GenAI4ELab/papercli-papers huggingface dataset:
STDDNet: Harnessing Mamba for Video Polyp Segmentation via Spatial-aligned Temporal Modeling and Discriminative Dynamic Representation Learning
Authors: Guilian Chen; Huisi Wu; Jing Qin
Venue: ICCV 2025 (main)

Abstract: Automated segmentation of polyps from colonoscopy videos is of great clinical significance as it can assist clinicians in making accurate diagnoses and precise interventions. However, video polyp segmentation (VPS) is challenging due to ambiguous polyp boundaries, as well as variations in polyp scale, contrast, and position across consecutive frames. Moreover, to meet clinical requirements, the inference must operate in real-time to enable intraoperative tracking and guidance. In this paper, we propose a novel and efficient segmentation network, STDDNet, which integrates a spatial-aligned temporal modeling strategy and a discriminative dynamic representation learning mechanism, to comprehensively address these challenges by harnessing the advantages of Mamba. Specifically, a spatial-aligned temporal dependency propagation (STDP) module is developed to model temporal consistency from the consecutive frames based on a bidirectional scanning Mamba block. Furthermore, we design a discriminative dynamic feature extraction (DDFE) module to explore frame-wise dynamic information from the structural feature generated by the Mamba block. Such dynamic features can effectively deal with the variations across colonoscopy frames, providing more details for refined segmentation. We extensively evaluate STDDNet on two benchmark datasets, SUN-SEG and CVC-ClinicDB, demonstrating superior segmentation performance compared to state-of-the-art methods while maintaining real-time inference. Codes are available at https://github.com/C-GLGLGL/STDDNet.
DIMO: Diverse 3D Motion Generation for Arbitrary Objects
Authors: Linzhan Mou; Jiahui Lei; Chen Wang; Lingjie Liu; Kostas Daniilidis
Venue: ICCV 2025 (main)

Abstract: We present DIMO, a generative approach capable of generating diverse 3D motions for arbitrary objects from a single image. The core idea of our work is to leverage the rich priors in well-trained video models to extract the common motion patterns and then embed them into a shared low-dimensional latent space. Specifically, we first generate multiple videos of the same object with diverse motions. We then embed each motion into a latent vector and train a shared motion decoder to learn the distribution of motions represented by a structured and compact motion representation, i.e., neural key point trajectories. The canonical 3D Gaussians are then driven by these key points and fused to model the geometry and appearance. During inference time with learned latent space, we can instantly sample diverse 3D motions in a single-forward pass and support several interesting applications including 3D motion interpolation and language-guided motion generation.
Enhancing Spatial Reasoning in Multimodal Large Language Models through Reasoning-based Segmentation
Authors: Zhenhua Ning; Zhuotao Tian; Shaoshuai Shi; Guangming Lu; Daojing He; Wenjie Pei; Li Jiang
Venue: ICCV 2025 (main)

Abstract: Recent advances in point cloud perception have demonstrated remarkable progress in scene understanding through vision-language alignment leveraging large language models (LLMs). However, existing methods may still encounter challenges in handling complex instructions that require accurate spatial reasoning, even if the 3D point cloud data provides detailed spatial cues such as size and position for identifying the targets. To tackle this issue, we propose Relevant Reasoning Segmentation (R^2S), a reasoning-based segmentation framework. The framework emulates human cognitive processes by decomposing spatial reasoning into two sequential stages: first identifying relevant elements, then processing instructions guided by their associated visual priors. Furthermore, acknowledging the inadequacy of existing datasets in complex reasoning tasks, we introduce 3D ReasonSeg, a reasoning-based segmentation dataset comprising 25,185 training samples and 3,966 validation samples with precise annotations. Both quantitative and qualitative experiments demonstrate that the R^2S and 3D ReasonSeg effectively endow 3D point cloud perception with stronger spatial reasoning capabilities, and we hope that they can serve as a new baseline and benchmark for future work.
MMAD: Multi-label Micro-Action Detection in Videos
Authors: Kun Li; Pengyu Liu; Dan Guo; Fei Wang; Zhiliang Wu; Hehe Fan; Meng Wang
Venue: ICCV 2025 (main)

Abstract: Human body actions are an important form of non-verbal communication in social interactions. This paper specifically focuses on a subset of body actions known as micro-actions, which are subtle, low-intensity body movements with promising applications in human emotion analysis. In real-world scenarios, human micro-actions often temporally co-occur, with multiple micro-actions overlapping in time, such as concurrent head and hand movements. However, current research primarily focuses on recognizing individual micro-actions while overlooking their co-occurring nature. To address this gap, we propose a new task named Multi-label Micro-Action Detection (MMAD), which involves identifying all micro-actions in a given short video, determining their start and end times, and categorizing them. Accomplishing this requires a model capable of accurately capturing both long-term and short-term action relationships to detect multiple overlapping micro-actions. To facilitate the MMAD task, we introduce a new dataset named Multi-label Micro-Action-52 (MMA-52) and propose a baseline method equipped with a dual-path spatial-temporal adapter to address the challenges of subtle visual change in MMAD. We hope that MMA-52 can stimulate research on micro-action analysis in videos and prompt the development of spatio-temporal modeling in human-centric video understanding. The proposed MMA-52 dataset is available at: https://github.com/VUT-HFUT/Micro-Action
Human-in-the-Loop Local Corrections of 3D Scene Layouts via Infilling
Authors: Christopher Xie; Armen Avetisyan; Henry Howard-Jenkins; Yawar Siddiqui; Julian Straub; Richard Newcombe; Vasileios Balntas; Jakob Engel
Venue: ICCV 2025 (main)

Abstract: We present a novel human-in-the-loop approach to estimate 3D scene layout that uses human feedback from an egocentric standpoint. We study this approach through introduction of a novel local correction task, where users identify local errors and prompt a model to automatically correct them. Building on SceneScript, a state-of-the-art framework for 3D scene layout estimation that leverages structured language, we propose a solution that structures this problem as "infilling", a task studied in natural language processing. We train a multi-task version of SceneScript that maintains performance on global predictions while significantly improving its local correction ability. We integrate this into a human-in-the-loop system, enabling a user to iteratively refine scene layout estimates via a low-friction "one-click fix" workflow. Our system enables the final refined layout to diverge from the training distribution, allowing for more accurate modelling of complex layouts.
2D Gaussian Splatting-based Sparse-view Transparent Object Depth Reconstruction via Physics Simulation for Scene Update
Authors: Jeongyun Kim; Seunghoon Jeong; Giseop Kim; Myung-Hwan Jeon; Eunji Jun; Ayoung Kim
Venue: ICCV 2025 (main)

Abstract: Understanding the 3D geometry of transparent objects from RGB images is challenging due to their inherent physical properties, such as reflection and refraction. To address these difficulties, especially in scenarios with sparse views and dynamic environments, we introduce TRAN-D, a novel 2D Gaussian Splatting-based depth reconstruction method for transparent objects. Our key insight lies in separating transparent objects from the background, enabling focused optimization of Gaussians corresponding to the object. We mitigate artifacts with an object-aware loss that places Gaussians in obscured regions, ensuring coverage of invisible surfaces while reducing overfitting. Furthermore, we incorporate a physics-based simulation that refines the reconstruction in just a few seconds, effectively handling object removal and chain-reaction movement of remaining objects without the need for rescanning. TRAN-D is evaluated on both synthetic and real-world sequences, and it consistently demonstrated robust improvements over existing GS-based state-of-the-art methods. In comparison with baselines, TRAN-D reduces the mean absolute error by over 39% for the synthetic TRansPose sequences. Furthermore, despite being updated using only one image, TRAN-D reaches a d < 2.5 cm accuracy of 48.46%, over 1.5 times that of baselines, which uses six images. Code and more results are available at https://jeongyun0609.github.io/TRAN-D/.
HouseCrafter: Lifting Floorplans to 3D Scenes with 2D Diffusion Models
Authors: Yiwen Chen; Hieu T. Nguyen; Vikram Voleti; Varun Jampani; Huaizu Jiang
Venue: ICCV 2025 (main)

Abstract: We introduce HouseCrafter, a novel approach that can lift a 2D floorplan into a complete large 3D indoor scene (e.g., a house). Our key insight is to adapt a 2D diffusion model, which is trained on web-scale images, to generate consistent multi-view color (RGB) and depth (D) images across different locations of the scene. Specifically, the RGB-D images are generated autoregressively in batches along sampled locations derived from the floorplan. At each step, the diffusion model conditions on previously generated images to produce new images at nearby locations. The global floorplan and attention design in the diffusion model ensures the consistency of the generated images, from which a 3D scene can be reconstructed. Through extensive evaluation on the 3D-FRONT dataset, we demonstrate that HouseCrafter can generate high-quality house-scale 3D scenes. Ablation studies also validate the effectiveness of different design choices. We will release our code and model weights.
Fast Globally Optimal and Geometrically Consistent 3D Shape Matching
Authors: Paul Roetzer; Florian Bernard
Venue: ICCV 2025 (main)

Abstract: Geometric consistency, i.e. the preservation of neighbourhoods, is a natural and strong prior in 3D shape matching. Geometrically consistent matchings are crucial for many downstream applications, such as texture transfer or statistical shape modelling. Yet, in practice, geometric consistency is often overlooked, or only achieved under severely limiting assumptions (e.g. a good initialisation). In this work, we propose a novel formalism for computing globally optimal and geometrically consistent matchings between 3D shapes which is scalable in practice. Our key idea is to represent the surface of the source shape as a collection of cyclic graphs, which are then consistently matched to the target shape. Mathematically, we construct a hyper product graph (between source and target shape), and then cast 3D shape matching as a minimum-cost circulation flow problem in this hyper graph, which yields global geometrically consistent matchings between both shapes. We empirically show that our formalism is efficiently solvable and that it leads to high-quality results. Our code is publicly available.
Guiding Diffusion Models with Adaptive Negative Sampling Without External Resources
Authors: Alakh Desai; Nuno Vasconcelos
Venue: ICCV 2025 (main)

Abstract: Diffusion models (DMs) have demonstrated an unparalleled ability to create diverse and high-fidelity images from text prompts. However, they are also well-known to vary substantially regarding both prompt adherence and quality. Negative prompting was introduced to improve prompt compliance by specifying what an image must not contain. Previous works have shown the existence of an ideal negative prompt that can maximize the odds of the positive prompt. In this work, we explore relations between negative prompting and classifier-free guidance (CFG) to develop a sampling procedure, Adaptive Negative Sampling Without External Resources (ANSWER), that accounts for both positive and negative conditions from a single prompt. This leverages the internal understanding of negation by the diffusion model to increase the odds of generating images faithful to the prompt. ANSWER is a training-free technique, applicable to any model that supports CFG, and allows for negative grounding of image concepts without an explicit negative prompts, which are lossy and incomplete. Experiments show that adding ANSWER to existing DMs outperforms the baselines on multiple benchmarks and is preferred by humans 2x more over the other methods
AlignGuard: Scalable Safety Alignment for Text-to-Image Generation
Authors: Runtao Liu; I Chieh Chen; Jindong Gu; Jipeng Zhang; Renjie Pi; Qifeng Chen; Philip Torr; Ashkan Khakzar; Fabio Pizzati
Venue: ICCV 2025 (main)

Abstract: Text-to-image (T2I) models have become widespread, but their limited safety guardrails expose end users to harmful content and potentially allow for model misuse. Current safety measures are typically limited to text-based filtering or concept removal strategies, able to remove just a few concepts from the model's generative capabilities. In this work, we introduce AlignGuard, a method for safety alignment of T2I models. We enable the application of Direct Preference Optimization (DPO) for safety purposes in T2I models by synthetically generating a dataset of harmful and safe image-text pairs, which we call CoProV2. Using a custom DPO strategy and this dataset, we train safety experts, in the form of low-rank adaptation (LoRA) matrices, able to guide the generation process away from specific safety-related concepts. Then, we merge the experts into a single LoRA using a novel merging strategy for optimal scaling performance. This expert-based approach enables scalability, allowing us to remove 7 times more harmful concepts from T2I models compared to baselines. AlignGuard consistently outperforms the state-of-the-art on many benchmarks and establishes new practices for safety alignment in T2I networks.
Function-centric Bayesian Network for Zero-Shot Object Goal Navigation
Authors: Sixian Zhang; Xinyao Yu; Xinhang Song; Yiyao Wang; Shuqiang Jiang
Venue: ICCV 2025 (main)

Abstract: Object goal navigation requires an agent to navigate to a specified target in unseen environments without an explicit map, which demands an understanding of object-scene context to infer the target's location based on partial observations. The function of an object plays a crucial role in its categorization and naming. Analyzing an object's functional role within a given scene enhances the understanding of its contextual relationships, thereby aiding in goal inference. In this paper, we propose the Function-centric Bayesian Network (FBN) for the zero-shot ObjectNav task. FBN is designed to uncover the functions that observed objects afford individually or collaboratively with other objects, as well as the functional semantics contained within the observed scenes. The probabilistic directed edges in FBN describe the object-function and scene-function relationships, which are derived by prompting LLMs with the proposed CounterfactCoT. Leveraging FBN with Bayesian inference, the probability of each function group and probability map of goal occurrence are computed. Then the waypoint is selected based on obtained probability map. Experiments on MP3D and HM3D demonstrate that FBN effectively captures object-scene-function relationships and improves zero-shot ObjectNav performance.
Preserve Anything: Controllable Image Synthesis with Object Preservation
Authors: Prasen Kumar Sharma; Neeraj Matiyali; Siddharth Srivastava; Gaurav Sharma
Venue: ICCV 2025 (main)

Abstract: We introduce Preserve Anything, a novel method for con-trolled image synthesis that addresses key limitations in ob-ject preservation and semantic consistency in text-to-image(T2I) generation. Existing approaches often fail (i) to pre-serve multiple objects with fidelity, (ii) maintain semanticalignment with prompts, or (iii) provide explicit control overscene composition. To overcome these challenges, the pro-posed method employs an N-channel ControlNet that inte-grates (i) object preservation with size and placement ag-nosticism, color and detail retention, and artifact elimi-nation, (ii) high-resolution, semantically consistent back-grounds with accurate shadows, lighting, and prompt ad-herence, and (iii) explicit user control over background lay-outs and lighting conditions. Key components of our frame-work include object preservation and background guid-ance modules, enforcing lighting consistency and a high-frequency overlay module to retain fine details while mit-igating unwanted artifacts. We introduce a benchmarkdataset consisting of 240K natural images filtered for aes-thetic quality and 18K 3D-rendered synthetic images withmetadata such as lighting, camera angles, and object rela-tionships. This dataset addresses the deficiencies of existingbenchmarks and allows a complete evaluation. Empiricalresults demonstrate that our method achieves state-of-the-art performance, significantly improving feature-space fi-delity (FID 15.26) and semantic alignment (CLIP-S 32.85)while maintaining competitive aesthetic quality. We alsoconducted a user study to demonstrate the efficacy of theproposed work on unseen benchmark and observed a re-markable improvement of  25%,  19%,  13%, and  14% in terms of prompt alignment, photorealism, thepresence of AI artifacts, and natural aesthetics over existingworks.
Leveraging Local Patch Alignment to Seam-cutting for Large Parallax Image Stitching
Authors: Tianli Liao; Chenyang Zhao; Lei Li; Heling Cao
Venue: ICCV 2025 (main)

Abstract: Seam cutting has shown significant effectiveness in the composition phase of image stitching, particularly for scenarios involving parallax. However, conventional implementations typically position seam-cutting as a downstream process contingent upon successful image alignment. This approach inherently assumes the existence of locally aligned regions where visually plausible seams can be established. Current alignment methods frequently fail to satisfy this prerequisite in large parallax scenarios despite considerable research efforts dedicated to improving alignment accuracy. In this paper, we propose an alignment-compensation paradigm that dissociates seam quality from initial alignment accuracy by integrating a Local Patch Alignment Module (LPAM) into the seam-cutting pipeline. Concretely, given the aligned images with an estimated initial seam, our method first identifies low-quality pixels along the seam through a seam quality assessment, then performs localized SIFT-flow alignment on the critical patches enclosing these pixels. Finally, we recomposite the aligned patches using adaptive seam-cutting and merge them into the original aligned images to generate the final mosaic. Comprehensive experiments on large parallax stitching datasets demonstrate that LPAM significantly enhances stitching quality while maintaining computational efficiency. The code is available at https://github.com/tlliao/LPAM_seam-cutting.
Revisiting Efficient Semantic Segmentation: Learning Offsets for Better Spatial and Class Feature Alignment
Authors: Shi-Chen Zhang; Yunheng Li; Yu-Huan Wu; Qibin Hou; Ming-Ming Cheng
Venue: ICCV 2025 (main)

Abstract: Semantic segmentation is fundamental to vision systems requiring pixel-level scene understanding, yet deploying it on resource-constrained devices demands efficient architectures. Although existing methods achieve real-time inference through lightweight designs, we reveal their inherent limitation: misalignment between class representations and image features caused by a per-pixel classification paradigm. With experimental analysis, we find that this paradigm results in a highly challenging assumption for efficient scenarios: Image pixel features should not vary for the same category in different images. To address this dilemma, we propose a coupled dual-branch offset learning paradigm that explicitly learns feature and class offsets to dynamically refine both class representations and spatial image features. Based on the proposed paradigm, we construct an efficient semantic segmentation network, OffSeg. Notably, the offset learning paradigm can be adopted to existing methods with no additional architectural changes. Extensive experiments on four datasets, including ADE20K, Cityscapes, COCO-Stuff-164K, and Pascal Context, demonstrate consistent improvements with negligible parameters.
GIViC: Generative Implicit Video Compression
Authors: Ge Gao; Siyue Teng; Tianhao Peng; Fan Zhang; David Bull
Venue: ICCV 2025 (main)

Abstract: While video compression based on implicit neural representations (INRs) has recently demonstrated great potential, existing INR-based video codecs still cannot achieve state-of-the-art (SOTA) performance compared to their conventional or autoencoder-based counterparts given the same coding configuration. In this context, we propose a **G**enerative **I**mplicit **Vi**deo **C**ompression framework, **GIViC**, aiming at advancing the performance limits of this type of coding methods. GIViC is inspired by the characteristics that INRs share with large language and diffusion models in exploiting *long-term dependencies*. Through the newly designed *implicit diffusion* process, GIViC performs diffusive sampling across coarse-to-fine spatiotemporal decompositions, gradually progressing from coarser-grained full-sequence diffusion to finer-grained per-token diffusion. A novel **Hierarchical Gated Linear Attention-based transformer** (HGLA), is also integrated into the framework, which dual-factorizes global dependency modeling along scale and sequential axes. The proposed GIViC model has been benchmarked against SOTA conventional and neural codecs using a Random Access (RA) configuration (YUV 4:2:0, GOPSize=32), and yields BD-rate savings of 15.94%, 22.46% and 8.52% over VVC VTM, DCVC-FM and NVRC, respectively. As far as we are aware, GIViC is the **first INR-based video codec that outperforms VTM based on the RA coding configuration**. The source code will be made available.
TAD-E2E: A Large-scale End-to-end Autonomous Driving Dataset
Authors: Chang Liu; Mingxu Zhu; Zheyuan Zhang; Linna Song; Xiao Zhao; Qingliang Luo; Qi Wang; Chufan Guo; Kuifeng Su
Venue: ICCV 2025 (main)

Abstract: End-to-end autonomous driving technology has recently become a focal point of research and application in autonomous driving. State-of-the-art (SOTA) methods are often trained and evaluated on the NuScenes dataset. However, the NuScenes dataset, introduced in 2019 for 3D perception tasks, faces several limitations--such as insufficient scale, simple scenes, and homogeneous driving behaviors--that restrict the upper-bound development of end-to-end autonomous driving algorithms. In light of these issues, we propose a novel, large-scale real-world dataset specifically designed for end-to-end autonomous driving tasks, named TAD-E2E, which is 25x larger, 1.7x scene complexity over NuScenes, and features a highly diverse range of driving behaviors. We replicated SOTA methods on the TAD-E2E dataset and observed that these methods no longer performed well, as expected. Additionally, in response to the challenging scenarios presented in the TAD-E2E dataset, we devised a multimodal sparse end-to-end method that significantly outperforms SOTA methods. Ablation studies demonstrate the effectiveness of our method, and we analyze the contributions of each module. The dataset will be released in the near future.