GenAI4ELab/papercli-papers huggingface dataset:
ERNet: Efficient Non-Rigid Registration Network for Point Sequences
Authors: Guangzhao He; Yuxi Xiao; Zhen Xu; Xiaowei Zhou; Sida Peng
Venue: ICCV 2025 (main)

Abstract: Registering an object shape to a sequence of point clouds undergoing non-rigid deformation is a long-standing challenge. The key difficulties stem from two factors: (i) the presence of local minima due to the non-convexity of registration objectives, especially under noisy or partial inputs, which hinders accurate and robust deformation estimation, and (ii) error accumulation over long sequences, leading to tracking failures. To address these challenges, we introduce to adopt a scalable data-driven approach and propose \methodname, an efficient feed-forward model trained on large deformation datasets.It is designed to handle noisy and partial inputs while effectively leveraging temporal information for accurate and consistent sequential registration. The key to our design is predicting a sequence of deformation graphs through a two-stage pipeline, which first estimates frame-wise coarse graph nodes for robust initialization, before refining their trajectories over time in a sliding-window fashion. Extensive experiments show that our proposed approach (i) outperforms previous state of the art on both the DeformingThings4D and D-FAUST datasets, and (ii) achieves more than 4x speedup compared to the previous best, offering significant efficiency improvement.
Does Your Vision-Language Model Get Lost in the Long Video Sampling Dilemma?
Authors: Tianyuan Qu; Longxiang Tang; Bohao Peng; Senqiao Yang; Bei Yu; Jiaya Jia
Venue: ICCV 2025 (main)

Abstract: The rise of Large Vision-Language Models (LVLMs) has significantly advanced video understanding. However, efficiently processing long videos remains a challenge due to the "Sampling Dilemma": low-density sampling risks missing critical information, while high-density sampling introduces redundancy. To address this issue, we introduce LSDBench, the first benchmark designed to evaluate LVLMs on long-video tasks by constructing high Necessary Sampling Density (NSD) questions--where NSD represents the minimum sampling density required to accurately answer a given question. LSDBench focuses on dense, short-duration actions to rigorously assess the sampling strategies employed by LVLMs. To tackle the challenges posed by high-NSD questions, we propose a novel Reasoning-Driven Hierarchical Sampling (RHS) framework, which combines global localization of question-relevant cues with local dense sampling for precise inference. Additionally, we develop a lightweight Semantic-Guided Frame Selector to prioritize informative frames, enabling RHS to achieve comparable or superior performance with significantly fewer sampled frames. Together, our LSDBench and RHS framework address the unique challenges of high-NSD long-video tasks, setting a new standard for evaluating and improving LVLMs in this domain.
Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models
Authors: Mateusz Michalkiewicz; Sheena Bai; Mahsa Baktashmotlagh; Varun Jampani; Guha Balakrishnan
Venue: ICCV 2025 (main)

Abstract: In this paper, we analyze the viewpoint stability of foundational models - specifically, their sensitivity to changes in viewpoint- and define instability as significant feature variations resulting from minor changes in viewing angle, leading to generalization gaps in 3D reasoning tasks. We investigate nine foundational models, focusing on their responses to viewpoint changes, including the often-overlooked accidental viewpoints where specific camera orientations obscure an object's true 3D structure. Our methodology enables recognizing and classifying accidental, stable and other viewpoints using feature representations alone, without accessing the actual images. Our findings indicate that while foundation models consistently encode accidental viewpoints, they vary in their interpretation of other viewpoints due to inherent biases, at times leading to object misclassifications based on geometric resemblance. Through quantitative and qualitative evaluations on three downstream tasks - classification, VQA, and 3D reconstruction - we illustrate the impact of viewpoint instability and underscore the importance of feature robustness across diverse viewing conditions.
HumorDB: Can AI understand graphical humor?
Authors: Vedaant V Jain; Gabriel Kreiman; Felipe dos Santos Alves Feitosa
Venue: ICCV 2025 (main)

Abstract: Despite significant advancements in image segmentation and object detection, understanding complex scenes remains a significant challenge. Here, we focus on graphical humor as a paradigmatic example of image interpretation that requires elucidating the interaction of different scene elements in the context of prior cognitive knowledge. This paper introduces HumorDB, a novel, controlled, and carefully curated dataset designed to evaluate and advance visual humor understanding by AI systems. The dataset comprises diverse images spanning photos, cartoons, sketches, and AI-generated content, including minimally contrastive pairs where subtle edits differentiate between humorous and non-humorous versions. We evaluate humans, state-of-the-art vision models, and large vision-language models on three tasks: binary humor classification, funniness rating prediction, and pairwise humor comparison. The results reveal a gap between current AI systems and human-level humor understanding. While pretrained vision-language models perform better than vision-only models, they still struggle with abstract sketches and subtle humor cues. Analysis of attention maps shows that even when models correctly classify humorous images, they often fail to focus on the precise regions that make the image funny. Preliminary mechanistic interpretability studies and evaluation of model explanations provide initial insights into how different architectures process humor. Our results identify promising trends and current limitations, suggesting that an effective understanding of visual humor requires sophisticated architectures capable of detecting subtle contextual features and bridging the gap between visual perception and abstract reasoning.All the code and data are available here: https://anonymous.4open.science/r/HumorDB_-049A
Towards Safer and Understandable Driver Intention Prediction
Authors: Mukilan Karuppasamy; Shankar Gangisetty; Shyam Nandan Rai; Carlo Masone; C V Jawahar
Venue: ICCV 2025 (main)

Abstract: Autonomous driving (AD) systems are becoming increasingly capable of handling complex tasks, mainly due to recent advances in deep learning and AI. As interactions between autonomous systems and humans increase, the interpretability of decision-making processes in driving systems becomes increasingly crucial for ensuring safe driving operations. Successful human-machine interaction requires understanding the underlying representations of the environment and the driving task, which remains a significant challenge in deep learning-based systems. To address this, we introduce the task of interpretability in maneuver prediction before they occur for driver safety, i.e., driver intent prediction (DIP), which plays a critical role in AD systems. To foster research in interpretable DIP, we curate the eXplainable Driving Action Anticipation Dataset (DAAD-X), a new multimodal, ego-centric video dataset to provide hierarchical, high-level textual explanations as causal reasoning for the driver's decisions. These explanations are derived from both the driver's eye-gaze and the ego-vehicle's perspective. Next, we propose Video Concept Bottleneck Model (VCBM), a framework that generates spatio-temporally coherent explanations inherently, without relying on post-hoc techniques. Finally, through extensive evaluations of the proposed VCBM on the DAAD-X dataset, we demonstrate that transformer-based models exhibit greater interpretability than conventional CNN-based models. Additionally, we introduce a multilabel t-SNE visualization technique to illustrate the disentanglement and causal correlation among multiple explanations.
Generative Gaussian Splatting: Generating 3D Scenes with Video Diffusion Priors
Authors: Katja Schwarz; Norman Müller; Peter Kontschieder
Venue: ICCV 2025 (main)

Abstract: Synthesizing consistent and photorealistic 3D scenes is an open problem in computer vision. Video diffusion models generate impressive videos but cannot directly synthesize 3D representations, i.e., lack 3D consistency in the generated sequences. In addition, directly training generative 3D models is challenging due to a lack of 3D training data at scale. In this work, we present Generative Gaussian Splatting (GGS) - a novel approach that integrates a 3D rep resentation with a pre-trained latent video diffusion model. Specifically, our model synthesizes a feature field parameterized via 3D Gaussian primitives. The feature field is then either rendered to feature maps and decoded into multi-view images, or directly upsampled into a 3D radiance field. We evaluate our approach on two common benchmark datasets for scene synthesis, RealEstate10K and ScanNet++, and find that our proposed GGS model significantly improves both the 3D consistency of the generated multi-view images, and the quality of the generated 3D scenes over all relevant baselines. Compared to a similar model without 3D representation, GGS improves FID on the generated 3D scenes by  20% on both RealEstate10K and ScanNet++.
AffordDexGrasp: Open-set Language-guided Dexterous Grasp with Generalizable-Instructive Affordance
Authors: Yi-Lin Wei; Mu Lin; Yuhao Lin; Jian-Jian Jiang; Xiao-Ming Wu; Ling-An Zeng; Wei-Shi Zheng
Venue: ICCV 2025 (main)

Abstract: Language-guided robot dexterous generation enables robots to grasp and manipulate objects based on human commands. However, previous data-driven methods are hard to understand intention and execute grasping with unseen categories in the open set. In this work, we explore a new task, Open-set Language-guided Dexterous Grasp, and find that the main challenge is the huge gap between high-level human language semantics and low-level robot action. To solve this problem, we propose an Affordance Dexterous Grasp (AffordDexGrasp) framework, with the insight that bridging the gap with a new generalizable-instructive affordance representation. This affordance can generalize to unseen categories by leveraging the object's local structure and category-agnostic semantic attributes, thereby effectively guiding dexterous grasp generation. Built upon the affordance, our framework introduces Affordance Flow Matching (AFM) for affordance generation with language as input, and Grasp Flow Matching (GFM) for generating dexterous grasp with affordance as input. To evaluate our framework, we build an open-set table-top language-guided dexterous grasp dataset. Extensive experiments in the simulation and real worlds show that our framework surpasses all previous methods in both seen category and unseen category generalization.
AR-1-to-3: Single Image to Consistent 3D Object via Next-View Prediction
Authors: Xuying Zhang; Yupeng Zhou; Kai Wang; Yikai Wang; Zhen Li; Shaohui Jiao; Daquan Zhou; Qibin Hou; Ming-Ming Cheng
Venue: ICCV 2025 (main)

Abstract: Novel view synthesis (NVS) is a cornerstone for image-to-3d creation. However, existing works still struggle to maintain consistency between the generated views and the input views, especially when there is a significant camera pose difference, leading to poor-quality 3D geometries and textures. We attribute this issue to their treatment of all target views with equal priority according to our empirical observation that the target views closer to the input views exhibit higher fidelity. With this inspiration, we propose AR-1-to-3, a novel next-view prediction paradigm based on diffusion models that first generates views close to the input views, which are then utilized as contextual information to progressively synthesize farther views. To encode the generated view subsequences as local and global conditions for the next-view prediction, we accordingly develop a stacked local feature encoding strategy (Stacked-LE) and an LSTM-based global feature encoding strategy (LSTM-GE). Extensive experiments demonstrate that our method significantly improves the consistency between the generated views and the input views, producing high-fidelity 3D assets.
Inverse 3D Microscopy Rendering for Cell Shape Inference with Active Mesh
Authors: Sacha Ichbiah; Anshuman Sinha; Fabrice Delbary; Hervé Turlier
Venue: ICCV 2025 (main)

Abstract: Traditional methods for biological shape inference, such as deep learning (DL) and active contour models, face important limitations in 3D. DL approaches require large annotated datasets, which are often impractical to obtain, while active contour methods depend on carefully tuned heuristics for intensity attraction and shape regularization. We introduce deltaMic, a novel differentiable 3D renderer for fluorescence microscopy that formulates shape inference as an inverse problem. By leveraging differentiable convolutions, deltaMic simulates the image formation process, integrating a parameterized point spread function (PSF) with a triangle mesh-based representation of biological structures. Unlike DL- or contour-based segmentation, deltaMic directly optimizes both shape and optical parameters to align synthetic and real microscopy images, removing the need for large datasets or sample-specific fine-tuning. To ensure scalability, we implement a GPU-accelerated Fourier transform for triangle meshes along with narrow-band spectral filtering. We show that deltaMic accurately reconstructs cell geometries from both synthetic and diverse experimental 3D microscopy data, while remaining robust to noise and initialization. This establishes a new physics-informed framework for biophysical image analysis and inverse modeling.
NeurOp-Diff: Continuous Remote Sensing Image Super-Resolution via Neural Operator Diffusion
Authors: Zihao Xu; Yuzhi Tang; Bowen Xu; Qingquan Li
Venue: ICCV 2025 (main)

Abstract: Most publicly accessible remote sensing data suffer from low resolution, limiting their practical applications. To address this, we propose a diffusion model guided by neural operators (NO) for continuous remote sensing image super-resolution (NeurOp-Diff). Neural operators are used to learn resolution representations at arbitrary scales, encoding low-resolution (LR) images into high-dimensional features, which are then used as prior conditions to guide the diffusion model for denoising. This effectively addresses the artifacts and excessive smoothing issues present in existing super-resolution (SR) methods, enabling the generation of high-quality, continuous super-resolution images. Specifically, we adjust the super-resolution scale by a scaling factor (s), allowing the model to adapt to different super-resolution magnifications. Furthermore, experiments on multiple datasets demonstrate the effectiveness of NeurOp-Diff. Our code is available at https://github.com/zerono000/NeurOp-Diff.
DC-TTA: Divide-and-Conquer Framework for Test-Time Adaptation of Interactive Segmentation
Authors: Jihun Kim; Hoyong Kwon; Hyeokjun  Kweon; Wooseong Jeong; Kuk-Jin Yoon
Venue: ICCV 2025 (main)

Abstract: Interactive segmentation (IS) allows users to iteratively refine object boundaries with minimal cues, such as positive and negative clicks. While the Segment Anything Model (SAM) has garnered attention in the IS community for its promptable segmentation capabilities, it often struggles in specialized domains or when handling complex scenarios (e.g., camouflaged or multi-part objects). To overcome these challenges, we propose DC-TTA, a novel test-time adaptation (TTA) framework that adapts SAM on a per-sample basis by leveraging user interactions as supervision. Instead of forcing a single model to incorporate all user clicks at once, DC-TTA partitions the clicks into more coherent subsets, each processed independently via TTA with a separated model. This Divide-and-Conquer strategy reduces conflicts among diverse cues and enables more localized updates. Finally, we merge the adapted models to form a unified predictor that integrates the specialized knowledge from each subset. Experimental results across various benchmarks demonstrate that DC-TTA significantly outperforms SAM's zero-shot results and conventional TTA methods, effectively handling complex tasks such as camouflaged object segmentation with fewer interactions and improved accuracy. The code is available at https://github.com/jihun1998/DCTTA.
PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning
Authors: M. Anwar Ma'sum; Mahardhika Pratama; Savitha Ramasamy; Lin Liu; Habibullah Habibullah; Ryszard Kowalczyk
Venue: ICCV 2025 (main)

Abstract: The data privacy constraint in online continual learning (OCL), where the data can be seen only once, complicates the catastrophic forgetting problem in streaming data. A common approach applied by the current SOTAs in OCL is the use of memory-saving exemplars or features from previous classes to be replayed in the current task. On the other hand, the prompt-based approach performs excellently in continual learning, but at the cost of a growing number of trainable parameters. The first approach may not be applicable in practice due to the data openness policy, while the second approach has the issue of throughput associated with the streaming data. In this study, we propose a novel prompt-based method for online continual learning that includes 4 main components: (1) single light-weight prompt generator as a general knowledge, (2) trainable scaler-and-shifter as specific knowledge, (3) pre-trained model (PTM) generalization preserving, and (4) hard-soft updates mechanism. Our proposed method achieves significantly higher performance than the current SOTAs in CIFAR100, ImageNet-R, ImageNet-A, and CUB dataset. Our complexity analysis shows that our method requires a relatively smaller number of parameters and achieves moderate training time, inference time, and throughput. For further study, the source code of our method is available at https://github.com/anwarmaxsum/PROL.
Generalization-Preserved Learning: Closing the Backdoor to Catastrophic Forgetting in Continual Deepfake Detection
Authors: Xueyi Zhang; Peiyin Zhu; Chengwei Zhang; Zhiyuan Yan; Jikang Cheng; Mingrui Lao; Siqi Cai; Yanming Guo
Venue: ICCV 2025 (main)

Abstract: Existing continual deepfake detection methods typically treat stability (retaining previously learned forgery knowl- edge) and plasticity (adapting to novel forgeries) as con- flicting properties, emphasizing an inherent trade-off be- tween them, while regarding generalization to unseen forg- eries as secondary. In contrast, we reframe the problem: stability and plasticity can coexist and be jointly improved through the model's inherent generalization. Specifically, we propose Generalization-Preserved Learning (GPL), a novel framework consisting of two key components: (1) Hy- perbolic Visual Alignment, which introduces learnable wa- termarks to align incremental data with the base set in hy- perbolic space, alleviating inter-task distribution shifts; (2) Generalized Gradient Projection, which prevents parame- ter updates that conflict with generalization constraints, en- suring new knowledge learning does not interfere with pre- viously acquired knowledge. Notably, GPL requires nei- ther backbone retraining nor historical data storage. Ex- periments conducted on four mainstream datasets (FF++, Celeb-DF v2, DFD, and DFDCP) demonstrate that GPL achieves an accuracy of 92.14%, outperforming replay- based state-of-the-art methods by 2.15%, while reducing forgetting by 2.66%. Moreover, GPL achieves an 18.38% improvement on unseen forgeries using only 1% of baseline parameters, thus presenting an efficient adaptation to con- tinuously evolving forgery techniques.
JailbreakDiffBench: A Comprehensive Benchmark for Jailbreaking Diffusion Models
Authors: Xiaolong Jin; Zixuan  Weng; Hanxi Guo; Chenlong Yin; Siyuan Cheng; Guangyu Shen; Xiangyu Zhang
Venue: ICCV 2025 (main)

Abstract: Diffusion models are widely used in real-world applications, but ensuring their safety remains a major challenge. Despite many efforts to enhance the security of diffusion models, jailbreak and adversarial attacks can still bypass these defenses, generating harmful content. However, the lack of standardized evaluation makes it difficult to assess the robustness of diffusion model system. To address this, we introduce JailbreakDiffBench, a comprehensive benchmark for systematically evaluating the safety of diffusion models against various attacks and under different defenses. Our benchmark includes a high-quality, human-annotated prompt and image dataset covering diverse attack scenarios. It consists of two key components: (1) an evaluation protocol to measure the effectiveness of moderation mechanisms and (2) an attack assessment module to benchmark adversarial jailbreak strategies. Through extensive experiments, we analyze existing filters and reveal critical weaknesses in current safety measures. JailbreakDiffBench is designed to support both text-to-image and text-to-video models, ensuring extensibility and reproducibility.
InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity
Authors: Liming Jiang; Qing Yan; Yumin Jia; Zichuan Liu; Hao Kang; Xin Lu
Venue: ICCV 2025 (main)

Abstract: Achieving flexible and high-fidelity identity-preserved image generation remains formidable, particularly with advanced Diffusion Transformers (DiTs) like FLUX. We introduce InfiniteYou (InfU), one of the earliest robust frameworks leveraging DiTs for this task. InfU addresses significant issues of existing methods, such as insufficient identity similarity, poor text-image alignment, and low generation quality and aesthetics. Central to InfU is InfuseNet, a component that injects identity features into the DiT base model via residual connections, enhancing identity similarity while maintaining generation capabilities. A multi-stage training strategy, including pretraining and supervised fine-tuning (SFT) with synthetic single-person-multiple-sample (SPMS) data, further improves text-image alignment, ameliorates image quality, and alleviates face copy-pasting. Extensive experiments demonstrate that InfU achieves state-of-the-art performance, surpassing existing baselines. In addition, the plug-and-play design of InfU ensures compatibility with various existing methods, offering a valuable contribution to the broader community.