GenAI4ELab/papercli-papers huggingface dataset:
SMSTracker: Tri-path Score Mask Sigma Fusion for Multi-Modal Tracking
Authors: Sixian  Chan; Zedong Li; Wenhao Li; Shijian Lu; Chunhua Shen; Xiaoqin  Zhang
Venue: ICCV 2025 (main)

Abstract: Multi-modal object tracking has emerged as a significant research focus in computer vision due to its robustness in complex environments, such as exposure variations, blur, and occlusions. Despite existing studies integrating supplementary modal information into pre-trained RGB trackers through visual prompt mechanisms, this approach exhibits a critical limitation: it inherently prioritizes RGB information as the dominant modality, thereby underutilizing the complementary information of alternative modalities. To address this fundamental limitation, we present SMSTracker, an innovative tri-path score mask sigma fusion framework for multi-modal tracking, including three key modules. Firstly, we design a tri-path Score Mask Fusion (SMF) module to evaluate and quantify the reliability of each modality, allowing optimal exploitation of complementary features between modalities. Secondly, we introduce a pioneering Sigma Interaction (SGI) module to facilitate a sophisticated fusion of modal features across tri-branches. Furthermore, we advance a Drop Key Fine-tuning (DKF) strategy to address the inherent challenge of unequal data contribution in multi-modal learning scenarios, thereby enhancing the model's capacity for comprehensive multi-modal information processing. Finally, extensive experiments on RGB+Thermal, RGB+Depth, and RGB+Event datasets demonstrate the significant performance improvements achieved by SMSTracker over existing state-of-the-art methods. Code and model are available at https://github.com/Leezed525/SMSTracker.
Distilling Parallel Gradients for Fast ODE Solvers of Diffusion Models
Authors: Beier Zhu; Ruoyu Wang; Tong Zhao; Hanwang Zhang; Chi Zhang
Venue: ICCV 2025 (main)

Abstract: Diffusion models (DMs) have achieved state-of-the-art generative performance but suffer from high sampling latency due to their sequential denoising nature. Existing solver-based acceleration methods often face image quality degradation under a low-latency budget. In this paper, we propose the Ensemble Parallel Direction solver (dubbed as EPD-Solver), a novel ODE solver that mitigates truncation errors by incorporating multiple parallel gradient evaluations in each ODE step. Importantly, since the additional gradient computations are independent, they can be fully parallelized, preserving low-latency sampling. Our method optimizes a small set of learnable parameters in a distillation fashion, ensuring minimal training overhead. In addition, our method can serve as a plugin to improve existing ODE samplers. Extensive experiments on various image synthesis benchmarks demonstrate the effectiveness of our EPD-Solver in achieving high-quality and low-latency sampling. For example, at the same latency level of 5 NFE, EPD achieves an FID of 4.47 on CIFAR-10, 7.97 on FFHQ, 8.17 on ImageNet, and 8.26 on LSUN Bedroom, surpassing existing learning-based solvers by a significant margin. Codes are available in https://github.com/BeierZhu/EPD.
Aligning Constraint Generation with Design Intent in Parametric CAD
Authors: Evan Casey; Tianyu Zhang; Shu Ishida; John Roger Thompson; Amir Khasahmadi; Joseph George Lambourne; Pradeep Kumar Jayaraman; Karl D.D. Willis
Venue: ICCV 2025 (main)

Abstract: We introduce alignment techniques from reasoning large language models (LLMs) to the task of generating engineering sketch constraints in computer-aided design (CAD) models. Engineering sketches are composed of geometric primitives (such as points and lines) connected by constraints (such as perpendicularity and tangency) that define their relationships. For a design to remain easily editable, these constraints must accurately capture design intent, ensuring that geometry updates predictably as parameters change. While current methods can generate CAD designs, aligning model outputs with design intent--what we call "design alignment"--remains an open challenge. A crucial first step is to generate constraints that fully constrain all geometric primitives without over-constraining or distorting the sketch geometry. By training an existing constraint generation model with alignment techniques and feedback from a constraint solver, we achieve a 93% rate of fully-constrained sketches, compared to 34% using a naive supervised fine-tuning (SFT) baseline and only 8.9% without SFT. Our approach is model-agnostic and paves the way for further research bridging alignment strategies between language and design domains.
DisCo: Towards Distinct and Coherent Visual Encapsulation in Video MLLMs
Authors: Jiahe Zhao; Rongkun Zheng; Yi Wang; Helin Wang; Hengshuang Zhao
Venue: ICCV 2025 (main)

Abstract: In video Multimodal Large Language Models (video MLLMs), the visual encapsulation process plays a pivotal role in converting video contents into representative tokens for LLM input. While linear projectors are widely employed for encapsulation, they introduce semantic indistinctness and temporal incoherence when applied to videos. Conversely, the structure of resamplers shows promise in tackling these challenges, but an effective solution remains unexplored. Drawing inspiration from resampler structures, we introduce DisCo, a novel visual encapsulation method designed to yield semantically distinct and temporally coherent visual tokens for video MLLMs. DisCo integrates two key components: (1) A Visual Concept Discriminator (VCD) module, assigning unique semantics for visual tokens by associating them in pair with discriminative concepts in the video. (2) A Temporal Focus Calibrator (TFC) module, ensuring consistent temporal focus of visual tokens to video elements across every video frame. Through extensive experiments on multiple video MLLM frameworks, we demonstrate that DisCo remarkably outperforms previous state-of-the-art methods across a variety of video understanding benchmarks, while also achieving higher token efficiency thanks to the reduction of semantic indistinctness.
PUMA: Empowering Unified MLLM with Multi-granular Visual Generation
Authors: Rongyao Fang; Chengqi Duan; Kun Wang; Hao Li; Linjiang Huang; Hao Tian; Xingyu Zeng; Rui Zhao; Jifeng Dai; Hongsheng Li; Xihui Liu
Venue: ICCV 2025 (main)

Abstract: Recent advancements in multimodal foundation models have yielded significant progress in vision-language understanding. Initial attempts have also explored the potential of multimodal large language models for visual content generation. However, existing approaches face a trade-off between generation diversity and controllability, struggling to meet the varying granularity demands of different image generation tasks within a unified MLLM framework. In this work, we propose PUMA, emPowering Unified MLLM with Multi-grAnular visual generation, a novel paradigm that tackles the diversity-controllability trade-off. PUMA achieves this by unifying multi-granular visual features as both inputs and outputs of MLLMs, thus effectively meeting the distinct granularity needs for diverse generation and precise manipulation within a single framework. Following multimodal pretraining and instruction tuning, PUMA demonstrates remarkable capabilities in a wide range of multimodal tasks, including image understanding, diverse text-to-image generation, editing, inpainting, colorization, and conditional generation. This work marks a significant stride towards realizing truly unified MLLMs capable of seamlessly adapting to the diverse granularity demands and task requirements inherent in various visual tasks. The code and model will be released upon acceptance.
Axis-level Symmetry Detection with Group-Equivariant Representation
Authors: Wongyun Yu; Ahyun Seo; Minsu Cho
Venue: ICCV 2025 (main)

Abstract: Symmetry is a fundamental concept that has been extensively studied, yet detecting it in complex scenes remains a significant challenge in computer vision. Recent heatmap-based approaches can localize potential regions of symmetry axes but often lack precision in identifying individual axes. In this work, we propose a novel framework for axis-level detection of the two most common symmetry types--reflection and rotation--by representing them as explicit geometric primitives, i.e., lines and points. Our method employs a dual-branch architecture that is equivariant to the dihedral group, with each branch specialized to exploit the structure of dihedral group-equivariant features for its respective symmetry type. For reflection symmetry, we introduce \orientational anchors, aligned with group components, to enable orientation-specific detection, and a reflectional matching that measures similarity between patterns and their mirrored counterparts across candidate axes. For rotational symmetry, we propose a rotational matching that compares patterns at fixed angular intervals to identify rotational centers. Extensive experiments demonstrate that our method achieves state-of-the-art performance, outperforming existing approaches.
SMP-Attack: Boosting the Transferability of Feature Importance-based Adversarial Attack with Semantics-aware Multi-granularity Patchout
Authors: Wen Yang; Guodong Liu; Di Ming
Venue: ICCV 2025 (main)

Abstract: Transfer-based attacks pose a significant security threat to deep neural networks (DNNs), due to their strong performance on unseen models in real-world black-box scenarios. Building on this, feature importance-based attacks further improve the transferability of adversarial examples by effectively suppressing model-specific feature patterns. However, existing methods primarily focus on single-granularity patch and single-stage training, leading to suboptimal solutions. To address these limitations, we propose a general multi-stage optimization framework based on Semantics-aware Multi-granularity Patchout, dubbed as SMP-Attack. Compared to the non-deformable/regular patch definition, we incorporate multi-granularity into the generation process of deformable/irregular patches, thereby enhancing the quality of the computed aggregate gradient. In contrast to conventional joint optimization of multi-layer losses, we introduce an effective multi-stage training strategy that systematically explores significant model-agnostic features from shallow to intermediate layers. Employing the ImageNet dataset, we conduct extensive experiments on undefended/defended CNNs and ViTs, which unequivocally demonstrate the superior performance of our proposed SMP-Attack over current state-of-the-art methods in black-box scenarios. Furthermore, we assess the compatibility of our multi-stage optimization, which supersedes single-stage training employed in existing feature-based methods, culminating in substantial performance improvement. Code is available at https://github.com/AdvML-Group/SMP-Attack.
Referring to Any Person
Authors: Qing Jiang; Lin Wu; Zhaoyang Zeng; Tianhe Ren; Yuda Xiong; Yihao Chen; Liu Qin; Lei Zhang
Venue: ICCV 2025 (main)

Abstract: Humans are undoubtedly the most important participants in computer vision, and the ability to detect any individual given a natural language description, a task we define as referring to any person, holds substantial practical value. However, we find that existing models generally fail to achieve real-world usability, and current benchmarks are limited by their focus on one-to-one referring, that hinder progress in this area. In this work, we revisit this task from three critical perspectives: task definition, dataset design, and model architecture. We first identify five aspects of referable entities and three distinctive characteristics of this task. Next, we introduce HumanRef, a novel dataset designed to tackle these challenges and better reflect real-world applications. From a model design perspective, we integrate a multimodal large language model with an object detection framework, constructing a robust referring model named RexSeek. Experimental results reveal that state-of-the-art models, which perform well on commonly used benchmarks like RefCOCO/+/g, struggle with HumanRef due to their inability to detect multiple individuals. In contrast, RexSeek not only excels in human referring but also generalizes effectively to common object referring, making it broadly applicable across various perception tasks.
GWM: Towards Scalable Gaussian World Models for Robotic Manipulation
Authors: Guanxing Lu; Baoxiong Jia; Puhao Li; Yixin Chen; Ziwei Wang; Yansong Tang; Siyuan Huang
Venue: ICCV 2025 (main)

Abstract: Training robot policies within a learned world model is trending due to the inefficiency of real-world interactions. The established image-based world models and policies have shown prior success, but lack robust geometric information that requires consistent spatial and physical understanding of the three-dimensional world, even pre-trained on internet-scale video sources. To this end, we propose a novel branch of world model named Gaussian World Model (GWM) for robotic manipulation, which reconstructs the future state by inferring the propagation of Gaussian primitives under the effect of robot actions. At its core is a latent Diffusion Transformer (DiT) combined with a 3D variational autoencoder, enabling fine-grained scene-level future state reconstruction with Gaussian Splatting. GWM can not only enhance the visual representation for imitation learning agent by self-supervised future prediction training, but can serve as a neural simulator that supports model-based reinforcement learning. Both simulated and real-world experiments depict that GWM can precisely predict future scenes conditioned on diverse robot actions, and can be further utilized to train policies that outperform the state-of-the-art by impressive margins, showcasing the initial data scaling potential of 3D world model.
Statistical Confidence Rescoring for Robust 3D Scene Graph Generation from Multi-View Images
Authors: Qi Xun Yeo; Yanyan Li; Gim Hee Lee
Venue: ICCV 2025 (main)

Abstract: Modern 3D semantic scene graph estimation methods utilize ground truth 3D annotations to accurately predict target objects, predicates, and relationships. In the absence of given 3D ground truth representations, we explore leveraging only multi-view RGB images to tackle this task. To attain robust features for accurate scene graph estimation, we must overcome the noisy reconstructed pseudo point-based geometry from predicted depth maps and reduce the amount of background noise present in multi-view image features. The key is to enrich node and edge features with accurate semantic and spatial information and through neighbouring relations. We obtain semantic masks to guide feature aggregation to filter background features and design a novel method to incorporate neighbouring node information to aid robustness of our scene graph estimates. Furthermore, we leverage on explicit statistical priors calculated from the training summary statistics to refine node and edge predictions based on their one-hop neighborhood. Our experiments show that our method outperforms current methods purely using multi-view images as the initial input. Our project page is available at https://qixun1.github.io/projects/SCRSSG.
Draw Your Mind: Personalized Generation via Condition-Level Modeling in Text-to-Image Diffusion Models
Authors: Hyungjin Kim; Seokho Ahn; Young-Duk Seo
Venue: ICCV 2025 (main)

Abstract: Personalized generation in T2I diffusion models aims to naturally incorporate individual user preferences into the generation process with minimal user intervention. However, existing studies primarily rely on prompt-level modeling with large-scale models, often leading to inaccurate personalization due to the limited input token capacity of T2I diffusion models. To address these limitations, we propose DrUM, a novel method that integrates user profiling with a transformer-based adapter to enable personalized generation through condition-level modeling in the latent space. DrUM demonstrates strong performance on large-scale datasets and seamlessly integrates with open-source text encoders, making it compatible with widely used foundation T2I models without requiring additional fine-tuning.
CopyrightShield: Enhancing Diffusion Model Security Against Copyright Infringement Attacks
Authors: Zhixiang Guo; Siyuan Liang; Aishan Liu; Dacheng Tao
Venue: ICCV 2025 (main)

Abstract: Diffusion models have attracted significant attention due to its exceptional data generation capabilities in fields such as image synthesis. However, recent studies have shown that diffusion models are vulnerable to copyright infringement attacks, where attackers inject strategically modified non-infringing images into the training set, inducing the model to generate infringing content under the prompt of specific poisoned captions. To address this issue, we first propose a defense framework, CopyrightShield, to defend against the above attack. Specifically, we analyze the memorization mechanism of diffusion models and find that attacks exploit the model's overfitting to specific spatial positions and prompts, causing it to reproduce poisoned samples under backdoor triggers. Based on this, we propose a poisoned sample detection method using spatial masking and data attribution to quantify poisoning risk and accurately identify hidden backdoor samples. To further mitigate memorization of poisoned features, we introduce an adaptive optimization strategy that integrates a dynamic penalty term into the training loss, reducing reliance on infringing features while preserving generative performance. Experimental results demonstrate that CopyrightShield significantly improves poisoned sample detection performance across two attack scenarios, achieving average F1-scores of 0.665, retarding the First-Attack Epoch (FAE) of 115. 2% and decreasing the Copyright Infringement Rate (CIR) by 56.7%. Compared to the SoTA backdoor defense in diffusion models, the defense effect is improved by about 25%, showcasing its superiority and practicality in enhancing the security of diffusion models.
PixTalk: Controlling Photorealistic Image Processing and Editing with Language
Authors: Marcos V. Conde; Zihao Lu; Radu Timofte
Venue: ICCV 2025 (main)

Abstract: Text-guided image generation and editing is emerging as a fundamental problem in computer vision. However, most approaches lack control, and the generated results are far from professional photography quality standards. In this work, we propose the first approach that introduces language and explicit control into the image processing and editing pipeline. PixTalk is a vision-language multi-task image processing model, guided using text instructions. Our method is able to perform over 40 transformations --the most popular techniques in photography--, delivering results as professional photography editing software. Our model can process 12MP images on consumer GPUs in real-time (under 1 second). As part of this effort, we propose a novel dataset and benchmark for new research on multi-modal image processing and editing.
Learning Streaming Video Representation via Multitask Training
Authors: Yibin Yan; Jilan Xu; Shangzhe Di; Yikun Liu; Yudi Shi; Qirui Chen; Zeqian Li; Yifei Huang; Weidi Xie
Venue: ICCV 2025 (main)

Abstract: Understanding continuous video streams plays a fundamental role in real-time applications, including embodied AI and autonomous driving. Unlike offline video processing, streaming video understanding requires the ability to process video streams frame by frame, preserve historical information, and make low-latency decisions. To address these challenges, our main contributions are three-fold. (i) we develop a novel streaming video backbone, termed as StreamFormer, by incorporating causal temporal attention into a pre-trained vision transformer. This enables efficient streaming video processing while maintaining image representation capability. (ii) to train StreamFormer, we propose to unify diverse spatial-temporal video understanding tasks within a multitask visual-language alignment framework. Hence, StreamFormer learns global semantics, temporal dynamics, and fine-grained spatial relationships simultaneously. (iii) we conduct extensive experiments for online action detection, online video instance segmentation, and video question answering. StreamFormer achieves competitive performance while maintaining efficiency, demonstrating its potential for real-time applications.
DASH: 4D Hash Encoding with Self-Supervised Decomposition for Real-Time Dynamic Scene Rendering
Authors: Jie Chen; Zhangchi Hu; Peixi Wu; Huyue Zhu; Hebei Li; Xiaoyan Sun
Venue: ICCV 2025 (main)

Abstract: Dynamic scene reconstruction is a long-term challenge in 3D vision. Existing plane-based methods in dynamic Gaussian splatting suffer from an unsuitable low-rank assumption, causing feature overlap and poor rendering quality. Although 4D hash encoding provides an explicit representation without low-rank constraints, directly applying it to the entire dynamic scene leads to substantial hash collisions and redundancy. To address these challenges, we present DASH, a real-time dynamic scene rendering framework that employs 4D hash encoding coupled with self-supervised decomposition. Our approach begins with a self-supervised decomposition mechanism that separates dynamic and static components without manual annotations or precomputed masks. Next, we introduce a multiresolution 4D hash encoder for dynamic elements, providing an explicit representation that avoids the low-rank assumption. Finally, we present a spatio-temporal smoothness regularization strategy to mitigate unstable deformation artifacts. Experiments on real-world datasets demonstrate that DASH achieves state-of-the-art dynamic rendering performance, exhibiting enhanced visual quality at real-time speeds of 264 FPS on a single 4090 GPU. Code: https://github.com/chenj02/DASH.
Pose-Star: Anatomy-Aware Editing for Open-World Fashion Images
Authors: Yuran Dong; Mang Ye
Venue: ICCV 2025 (main)

Abstract: To advance real-world fashion image editing, we analyze existing two-stage pipelines--mask generation followed by diffusion-based editing--which overly prioritize generator optimization while neglecting mask controllability. This results in two critical limitations: I) poor user-defined flexibility (coarse-grained human masks restrict edits to predefined regions like upper torso; fine-grained clothes masks preserve poses but forbid style/length customization). II) weak pose robustness (mask generators fail due to articulated poses and miss rare regions like waist, while human parsers remain limited by predefined categories).To address these gaps, we propose Pose-Star, a framework that dynamically recomposes body structures (e.g., neck, chest, etc.) into anatomy-aware masks (e.g., chest-length) for user-defined edits. In Pose-Star, we calibrate diffusion-derived attention (Star tokens) via skeletal keypoints to enhance rare structure localization in complex poses, suppress noise through phase-aware analysis of attention dynamics (Convergence-Stabilization-Divergence) with threshold masking and sliding-window fusion, and refine edges via cross-self attention merging and Canny alignment. This work bridges controlled benchmarks and open-world demands, pioneering anatomy-aware, pose-robust editing and laying the foundation for industrial fashion image editing.