Aware Diffusion Model for Text-to-Image Generation
Authors: Zhennan Chen; Yajie Li; Haofan Wang; Zhibo Chen; Zhengkai Jiang; Jun Li; Qian Wang; Jian Yang; Ying Tai
Venue: ICCV 2025 (main)

Abstract: Regional prompting, or compositional generation, which enables fine-grained spatial control, has gained increasing attention for its practicality in real-world applications. However, previous methods either introduce additional trainable modules, thus only applicable to specific models, or manipulate on score maps within attention layers using attention masks, resulting in limited control strength when the number of regions increases. To handle these limitations, we present RAGD, a Regional-Aware text-to-image Generation method conditioned on regional descriptions for precise layout composition. RAGD decouples the multi-region generation into two sub-tasks, the construction of individual region (Regional Hard Binding) that ensures the regional prompt is properly executed, and the overall detail refinement (Regional Soft Refinement) over regions that dismiss the visual boundaries and enhance adjacent interactions. Furthermore, RAGD novelly makes repainting feasible, where users can modify specific unsatisfied regions in the last generation while keeping all other regions unchanged, without relying on additional inpainting models. Our approach is tuning-free and applicable to other frameworks as an enhancement to the prompt following property. Quantitative and qualitative experiments demonstrate that RAGD achieves superior performance over attribute binding and object relationship than previous methods.
OpenM3D: Open Vocabulary Multi-view Indoor 3D Object Detection without Human Annotations
Authors: Peng-Hao Hsu; Ke Zhang; Fu-En Wang; Tao Tu; Ming-Feng Li; Yu-Lun Liu; Albert Y. C. Chen; Min Sun; Cheng-Hao Kuo
Venue: ICCV 2025 (main)

Abstract: Open-vocabulary (OV) 3D object detection is an emerging field, yet its exploration through image-based methods remains limited compared to 3D point cloud-based methods. We introduce OpenM3D, a novel open-vocabulary multi-view indoor 3D object detector trained without human annotations. In particular, OpenM3D is a single-stage detector adapting the 2D-induced voxel features from the ImGeoNet model. To support OV, it is jointly trained with a class-agnostic 3D localization loss requiring high-quality 3D pseudo boxes and a voxel-semantic alignment loss requiring diverse pre-trained CLIP features. We follow the training setting of OV-3DET where posed RGB-D images are given but no human annotations of 3D boxes or classes are available. We propose a 3D Pseudo Box Generation method using a graph embedding technique that combines 2D segments into coherent 3D structures. Our pseudo-boxes achieve higher precision and recall than other methods, including the method proposed in OV-3DET. We further sample diverse CLIP features from 2D segments associated with each coherent 3D structure to align with the corresponding voxel feature. The key to training a highly accurate single-stage detector requires both losses to be learned toward high-quality targets. At inference, OpenM3D, a highly efficient detector, requires only multi-view images for input and demonstrates superior accuracy and speed (0.3 sec. per scene) on ScanNet200 and ARKitScenes indoor benchmarks compared to existing methods. We outperform a strong two-stage method that leverages our class-agnostic detector with a ViT CLIP-based OV classifier and a baseline incorporating multi-view depth estimator on both accuracy and speed.