GenAI4ELab/papercli-papers huggingface dataset:
SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography Images
Authors: Yichi Zhang; Le Xue; Wenbo Zhang; Lanlan Li; Yuchen Liu; Chen Jiang; Yuan Cheng; Yuan Qi
Venue: ICCV 2025 (main)

Abstract: Positron Emission Tomography (PET) is a powerful molecular imaging tool that plays a crucial role in modern medical diagnostics by visualizing radio-tracer distribution to reveal physiological processes. Accurate organ segmentation from PET images is essential for comprehensive multi-systemic analysis of interactions between different organs and pathologies.Existing segmentation methods are limited by insufficient annotation data and varying levels of annotation, resulting in weak generalization ability and difficulty in clinical application. Recent developments in segmentation foundation models have shown superior versatility across diverse segmentation tasks. Despite the efforts of medical adaptations, these works primarily focus on structural medical images with detailed physiological structural information and exhibit limited generalization performance on molecular PET imaging. In this paper, we collect and construct PETS-5k, the largest PET segmentation dataset to date, comprising 5,731 three-dimensional whole-body PET images and encompassing over 1.3M 2D images. Based on the established dataset, we develop SegAnyPET, a modality-specific 3D foundation model for universal promptable segmentation from PET images. To issue the challenge of discrepant annotation quality, we adopt a cross prompting confident learning (CPCL) strategy with an uncertainty-guided self-rectification process to robustly learn segmentation from high-quality labeled data and low-quality noisy labeled data for promptable segmentation. Experimental results demonstrate that SegAnyPET can segment seen and unseen target organs using only one or a few prompt points, outperforming state-of-the-art foundation models and task-specific fully supervised models with higher accuracy and strong generalization ability for universal segmentation. As the first foundation model for PET images, we believe that SegAnyPET will advance the applications to various downstream tasks for molecular imaging.
StolenLoRA: Exploring LoRA Extraction Attacks via Synthetic Data
Authors: Yixu Wang; Yan Teng; Yingchun Wang; Xingjun Ma
Venue: ICCV 2025 (main)

Abstract: Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA have transformed vision model adaptation, enabling the rapid deployment of customized models. However, the compactness of LoRA adaptations introduces new safety concerns, particularly their vulnerability to model extraction attacks. This paper introduces a new focus of model extraction attacks named LoRA extraction that extracts LoRA-adaptive models based on a public pre-trained model. We then propose a novel extraction method called StolenLoRA which trains a substitute model to extract the functionality of a LoRA-adapted model using synthetic data. StolenLoRA leverages a Large Language Model to craft effective prompts for data generation, and it incorporates a Disagreement-based Semi-supervised Learning (DSL) strategy to maximize information gain from limited queries.Our experiments demonstrate the effectiveness of StolenLoRA, achieving up to a 96.60% attack success rate with only 10k queries, even in cross-backbone scenarios where the attacker and victim models utilize different pre-trained backbones. These findings reveal the specific vulnerability of LoRA-adapted models to this type of extraction and underscore the urgent need for robust defense mechanisms tailored to PEFT methods.We also explore a preliminary defense strategy based on diversified LoRA deployments, highlighting its potential to mitigate such attacks.
The Best of Both Worlds: Integrating Language Models and Diffusion Models for Video Generation
Authors: Aoxiong Yin; Xu Tan; Kai Shen; Yichong Leng; Xinyu Zhou; Juncheng Li; Siliang Tang
Venue: ICCV 2025 (main)

Abstract: Recent advancements in text-to-video (T2V) generation have been driven by two competing paradigms: autoregressive language models and diffusion models. However, each paradigm has intrinsic limitations: language models struggle with visual quality and error accumulation, while diffusion models lack semantic understanding and causal modeling. In this work, we propose LanDiff, a hybrid framework that synergizes the strengths of both paradigms through coarse-to-fine generation. Our architecture introduces three key innovations: (1) a semantic tokenizer that compresses 3D visual features into compact 1D discrete representations through efficient semantic compression, achieving a ~14,000xcompression ratio; (2) a language model that generates semantic tokens with high-level semantic relationships; (3) a streaming diffusion model that refines coarse semantics into high-fidelity videos. Experiments show that LanDiff, a 5B model, achieves a score of 85.43 on the VBench T2V benchmark, surpassing the state-of-the-art open-source models Hunyuan Video (13B) and other commercial models such as Sora, Kling, and Hailuo. Furthermore, our model also achieves state-of-the-art performance in long video generation, surpassing other open-source models in this field. Our demo can be viewed at https://landiff.github.io/.
SV4D 2.0: Enhancing Spatio-Temporal Consistency in Multi-View Video Diffusion for High-Quality 4D Generation
Authors: Chun-Han Yao; Yiming Xie; Vikram Voleti; Huaizu Jiang; Varun Jampani
Venue: ICCV 2025 (main)

Abstract: We present Stable Video 4D 2.0 (SV4D 2.0), a multi-view video diffusion model for dynamic 3D asset generation. Compared to its predecessor SV4D, SV4D 2.0 is more robust to occlusions and large motion, generalizes better to real-world videos, and produces higher-quality outputs in terms of detail sharpness and spatio-temporal consistency. We achieve this by introducing key improvements in multiple aspects: 1) network architecture: eliminating the dependency of reference multi-views and designing blending mechanism for 3D and frame attention, 2) data: enhancing quality and quantity of training data, 3) training strategy: adopting progressive 3D-4D training for better generalization, and 4) 4D optimization: handling 3D inconsistency and large motion via 2-stage refinement and progressive frame sampling. Extensive experiments demonstrate significant performance gain by SV4D 2.0 both visually and quantitatively, achieving better detail (-14% LPIPS) and 4D consistency (-44% FV4D) in novel-view video synthesis and 4D optimization (-12% LPIPS and -24% FV4D) compared to SV4D.
Long Context Tuning for Video Generation
Authors: Yuwei Guo; Ceyuan Yang; Ziyan Yang; Zhibei Ma; Zhijie Lin; Zhenheng Yang; Dahua Lin; Lu Jiang
Venue: ICCV 2025 (main)

Abstract: Recent advances in video generation can produce realistic, minute-long single-shot videos with scalable diffusion transformers. However, real-world narrative videos require multi-shot scenes with visual and dynamic consistency across shots. In this work, we introduce Long Context Tuning (LCT), a training paradigm that expands the context window of pre-trained single-shot video diffusion models to learn scene-level consistency directly from data. Our method expands full attention mechanisms from individual shots to encompass all shots within a scene, incorporating interleaved 3D position embedding and an asynchronous noise strategy, enabling both joint and auto-regressive shot generation without additional parameters. Models with bidirectional attention after LCT can further be fine-tuned with context-causal attention, facilitating auto-regressive generation with efficient KV-cache. Experiments demonstrate single-shot models after LCT can produce coherent multi-shot scenes and exhibit emerging capabilities, including compositional generation and interactive shot extension, paving the way for more practical visual content creation.
TCFG: Truncated Classifier-Free Guidance for Efficient and Scalable Text-to-Image Acceleration
Authors: Xiaomeng Fu; Jia Li
Venue: ICCV 2025 (main)

Abstract: Diffusion models have achieved remarkable success in image and video generation due to their powerful generative capabilities. However, they suffer from slow inference speed and high computational costs. Existing acceleration methods for diffusion models may compromise model performance and struggle to generalize across diverse diffusion model architectures and downstream tasks. To address these issues, we propose a model-agnostic and highly scalable acceleration strategy for text-controlled image generation. Specifically, we dynamically modulate the text guidance coefficience and truncate redundant text-related computations during the denoising process. Experimental results demonstrate that our approach achieves significant model acceleration while preserving precise text-image alignment, showcasing the potential for a wide range of diffusion models and downstream applications.
Removing Out-of-Focus Reflective Flares via Color Alignment
Authors: Fengbo Lan; Chang Wen Chen
Venue: ICCV 2025 (main)

Abstract: Reflective flares are common artifacts in photography that degrade image quality, introducing in-focus flares, which appear as bright, regular spot patterns, and out-of-focus flares, which are diffuse and semi-transparent, obscuring the underlying scene. While previous methods have achieved some success in removing in-focus flares, they struggle with the diffuse nature of out-of-focus flares. The lack of an out-of-focus flare dataset has further hindered the development of effective flare removal models. In this work, we construct a large-scale out-of-focus flare dataset generated with Blender. We propose to use a color alignment approach on diffusion models to address the challenges of out-of-focus reflective flare removal. Rather than fully reconstructing flare-affected regions, our method adjusts the color distribution to reduce artifact visibility while preserving image content. Specifically, we apply a differentiable histogram loss on the diffusion model, which is derived from the Earth Mover's Distance (EMD), to effectively align color distributions. The proposed approach, trained exclusively on synthetic data, outperforms existing methods on both synthetic and real-world data, demonstrating improved performance in flare removal.
ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance
Authors: Chunwei Wang; Guansong Lu; Junwei Yang; Runhui Huang; Jianhua Han; Lu Hou; Wei Zhang; Hang Xu
Venue: ICCV 2025 (main)

Abstract: In this paper, we introduce ILLUME, a unified multimodal large language model (MLLM) that seamlessly integrates multimodal understanding and generation capabilities within a single large language model through a unified next-token prediction formulation.To address the large dataset size typically required for image-text alignment, we propose to enhance data efficiency through the design of a vision tokenizer that incorporates semantic information and a progressive multi-stage training procedure. This approach reduces the dataset size to just 15M for pretraining -- over four times fewer than what is typically needed -- while achieving competitive or even superior performance with existing unified MLLMs, such as Janus. Additionally, to promote synergistic enhancement between understanding and generation capabilities, which is under-explored in previous works, we introduce a novel self-enhancing multimodal alignment scheme. This scheme supervises the MLLM to self-assess the consistency between text descriptions and self-generated images, facilitating the model to interpret images more accurately and avoid unrealistic and incorrect predictions caused by misalignment in image generation. Based on our extensive experiments, our proposed ILLUME stands out and competes with state-of-the-art unified MLLMs and specialized models across various benchmarks for multimodal understanding, generation, and editing.
No Pose at All: Self-Supervised Pose-Free 3D Gaussian Splatting from Sparse Views
Authors: Ranran Huang; Krystian Mikolajczyk
Venue: ICCV 2025 (main)

Abstract: We introduce SPFSplat, an efficient framework for 3D Gaussian splatting from sparse multi-view images, requiring no ground-truth poses during training or inference. It employs a shared feature extraction backbone, enabling simultaneous prediction of 3D Gaussian primitives and camera poses in a canonical space from unposed inputs within a single feed-forward step. Alongside the rendering loss based on estimated novel-view poses, a reprojection loss is integrated to enforce the learning of pixel-aligned Gaussian primitives for enhanced geometric constraints. This pose-free training paradigm and efficient one-step feed-forward design make SPFSplat well-suited for practical applications. Remarkably, despite the absence of pose supervision, SPFSplat achieves state-of-the-art performance in novel view synthesis even under significant viewpoint changes and limited image overlap. It also surpasses recent methods trained with geometry priors in relative pose estimation. Code and trained models are available on our project page: https://ranrhuang.github.io/spfsplat/.
Skeleton Motion Words for Unsupervised Skeleton-Based Temporal Action Segmentation
Authors: Uzay Gökay; Federico Spurio; Dominik R. Bach; Juergen Gall
Venue: ICCV 2025 (main)

Abstract: Current state-of-the-art methods for skeleton-based temporal action segmentation are predominantly supervised and require annotated data, which is expensive to collect. In contrast, existing unsupervised temporal action segmentation methods have focused primarily on video data, while skeleton sequences remain underexplored, despite their relevance to real-world applications, robustness, and privacy-preserving nature. In this paper, we propose a novel approach for unsupervised skeleton-based temporal action segmentation. Our method utilizes a sequence-to-sequence temporal autoencoder that keeps the information of the different joints disentangled in the embedding space. Latent skeleton sequences are then divided into non-overlapping patches and quantized to obtain distinctive skeleton motion words, driving the discovery of semantically meaningful action clusters. We thoroughly evaluate the proposed approach on three widely used skeleton-based datasets, namely HuGaDB, LARa, and BABEL. The results demonstrate that our model outperforms the current state-of-the-art unsupervised temporal action segmentation methods.
Towards Robust Defense against Customization via Protective Perturbation Resistant to Diffusion-based Purification
Authors: Wenkui Yang; Jie Cao; Junxian Duan; Ran He
Venue: ICCV 2025 (main)

Abstract: Diffusion models like Stable Diffusion have become prominent in visual synthesis tasks due to their powerful customization capabilities, which also introduce significant security risks, including deepfakes and copyright infringement. In response, a class of methods known as protective perturbation emerged, which mitigates image misuse by injecting imperceptible adversarial noise. However, purification can remove protective perturbations, thereby exposing images again to the risk of malicious forgery. In this work, we formalize the anti-purification task, highlighting challenges that hinder existing approaches, and propose a simple diagnostic protective perturbation named AntiPure. AntiPure exposes vulnerabilities of purification within the "purification-customization" workflow, owing to two guidance mechanisms: 1) Patch-wise Frequency Guidance, which reduces the model's influence over high-frequency components in the purified image, and 2) Erroneous Timestep Guidance, which disrupts the model's denoising strategy across different timesteps. With additional guidance, AntiPure embeds imperceptible perturbations that persist under representative purification settings, achieving effective post-customization distortion. Experiments show that, as a stress test for purification, AntiPure achieves minimal perceptual discrepancy and maximal distortion, outperforming other protective perturbation methods within the purification-customization workflow.
EmbodiedOcc: Embodied 3D Occupancy Prediction for Vision-based Online Scene Understanding
Authors: Yuqi Wu; Wenzhao Zheng; Sicheng Zuo; Yuanhui Huang; Jie Zhou; Jiwen Lu
Venue: ICCV 2025 (main)

Abstract: 3D occupancy prediction provides a comprehensive description of the surrounding scenes and has become an essential task for 3D perception. Most existing methods focus on offline perception from one or a few views and cannot be applied to embodied agents that demand to gradually perceive the scene through progressive embodied exploration. In this paper, we formulate an embodied 3D occupancy prediction task to target this practical scenario and propose a Gaussian-based EmbodiedOcc framework to accomplish it. We initialize the global scene with uniform 3D semantic Gaussians and progressively update local regions observed by the embodied agent. For each update, we extract semantic and structural features from the observed image and efficiently incorporate them via deformable cross-attention to refine the regional Gaussians. Finally, we employ Gaussian-to-voxel splatting to obtain the global 3D occupancy from the updated 3D Gaussians. Our EmbodiedOcc assumes an unknown (i.e., uniformly distributed) environment and maintains an explicit global memory of it with 3D Gaussians. It gradually gains knowledge through the local refinement of regional Gaussians, which is consistent with how humans understand new scenes through embodied exploration. We reorganize an EmbodiedOcc-ScanNet benchmark based on local annotations to facilitate the evaluation of the embodied 3D occupancy prediction task. Our EmbodiedOcc outperforms existing methods by a large margin and accomplishes the embodied occupancy prediction with high accuracy and efficiency. Code: https://github.com/YkiWu/EmbodiedOcc.
MAVias: Mitigate any Visual Bias
Authors: Ioannis Sarridis; Christos Koutlis; Symeon Papadopoulos; Christos Diou
Venue: ICCV 2025 (main)

Abstract: Mitigating biases in computer vision models is an essential step towards trustworthy artificial intelligence systems. Existing bias mitigation methods are limited to predefined biases, preventing their use in visual datasets where multiple, possibly unknown biases exist. To address this limitation, we introduce MAVias, an open-set bias mitigation approach that leverages foundation models to discover spurious associations between visual attributes and target classes. MAVias first captures a wide variety of visual features in natural language via a foundation image tagging model, and then leverages a large language model to select visual features that define the target class, resulting in a set of language-coded potential visual biases. It then translates these biases into vision-language embeddings and introduces an in-processing bias mitigation approach to prevent the model from encoding information related to them. Experiments on diverse datasets, including CelebA, Waterbirds, ImageNet, and UrbanCars, show that MAVias effectively detects and mitigates a wide range of biases in visual recognition tasks, outperforming current state-of-the-art.
Calibrating MLLM-as-a-judge via Multimodal Bayesian Prompt Ensembles
Authors: Eric Slyman; Mehrab Tanjim; Kushal Kafle; Stefan Lee
Venue: ICCV 2025 (main)

Abstract: Multimodal large language models (MLLMs) are increasingly used to evaluate text-to-image (TTI) generation systems, providing automated judgments based on visual and textual context. However, these "judge" models often suffer from biases, overconfidence, and inconsistent performance across diverse image domains. While prompt ensembling has shown promise for mitigating these issues in unimodal, text-only settings, our experiments reveal that standard ensembling methods fail to generalize effectively for TTI tasks. To address these limitations, we propose a new multimodal-aware method called **M**ultimodal **M**ixture-of-**B**ayesian Prompt Ensembles (MMB). Our approach uses a Bayesian prompt ensemble approach augmented by image clustering, allowing the judge to dynamically assign prompt weights based on the visual characteristics of each sample. We show that MMB improves accuracy in pairwise preference judgments and greatly enhances calibration, making it easier to gauge the judge's true uncertainty. In evaluations on two TTI benchmarks, HPSv2 and MJBench, MMB outperforms existing baselines in alignment with human annotations and calibration across varied image content. Our findings highlight the importance of multimodal-specific strategies for judge calibration and suggest a promising path forward for reliable large-scale TTI evaluation.
SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models
Authors: Pingchuan Ma; Xiaopei Yang; Yusong Li; Ming Gui; Felix Krause; Johannes Schusterbauer; Björn Ommer
Venue: ICCV 2025 (main)

Abstract: Explicitly disentangling style and content in vision models remains challenging due to their semantic overlap and the subjectivity of human perception. Existing methods propose separation through generative or discriminative objectives, but they still face the inherent ambiguity of disentangling intertwined concepts. Instead, we ask: Can we bypass explicit disentanglement by learning to merge style and content invertibly, allowing separation to emerge naturally? We propose SCFlow, a flow-matching framework that learns bidirectional mappings between entangled and disentangled representations. Our approach is built upon three key insights: 1) Training solely to merge style and content, a well-defined task, enables invertible disentanglement without explicit supervision; 2) flow matching bridges on arbitrary distributions, avoiding the restrictive Gaussian priors of diffusion models and normalizing flows; and 3) a synthetic dataset of 510,000 samples was curated to simulate disentanglement through systematic style-content pairing. Beyond controllable generation tasks, we demonstrate that SCFlow generalizes to ImageNet-1k and WikiArt in zero-shot settings and achieves competitive performance, highlighting that disentanglement naturally emerges from the invertible merging process. Code and dataset: https://github.com/CompVis/SCFlow
Lay-Your-Scene: Natural Scene Layout Generation with Diffusion Transformers
Authors: Divyansh Srivastava; Xiang Zhang; He Wen; Chenru Wen; Zhuowen Tu
Venue: ICCV 2025 (main)

Abstract: We present Lay-Your-Scene (shorthand LayouSyn), a novel text-to-layout generation pipeline for natural scenes. Prior scene layout generation methods are either closed-vocabulary or use proprietary large language models for open-vocabulary generation, limiting their modeling capabilities and broader applicability in controllable image generation. In this work, we propose to use lightweight open-source language models to obtain scene elements from text prompts and a novel aspect-aware diffusion Transformer architecture trained in an open-vocabulary manner for conditional layout generation. Extensive experiments demonstrate that LayouSyn outperforms existing methods and achieves state-of-the-art performance on challenging spatial and numerical reasoning benchmarks. Additionally, we present two applications of LayouSyn: First, we show that coarse initialization from large language models can be seamlessly combined with our method to achieve better results. Second, we present a pipeline for adding objects to images, demonstrating the potential of LayouSyn in image editing applications.