GenAI4ELab/papercli-papers huggingface dataset:
I2VControl: Disentangled and Unified Video Motion Synthesis Control
Authors: Wanquan Feng; Tianhao Qi; Jiawei Liu; Mingzhen Sun; Pengqi Tu; Tianxiang Ma; Fei Dai; Songtao Zhao; Siyu Zhou; Qian He
Venue: ICCV 2025 (main)

Abstract: Motion controllability is crucial in video synthesis. However, most previous methods are limited to single control types, and combining them often results in logical conflicts. In this paper, we propose a disentangled and unified framework, namely I2VControl, to overcome the logical conflicts. We rethink camera control, object dragging, and motion brush, reformulating all tasks into a consistent representation based on point trajectories, each managed by a dedicated formulation. Accordingly, we propose a spatial partitioning strategy, where each unit is assigned to a concomitant control category, enabling diverse control types to be dynamically orchestrated within a single synthesis pipeline without conflicts. Furthermore, we design an adapter structure that functions as a plug-in for pre-trained models and is agnostic to specific model architectures. We conduct extensive experiments, achieving excellent performance on various control tasks, and our method further facilitates user-driven creative combinations, enhancing innovation and creativity. Project page: https://wanquanf.github.io/I2VControl.
Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens
Authors: Dongwon Kim; Ju He; Qihang Yu; Chenglin Yang; Xiaohui Shen; Suha Kwak; Liang-Chieh Chen
Venue: ICCV 2025 (main)

Abstract: Image tokenizers form the foundation of modern text-toimage generative models but are notoriously difficult to train. Furthermore, most existing text-to-image models rely on large-scale, high-quality private datasets, making them challenging to replicate. In this work, we introduce **T**ext-**A**ware **T**ransformer-based 1-D**i**mensional **Tok**enizer (TA-TiTok), an efficient and powerful image tokenizer that can utilize either discrete or continuous 1-dimensional tokens. TA-TiTok uniquely integrates textual information during the tokenizer decoding stage (i.e., de-tokenization), accelerating convergence and enhancing performance. TA-TiTok also benefits from a simplified, yet effective, one-stage training process, eliminating the need for the complex two-stage distillation used in previous 1-dimensional tokenizers. This design allows for seamless scalability to large datasets. Building on this, we introduce a family of text-to-image **Mask**ed **Gen**erative Models (MaskGen), trained exclusively on open data while achieving comparable performance to models trained on private data. We aim to release both the efficient, strong TA-TiTok tokenizers and the open-data, open-weight MaskGen models to promote broader access and democratize the field of text-to-image masked generative models.
Aligning Global Semantics and Local Textures in Generative Video Enhancement
Authors: Zhikai Chen; Fuchen Long; Zhaofan Qiu; Ting Yao; Wengang Zhou; Jiebo Luo; Tao Mei
Venue: ICCV 2025 (main)

Abstract: Recent advances in video generation have demonstrated the utility of powerful diffusion models. One important direction among them is to enhance the visual quality of the AI-synthesized videos for artistic creation. Nevertheless, solely relying on the knowledge embedded in the pre-trained video diffusion models might limit the generalization ability of local details (e.g., texture). In this paper, we address this issue by exploring the visual cues from a high-quality (HQ) image reference to facilitate visual details generation in video enhancement. We present GenVE, a new recipe of generative video enhancement framework that pursues the semantic and texture alignment between HQ image reference and denoised video in diffusion. Technically, GenVE first leverages an image diffusion model to magnify a key frame of the input video to attain a semantics-aligned HQ image reference. Then, a video controller is integrated into 3D-UNet to capture patch-level texture of the image reference to enhance fine-grained details generation at the corresponding region of low-quality (LQ) video. Moreover, a series of conditioning augmentation strategies are implemented for effective model training and algorithm robustness. Extensive experiments conducted on the public YouHQ40 and VideoLQ, as well as self-built AIGC-Vid dataset, quantitatively and qualitatively demonstrate the efficacy of our GenVE over the state-of-the-art video enhancement approaches.
VISO: Accelerating In-orbit Object Detection with Language-Guided Mask Learning and Sparse Inference
Authors: Meiqi Wang; Han Qiu
Venue: ICCV 2025 (main)

Abstract: In-orbit object detection is essential for Earth observation missions on satellites equipped with GPUs. A promising approach is to use pre-trained vision-language modeling (VLM) to enhance its open-vocabulary capability. However, adopting it on satellites poses two challenges: (1) satellite imagery differs substantially from natural images, and (2) satellites' embedded GPUs are insufficient for complex models' inference. We reveal their lack of a crucial prior: in-orbit detection involves identifying a set of known objects within a cluttered yet monotonous background. Motivated by this observation, we propose VISO, a Vision-language Instructed Satellite Object detection model that focuses on object-specific features while suppressing irrelevant regions through language-guided mask learning. After pre-training on a large-scale satellite dataset with 3.4M region-text pairs, VISO enhances object-text alignment and object-centric features to improve detection accuracy. Also, VISO suppresses irrelevant regions, enabling highly sparse inference to accelerate speed on satellites. Extensive experiments show that VISO without sparsity outperforms state-of-the-art (SOTA) VLMs in zero-shot detection by increasing 34.1% AP and reducing 27xFLOPs, and surpasses specialist models in supervised object detection and object referring by improving 2.3% AP. When sparsifying VISO to a comparable AP, FLOPs can be greatly reduced by up to 8.5x. Real-world tests reveal that VISO achieves a 2.8-4.8xFPS speed-up on satellites' embedded GPUs.
Reverse Convolution and Its Applications to Image Restoration
Authors: Xuhong Huang; Shiqi Liu; Kai Zhang; Ying Tai; Jian Yang; Hui Zeng; Lei Zhang
Venue: ICCV 2025 (main)

Abstract: Convolution and transposed convolution are fundamental operators widely used in neural networks. However, transposed convolution (a.k.a. deconvolution) does not serve as a true inverse of convolution due to inherent differences in their mathematical formulations. To date, no reverse convolution operator has been established as a standard component in neural architectures. In this paper, we propose a novel depthwise reverse convolution operator as an initial attempt to effectively reverse depthwise convolution by formulating and solving a regularized least-squares optimization problem. We thoroughly investigate its kernel initialization, padding strategies, and other critical aspects to ensure its effective implementation. Building upon this operator, we further construct a reverse convolution block by combining it with layer normalization, 1x1 convolution, and GELU activation, forming a Transformer-like structure. The proposed operator and block can directly replace conventional convolution and transposed convolution layers in existing architectures, leading to the development of ConverseNet. Corresponding to typical image restoration models such as DnCNN, SRResNet and USRNet, we train three variants of ConverseNet for Gaussian denoising, super-resolution and deblurring, respectively. Extensive experiments demonstrate the effectiveness of the proposed reverse convolution operator as a basic building module. We hope this work could pave the way for developing new operators in deep model design and applications.
ATCTrack: Aligning Target-Context Cues with Dynamic Target States for Robust Vision-Language Tracking
Authors: Xiaokun Feng; Shiyu Hu; Xuchen Li; Dailing Zhang; Meiqi Wu; Jing Zhang; Xiaotang Chen; Kaiqi Huang
Venue: ICCV 2025 (main)

Abstract: Vision-language tracking aims to locate the target object in the video sequence using a template patch and a language description provided in the initial frame. To achieve robust tracking, especially in complex long-term scenarios that reflect real-world conditions as recently highlighted by MGIT, it is essential not only to characterize the target features but also to utilize the context features related to the target. However, the visual and textual target-context cues derived from the initial prompts generally align only with the initial target state. Due to their dynamic nature, target states are constantly changing, particularly in complex long-term sequences. It is intractable for these cues to continuously guide Vision-Language Trackers (VLTs). Furthermore, for the text prompts with diverse expressions, our experiments reveal that existing VLTs struggle to discern which words pertain to the target or the context, complicating the utilization of textual cues. In this work, we present a novel tracker named ATCTrack, which can obtain multimodal cues Aligned with the dynamic target states through comprehensive Target-Context feature modeling, thereby achieving robust tracking. Specifically, (1) for the visual modality, we propose an effective temporal visual target-context modeling approach that provides the tracker with timely visual cues. (2) For the textual modality, we achieve precise target words identification solely based on textual content, and design an innovative context words calibration method to adaptively utilize auxiliary context words. (3) We conduct extensive experiments on mainstream benchmarks and ATCTrack achieves a new SOTA performance. The code and models will be released at: https://github.com/XiaokunFeng/ATCTrack.
Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy
Authors: Yaxin Xiao; Qingqing Ye; Li Hu; Huadi Zheng; Haibo Hu; Zi Liang; Haoyang Li; Yijie Jiao
Venue: ICCV 2025 (main)

Abstract: Machine unlearning enables the removal of specific data from ML models to uphold the *right to be forgotten*. While approximate unlearning algorithms offer efficient alternatives to full retraining, this work reveals that they fail to adequately protect the privacy of unlearned data. In particular, these algorithms introduce implicit residuals which facilitate privacy attacks targeting at unlearned data. We observe that these residuals persist regardless of model architectures, parameters, and unlearning algorithms, exposing a new attack surface beyond conventional output-based leakage. Based on this insight, we propose the *Reminiscence Attack (ReA)*, which amplifies the correlation between residuals and membership privacy through targeted fine-tuning processes. ReA achieves up to 1.90x and 1.12x higher accuracy than prior attacks when inferring class-wise and sample-wise membership, respectively. To mitigate such residual-induced privacy risk, we develop a dual-phase approximate unlearning framework that first eliminates deep-layer unlearned data traces and then enforces convergence stability to prevent models from "pseudo-convergence", where their outputs are similar to retrained models but still preserve unlearned residuals. Our framework works for both classification and generation tasks. Experimental evaluations confirm that our approach maintains high unlearning efficacy, while reducing the adaptive privacy attack accuracy to nearly random guess, at the computational cost of 2-12% of full retraining.
SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning
Authors: Zhewei Dai; Shilei Zeng; Haotian Liu; Xurui Li; Feng Xue; Yu Zhou
Venue: ICCV 2025 (main)

Abstract: We introduce SeaS, a unified industrial generative model for automatically creating diverse anomalies, authentic normal products, and precise anomaly masks. While extensive research exists, most efforts either focus on specific tasks, i.e., anomalies or normal products only, or require separate models for each anomaly type. Consequently, prior methods either offer limited generative capability or depend on a vast array of anomaly-specific models. We demonstrate that U-Net's differentiated learning ability captures the distinct visual traits of slightly-varied normal products and diverse anomalies, enabling us to construct a unified model for all tasks. Specifically, we first introduce an Unbalanced Abnormal (UA) Text Prompt, comprising one normal token and multiple anomaly tokens. More importantly, our Decoupled Anomaly Alignment (DA) loss decouples anomaly attributes and binds them to distinct anomaly tokens of UA, enabling SeaS to create unseen anomalies by recombining these attributes. Furthermore, our Normal-image Alignment (NA) loss aligns the normal token to normal patterns, making generated normal products globally consistent and locally varied. Finally, SeaS produces accurate anomaly masks by fusing discriminative U-Net features with high-resolution VAE features. SeaS sets a new benchmark for industrial generation, significantly enhancing downstream applications, with average improvements of +8.66% pixel-level AP for synthesis-based AD approaches, +1.10% image-level AP for unsupervised AD methods, and +12.79% IoU for supervised segmentation models. The code is available at https://github.com/HUST-SLOW/SeaS.
CreatiLayout: Siamese Multimodal Diffusion Transformer for Creative Layout-to-Image Generation
Authors: Hui Zhang; Dexiang Hong; Yitong Wang; Jie Shao; Xinglong Wu; Zuxuan Wu; Yu-Gang Jiang
Venue: ICCV 2025 (main)

Abstract: Diffusion models have been recognized for their ability to generate images that are not only visually appealing but also of high artistic quality. As a result, Layout-to-Image (L2I) generation has been proposed to leverage region-specific positions and descriptions to enable more precise and controllable generation. However, previous methods primarily focus on UNet-based models (e.g., SD1.5 and SDXL), and limited effort has explored Multimodal Diffusion Transformers (MM-DiTs), which have demonstrated powerful image generation capabilities. Enabling MM-DiT for layout-to-image generation seems straightforward but is challenging due to the complexity of how layout is introduced, integrated, and balanced among multiple modalities. To this end, we explore various network variants to efficiently incorporate layout guidance into MM-DiT, and ultimately present SiamLayout. To inherit the advantages of MM-DiT, we use a separate set of network weights to process the layout, treating it as equally important as the image and text modalities. Meanwhile, to alleviate the competition among modalities, we decouple the image-layout interaction into a siamese branch alongside the image-text one and fuse them in the later stage. Moreover, we contribute a large-scale layout dataset, named LayoutSAM, which includes 2.7 million image-text pairs and 10.7 million entities. Each entity is annotated with a bounding box and a detailed description. We further construct the LayoutSAM-Eval benchmark as a comprehensive tool for evaluating the L2I generation quality. Finally, we introduce the Layout Designer, which taps into the potential of large language models in layout planning, transforming them into experts in layout generation and optimization. These components form CreatiLayout---a systematic solution that integrates the layout model, dataset, and planner for creative layout-to-image generation.
AMD: Adaptive Momentum and Decoupled Contrastive Learning Framework for Robust Long-Tail Trajectory Prediction
Authors: Bin Rao; Haicheng Liao; Yanchen Guan; Chengyue Wang; Bonan Wang; Jiaxun Zhang; Zhenning Li
Venue: ICCV 2025 (main)

Abstract: Accurately predicting the future trajectories of traffic agents is essential in autonomous driving. However, due to the inherent imbalance in trajectory distributions, tail data in natural datasets often represents more complex and hazardous scenarios. Existing studies typically rely solely on a base model's prediction error, without considering the diversity and uncertainty of long-tail trajectory patterns. We propose an adaptive momentum and decoupled contrastive learning framework (AMD), which integrates unsupervised and supervised contrastive learning strategies. By leveraging an improved momentum contrast learning (MoCo-DT) and decoupled contrastive learning (DCL) module, our framework enhances the model's ability to recognize rare and complex trajectories. Additionally, we design four types of trajectory random augmentation methods and introduce an online iterative clustering strategy, allowing the model to dynamically update pseudo-labels and better adapt to the distributional shifts in long-tail data. We propose three different criteria to define long-tail trajectories and conduct extensive comparative experiments on the nuScenes and ETH/UCY datasets. The results show that AMD not only achieves optimal performance in long-tail trajectory prediction but also demonstrates outstanding overall prediction accuracy.
Music Grounding by Short Video
Authors: Zijie Xin; Minquan Wang; Jingyu Liu; Quan Chen; Ye Ma; Peng Jiang; Xirong Li
Venue: ICCV 2025 (main)

Abstract: Adding proper background music helps complete a short video to be shared. Previous work tackles the task by video-to-music retrieval (V2MR), aiming to find the most suitable music track from a collection to match the content of a given query video. In practice, however, music tracks are typically much longer than the query video, necessitating (manual) trimming of the retrieved music to a shorter segment that matches the video duration. In order to bridge the gap between the practical need for music moment localization and V2MR, we propose a new task termed Music Grounding by Short Video (MGSV). To tackle the new task, we introduce a new benchmark, MGSV-EC, which comprises a diverse set of 53k short videos associated with 35k different music moments from 4k unique music tracks. Furthermore, we develop a new baseline method, MaDe, which performs both video-to-music matching and music moment detection within a unified end-to-end deep network. Extensive experiments on MGSV-EC not only highlight the challenging nature of MGSV but also set MaDe as a strong baseline.
DyWA: Dynamics-adaptive World Action Model for Generalizable Non-prehensile Manipulation
Authors: Jiangran Lyu; Ziming Li; Xuesong Shi; Chaoyi Xu; Yizhou Wang; He Wang
Venue: ICCV 2025 (main)

Abstract: Non-prehensile manipulation is crucial for handling objects that are too thin, large, or otherwise ungraspable in unstructured environments. While conventional planning-based approaches struggle with complex contact modeling, learning-based methods have recently emerged as a promising alternative. However, existing learning-based approaches face two major limitations: they heavily rely on multi-view cameras and precise pose tracking, and they fail to generalize across varying physical conditions, such as changes in object mass and table friction. To address these challenges, we propose the Dynamics-Adaptive World Action Model (DyWA), a novel framework that enhances action learning by jointly predicting future states while adapting to dynamics variations based on historical trajectories. By unifying the modeling of geometry, state, physics, and robot actions, DyWA enables more robust policy learning under partial observability. Compared to baselines, our method improves the success rate by 31.5% using only single-view point cloud observations in the simulation. Furthermore, DyWA achieves an average success rate of 68% in real-world experiments, demonstrating its ability to generalize across diverse object geometries, adapt to varying table friction, and robustness in challenging scenarios such as half-filled water bottles and slippery surfaces.
POMATO: Marrying Pointmap Matching with Temporal Motions for Dynamic 3D Reconstruction
Authors: Songyan Zhang; Yongtao Ge; Jinyuan Tian; Guangkai Xu; Hao Chen; Chen Lv; Chunhua Shen
Venue: ICCV 2025 (main)

Abstract: Recent approaches to 3D reconstruction in dynamic scenes primarily rely on the integration of separate geometry estimation and matching modules, where the latter plays a critical role in distinguishing dynamic regions and mitigating the interference caused by moving objects. Furthermore, the matching module explicitly models object motion, enabling the tracking of specific targets and advancing motion understanding in complex scenarios. Recently, the proposed representation of pointmap in DUSt3R suggests a potential solution to unify both geometry estimation and matching in 3D space, effectively reducing computational overhead by eliminating the need for redundant auxiliary modules. However, it still struggles with ambiguous correspondences in dynamic regions, which limits reconstruction performance in such scenarios. In this work, we present POMATO, a unified framework for dynamic 3D reconstruction by marrying POintmap MAtching with Temporal mOtion. Specifically, our method first learns an explicit matching relationship by mapping RGB pixels across different views to 3D pointmaps within a unified coordinate system. Furthermore, we introduce a temporal motion module for dynamic motions that ensures scale consistency across different frames and enhances performance in 3D reconstruction tasks requiring both precise geometry and reliable matching, most notably 3D point tracking. We show the effectiveness of our proposed POMATO by demonstrating the remarkable performance across multiple downstream tasks, including video depth estimation, 3D point tracking, and pose estimation. Code and models are publicly available at https://github.com/wyddmw/POMATO.
Flow Stochastic Segmentation Networks
Authors: Fabio De Sousa Ribeiro; Omar Todd; Charles Jones; Avinash Kori; Raghav Mehta; Ben Glocker
Venue: ICCV 2025 (main)

Abstract: We propose the Flow Stochastic Segmentation Network (Flow-SSN), a generative model for probabilistic segmentation featuring discrete-time autoregressive and modern continuous-time flow parameterisations. We prove fundamental limitations of the low-rank parameterisation of previous methods and show that Flow-SSNs can estimate arbitrarily high-rank pixel-wise covariances without assuming the rank or storing the distributional parameters. Flow-SSNs are also more efficient to sample from than standard diffusion-based segmentation models, as most of the model capacity is allocated to learning the base distribution of the flow, which constitutes an expressive prior. We apply Flow-SSNs to challenging medical imaging benchmarks and achieve state-of-the-art results.