GenAI4ELab/papercli-papers huggingface dataset:
Pruning All-Rounder: Rethinking and Improving Inference Efficiency for Large Vision Language Models
Authors: Wei Suo; Ji Ma; Mengyang Sun; Lin Yuanbo Wu; Peng Wang; Yanning Zhang
Venue: ICCV 2025 (main)

Abstract: Although Large Vision-Language Models (LVLMs) have achieved impressive results, their high computational costs pose a significant barrier to wide application. To enhance inference efficiency, most existing approaches can be categorized as parameter-dependent or token-dependent strategies to reduce computational demands. However, parameter-dependent methods require retraining LVLMs to recover performance while token-dependent strategies struggle to consistently select the most relevant tokens. In this paper, we systematically analyze the above challenges and provide a series of valuable insights for inference acceleration. Based on these findings, we propose a novel framework, the Pruning All-Rounder (PAR). Different from previous works, PAR develops a meta-router to adaptively organize pruning flows across both tokens and layers. With a self-supervised learning manner, our method achieves a superior balance between performance and efficiency. Notably, PAR is highly flexible, offering multiple pruning versions to address a range of acceleration scenarios. The code for this work is publicly available at https://github.com/ASGO-MM/Pruning-All-Rounder.
Discontinuity-aware Normal Integration for Generic Central Camera Models
Authors: Francesco Milano; Manuel López-Antequera; Naina Dhingra; Roland Siegwart; Robert Thiel
Venue: ICCV 2025 (main)

Abstract: Recovering a 3D surface from its surface normal map, a problem known as normal integration, is a key component for photometric shape reconstruction techniques such as shape-from-shading and photometric stereo. The vast majority of existing approaches for normal integration handle only implicitly the presence of depth discontinuities and are limited to orthographic or ideal pinhole cameras. In this paper, we propose a novel formulation that allows modeling discontinuities explicitly and handling generic central cameras. Our key idea is based on a local planarity assumption, that we model through constraints between surface normals and ray directions. Compared to existing methods, our approach more accurately approximates the relation between depth and surface normals, achieves state-of-the-art results on the standard normal integration benchmark, and is the first to directly handle generic central camera models.
LazyMAR: Accelerating Masked Autoregressive Models via Feature Caching
Authors: Feihong Yan; Qingyan Wei; Jiayi Tang; Jiajun Li; Yulin Wang; Xuming Hu; Huiqi Li; Linfeng Zhang
Venue: ICCV 2025 (main)

Abstract: Masked Autoregressive (MAR) models have emerged as a promising approach in image generation, expected to surpass traditional autoregressive models in computational efficiency by leveraging the capability of parallel decoding. However, their dependence on bidirectional self-attention inherently conflicts with conventional KV caching mechanisms, creating unexpected computational bottlenecks that undermine their expected efficiency. To address this problem, this paper studies the caching mechanism for MAR by leveraging two types of redundancy: Token Redundancy indicates that a large portion of tokens have very similar representations in the adjacent decoding steps, which allows us to first cache them in previous steps and then reuse them in the later steps. Condition Redundancy indicates that the difference between conditional and unconditional output in classifier-free guidance exhibits very similar values in adjacent steps. Based on these two redundancies, we propose LazyMAR, which introduces two caching mechanisms to handle them one by one. LazyMAR is training-free and plug-and-play for all MAR models. Experimental results demonstrate that our method achieves 2.83 times acceleration with almost no drop in generation quality. Our codes will be released in https://github.com/feihongyan1/LazyMAR.
OmniDiff: A Comprehensive Benchmark for Fine-grained Image Difference Captioning
Authors: Yuan Liu; Saihui Hou; Saijie Hou; Jiabao Du; Shibei Meng; Yongzhen Huang
Venue: ICCV 2025 (main)

Abstract: Image Difference Captioning (IDC) aims to generate natural language descriptions of subtle differences between image pairs, requiring both precise visual change localization and coherent semantic expression. Despite recent advancements, existing datasets often lack breadth and depth, limiting their applicability in complex and dynamic environments: (1) from a breadth perspective, current datasets are constrained to limited variations of objects in specific scenes, and (2) from a depth perspective, prior benchmarks often provide overly simplistic descriptions. To address these challenges, we introduce OmniDiff, a comprehensive dataset comprising 324 diverse scenarios--spanning real-world complex environments and 3D synthetic settings--with fine-grained human annotations averaging 60 words in length and covering 12 distinct change types. Building on this foundation, we propose M3Diff, a Multi Modal large language model enhanced by a plug-and-play Multi-scale Differential Perception (MDP) module. This module improves the model's ability to accurately identify and describe inter-image differences while maintaining the foundational model's generalization capabilities. With the addition of the OmniDiff dataset, M3Diff achieves state-of-the-art performance across multiple benchmarks, including Spot-the-Diff, IEdit, CLEVR-Change, CLEVR-DC, and OmniDiff, demonstrating significant improvements in cross-scenario difference recognition accuracy compared to existing methods. The dataset, code, and models will be made publicly available to support further research.
MAESTRO: Task-Relevant Optimization via Adaptive Feature Enhancement and Suppression for Multi-task 3D Perception
Authors: Changwon Kang; Jisong Kim; Hongjae Shin; Junseo Park; Jun Won Choi
Venue: ICCV 2025 (main)

Abstract: The goal of multi-task learning is to learn to conduct multiple tasks simultaneously based on a shared data representation. While this approach can improve learning efficiency, it may also cause performance degradation due to task conflicts that arise when optimizing the model for different objectives. To address this challenge, we introduce MAESTRO, a structured framework designed to generate task-specific features and mitigate feature interference in multi-task 3D perception, including 3D object detection, bird's-eye view (BEV) map segmentation, and 3D occupancy prediction. MAESTRO comprises three components: the Class-wise Prototype Generator (CPG), the Task-Specific Feature Generator (TSFG), and the Scene Prototype Aggregator (SPA). CPG groups class categories into foreground and background groups and generates group-wise prototypes. The foreground and background prototypes are assigned to the 3D object detection task and the map segmentation task, respectively, while both are assigned to the 3D occupancy prediction task. TSFG leverages these prototype groups to retain task-relevant features while suppressing irrelevant features, thereby enhancing the performance for each task. SPA enhances the prototype groups assigned for 3D occupancy prediction by utilizing the information produced by the 3D object detection head and the map segmentation head. Extensive experiments on the nuScenes and Occ3D benchmarks demonstrate that MAESTRO consistently outperforms existing methods across 3D object detection, BEV map segmentation, and 3D occupancy prediction tasks.
MM-Spatial: Exploring 3D Spatial Understanding in Multimodal LLMs
Authors: Erik Daxberger; Nina Wenzel; David Griffiths; Haiming Gang; Justin Lazarow; Gefen Kohavi; Kai Kang; Marcin Eichner; Yinfei Yang; Afshin Dehghan; Peter Grasch
Venue: ICCV 2025 (main)

Abstract: Multimodal large language models (MLLMs) excel at 2D visual understanding but remain limited in their ability to reason about 3D space. In this work, we leverage large-scale high-quality 3D scene data with open-set annotations to introduce 1) a novel supervised fine-tuning dataset and 2) a new evaluation benchmark, focused on indoor scenes. Our Cubify Anything VQA (CA-VQA) data covers diverse spatial tasks including spatial relationship prediction, metric size and distance estimation, and 3D grounding. We show that CA-VQA enables us to train MM-Spatial, a strong generalist MLLM that also achieves state-of-the-art performance on 3D spatial understanding benchmarks, including our own. We show how incorporating metric depth and multi-view inputs (provided in CA-VQA) can further improve 3D understanding, and demonstrate that data alone allows our model to achieve depth perception capabilities comparable to dedicated monocular depth estimation models.
CasP: Improving Semi-Dense Feature Matching Pipeline Leveraging Cascaded Correspondence Priors for Guidance
Authors: Peiqi Chen; Lei Yu; Yi Wan; Yingying Pei; Xinyi Liu; Yongxiang Yao; Yingying Zhang; Lixiang Ru; Liheng Zhong; Jingdong Chen; Ming Yang; Yongjun Zhang
Venue: ICCV 2025 (main)

Abstract: Semi-dense feature matching methods have shown strong performance in challenging scenarios. However, the existing pipeline relies on a global search across the entire feature map to establish coarse matches, limiting further improvements in accuracy and efficiency. Motivated by this limitation, we propose a novel pipeline, CasP, which leverages cascaded correspondence priors for guidance. Specifically, the matching stage is decomposed into two progressive phases, bridged by a region-based selective cross-attention mechanism designed to enhance feature discriminability. In the second phase, one-to-one matches are determined by restricting the search range to the one-to-many prior areas identified in the first phase. Additionally, this pipeline benefits from incorporating high-level features, which helps reduce the computational costs of low-level feature extraction. The acceleration gains of CasP increase with higher resolution, and our lite model achieves a speedup of ~2.2xat a resolution of 1152 compared to the most efficient method, ELoFTR. Furthermore, extensive experiments demonstrate its superiority in geometric estimation, particularly with impressive cross-domain generalization. These advantages highlight its potential for latency-sensitive and high-robustness applications, such as SLAM and UAV systems.
Multimodal Latent Diffusion Model for Complex Sewing Pattern Generation
Authors: Shengqi Liu; Yuhao Cheng; Zhuo Chen; Xingyu Ren; Wenhan Zhu; Lincheng Li; Mengxiao Bi; Xiaokang Yang; Yichao Yan
Venue: ICCV 2025 (main)

Abstract: Generating sewing patterns in garment design is receiving increasing attention due to its CG-friendly and flexible-editing nature. Previous sewing pattern generation methods have been able to produce exquisite clothing, but struggle to design complex garments with detailed control. To address these issues, we propose **SewingLDM**, a multi-modal generative model that generates sewing patterns controlled by text prompts, body shapes, and garment sketches. Initially, we extend the original vector of sewing patterns into a more comprehensive representation to cover more intricate details and then compress them into a compact latent space. To learn the sewing pattern distribution in the latent space, we design a two-step training strategy to inject the multi-modal conditions, i.e., body shapes, text prompts, and garment sketches, into a diffusion model, ensuring the generated garments are body-suited and detail-controlled. Comprehensive qualitative and quantitative experiments show the effectiveness of our proposed method, significantly surpassing previous approaches in terms of complex garment design and various body adaptability.
CObL: Toward Zero-Shot Ordinal Layering without User Prompting
Authors: Aneel Damaraju; Dean Hazineh; Todd Zickler
Venue: ICCV 2025 (main)

Abstract: Vision benefits from grouping pixels into objects and understanding their spatial relationships, both laterally and in depth. We capture this with a scene representation comprising an occlusion-ordered stack of "object layers," each containing an isolated and amodally-completed object. To infer this representation from an image, we introduce a diffusion-based architecture named Concurrent Object Layers (CObL). CObL generates a stack of object layers in parallel, using Stable Diffusion as a prior for natural objects and inference-time guidance to ensure the inferred layers composite back to the input image. We train CObL using a few thousand synthetically-generated images of multi-object tabletop scenes, and we find that it zero-shot generalizes to photographs of real-world tabletops with varying numbers of novel objects. In contrast to recent models for amodal object completion, CObL reconstructs multiple occluded objects without user prompting and without knowing the number of objects beforehand. Unlike previous models for unsupervised object-centric representation learning, CObL is not limited to the world it was trained in.
Rethinking the Upsampling Process in Light Field Super-Resolution with Spatial-Epipolar Implicit Image Function
Authors: Ruixuan Cong; Yu Wang; Mingyuan Zhao; Da Yang; Rongshan Chen; Hao Sheng
Venue: ICCV 2025 (main)

Abstract: Deep learning-based light field image super-resolution methods have witnessed remarkable success in recent years. However, most of them only focus on the encoder design and overlook the importance of upsampling process in decoder part. Inspired by the recent progress in single image domain with implicit neural representation, we elaborately propose spatial-epipolar implicit image function (SEIIF), which optimizes upsampling process to significantly improve performance and supports arbitrary-scale light filed image super-resolution. Specifically, SEIIF contains two complementary upsampling patterns. One is spatial implicit image function (SIIF) that exploits intra-view information in sub-aperture images. The other is epipolar implicit image function (EIIF) that mines inter-view information in epipolar plane images. By unifying the upsampling step of two branches, SEIIF extra introduces cross-branch feature interaction to fully fuse intra-view information and inter-view information. Besides, given that line structure in epipolar plane image integrates spatial-angular correlation of light field, we present an oriented line sampling strategy to exactly aggregate inter-view information. The experimental results demonstrate that our SEIIF can be effectively combined with most encoders and achieve outstanding performance on both fixed-scale and arbitrary-scale light field image super-resolution. Our code is available at https://github.com/Congrx/SEIIF.
GGTalker: Talking Head Systhesis with Generalizable Gaussian Priors and Identity-Specific Adaptation
Authors: Wentao Hu; Shunkai Li; Ziqiao Peng; Haoxian Zhang; Fan Shi; Xiaoqiang Liu; Pengfei Wan; Di Zhang; Hui Tian
Venue: ICCV 2025 (main)

Abstract: Creating high-quality, generalizable speech-driven 3D talking heads remains a persistent challenge. Previous methods achieve satisfactory results for fixed viewpoints and small-scale audio variations, but they struggle with large head rotations and out-of-distribution (OOD) audio. Moreover, they are constrained by the need for time-consuming, identity-specific training. We believe the core issue lies in the lack of sufficient 3D priors, which limits the extrapolation capabilities of synthesized talking heads. To address this, we propose GGTalker, which synthesizes talking heads through a combination of generalizable priors and identity-specific adaptation. We introduce a two-stage Prior-Adaptation training strategy to learn Gaussian head priors and adapt to individual characteristics. We train Audio-Expression and Expression-Visual priors to capture the universal patterns of lip movements and the general distribution of head textures. During the Customized Adaptation, individual speaking styles and texture details are precisely modeled. Additionally, we introduce a color MLP to generate fine-grained, motion-aligned textures and a Body Inpainter to blend rendered results with the background, producing indistinguishable, photorealistic video frames. Comprehensive experiments show that GGTalker achieves state-of-the-art performance in rendering quality, 3D consistency, lip-sync accuracy, and training efficiency.
SAC-GNC: SAmple Consensus for adaptive Graduated Non-Convexity
Authors: Valter Piedade; Chitturi Sidhartha; José Gaspar; Venu Madhav Govindu; Pedro Miraldo
Venue: ICCV 2025 (main)

Abstract: Outliers are ubiquitous in geometric vision contexts such as pose estimation and mapping, leading to inaccurate estimates. While robust loss functions can tackle outliers, it is challenging to make the estimation robust to the choice of initialization and to estimate the appropriate robust loss shape parameter that allows distinguishing inliers from outliers. Graduated non-convexity (GNC) often mitigates these issues. However, typical GNC uses a fixed annealing factor to update the shape parameter, which can lead to low-quality or inefficient estimates. This paper proposes a novel approach to adaptively anneal the shape parameter within a GNC framework. We developed a search strategy that incorporates a sampling of annealing choices and model scorings to select the most promising shape parameter at each GNC iteration. Additionally, we propose new stopping criteria and an initialization technique that improves performance for diverse data, and we show the benefits of combining discrete and continuous robust estimation strategies. We evaluate our method using synthetic and real-world data in two problems: 3D registration and pose graph optimization in SLAM sequences. Our results demonstrate greater efficiency and robustness compared to previous GNC schemes. Code and other resources are available at https://www.merl.com/research/highlights/sac-gnc.
VGGSounder: Audio-Visual Evaluations for Foundation Models
Authors: Daniil Zverev; Thaddäus Wiedemer; Ameya Prabhu; Matthias Bethge; Wieland Brendel; A. Sophia Koepke
Venue: ICCV 2025 (main)

Abstract: Designing effective foundation models requires high-quality evaluation datasets. With the emergence of audio-visual foundation models, reliable assessment of their multi-modal understanding is essential. The current gold standard for evaluating audio-visual understanding is the popular classification dataset VGGSound. However, our analysis identifies several critical issues in VGGSound, including incomplete labelling, partially overlapping classes, and misaligned modalities. These flaws lead to distorted evaluations of models' true auditory and visual capabilities. To address these limitations, we introduce VGGSounder, a comprehensively re-annotated, multi-label test set extending VGGSound that is explicitly designed to accurately evaluate audio-visual foundation models. VGGSounder features detailed modality annotations, enabling precise analyses of modality-specific performance and revealing previously unnoticed model limitations. We believe VGGSounder offers a robust and reliable benchmark supporting the future development of audio-visual foundation models.
ResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum Computers
Authors: Nicholas S. DiBrita; Jason Han; Tirthak Patel
Venue: ICCV 2025 (main)

Abstract: Research in quantum machine learning has recently proliferated due to the potential of quantum computing to accelerate machine learning. An area of machine learning that has not yet been explored is neural ordinary differential equation (neural ODE) based residual neural networks (ResNets), which aim to improve the effectiveness of neural networks using the principles of ordinary differential equations. In this work, we present our insights about why analog Rydberg atom quantum computers are especially well-suited for ResNets. We also introduce ResQ, a novel framework to optimize the dynamics of Rydberg atom quantum computers to solve classification problems in machine learning using analog quantum neural ODEs.
Make Me Happier: Evoking Emotions Through Image Diffusion Models
Authors: Qing Lin; Jingfeng Zhang; Yew-Soon Ong; Mengmi Zhang
Venue: ICCV 2025 (main)

Abstract: Despite the rapid progress in image generation, emotional image editing remains under-explored. The semantics, context, and structure of an image can evoke emotional responses, making emotional image editing techniques valuable for various real-world applications, including treatment of psychological disorders, commercialization of products, and artistic design. First, we present a novel challenge of emotion-evoked image generation, aiming to synthesize images that evoke target emotions while retaining the semantics and structures of the original scenes. To address this challenge, we propose a diffusion model capable of effectively understanding and editing source images to convey desired emotions and sentiments. Moreover, due to the lack of emotion editing datasets, we provide a unique dataset consisting of 340,000 pairs of images and their emotion annotations. Furthermore, we conduct human psychophysics experiments and introduce a new evaluation metric to systematically benchmark all the methods. Experimental results demonstrate that our method surpasses all competitive baselines. Our diffusion model is capable of identifying emotional cues from original images, editing images that elicit desired emotions, and meanwhile, preserving the semantic structure of the original images. All code, model, and dataset are available at GitHub.
SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Models
Authors: Xianfu Cheng; Wei Zhang; Shiwei Zhang; Jian Yang; Xiangyuan Guan; Xianjie Wu; Xiang Li; Ge Zhang; Jiaheng Liu; Yuying Mai; Yutao Zeng; Zhoufutu Wen; Ke Jin; Baorui Wang; Weixiao Zhou; Yunhong Lu; Hangyuan Ji; Tongliang Li; Wenhao Huang; Zhoujun Li
Venue: ICCV 2025 (main)

Abstract: The increasing application of multi-modal large language models (MLLMs) across various sectors has spotlighted the essence of their output reliability and accuracy, particularly their ability to produce content grounded in factual information (e.g. common and domain-specific knowledge). In this work, we introduce SimpleVQA, the first comprehensive multi-modal benchmark to evaluate the factuality ability of MLLMs to answer natural language short questions. SimpleVQA is characterized by 7 key features: it is based on bilingual, it covers multiple tasks and multiple scenarios, ensures high quality and challenging queries, maintains static and timeless reference answers, and is straightforward to evaluate. Our approach involves categorizing visual question-answering items into 9 different tasks around objective events or common knowledge and situating these within 9 scenario domains. Rigorous quality control processes are implemented to guarantee high-quality, concise, and clear answers, facilitating evaluation with minimal variance via an LLM-as-a-judge scoring system. Using SimpleVQA, we perform a comprehensive assessment of leading 18 MLLMs and 8 text-only LLMs, delving into their image comprehension and text generation abilities by identifying and analyzing error cases.