GenAI4ELab/papercli-papers huggingface dataset:
Task Vector Quantization for Memory-Efficient Model Merging
Authors: Youngeun Kim; Seunghwan Lee; Aecheon Jung; Bogon Ryu; Sungeun Hong
Venue: ICCV 2025 (main)

Abstract: Model merging enables efficient multi-task models by combining task-specific fine-tuned checkpoints. However, storing multiple task-specific checkpoints requires significant memory, limiting scalability and restricting model merging to larger models and diverse tasks. In this paper, we propose quantizing task vectors (i.e., the difference between pre-trained and fine-tuned checkpoints) instead of quantizing fine-tuned checkpoints. We observe that task vectors exhibit a narrow weight range, enabling low-precision quantization (<= 4 bit) within existing task vector merging frameworks. To further mitigate quantization errors within ultra-low bit precision (e.g., 2 bit), we introduce Residual Task Vector Quantization, which decomposes the task vector into a base vector and offset component. We allocate bits based on quantization sensitivity, ensuring precision while minimizing error within a memory budget. Experiments on image classification and dense prediction show our method maintains or improves model merging performance while using only 8% of the memory required for full-precision checkpoints. Our code is available at https://aim-skku.github.io/TVQ/.
Dirichlet-Constrained Variational Codebook Learning for Temporally Coherent Video Face Restoration
Authors: Baoyou Chen; Ce Liu; Weihao Yuan; Zilong Dong; Siyu Zhu
Venue: ICCV 2025 (main)

Abstract: Video face restoration faces a critical challenge in maintaining temporal consistency while recovering fine facial details from degraded inputs. This paper presents a novel approach that extends Vector-Quantized Variational Autoencoders (VQ-VAEs), pretrained on static high-quality portraits, into a video restoration framework through variational latent space modeling. Our key innovation lies in reformulating discrete codebook representations as Dirichlet-distributed continuous variables, enabling probabilistic transitions between facial features across frames. A spatio-temporal Transformer architecture jointly models inter-frame dependencies and predicts latent distributions, while a Laplacian-constrained reconstruction loss combined with perceptual (LPIPS) regularization enhances both pixel accuracy and visual quality. Comprehensive evaluations on blind face restoration, video inpainting, and facial colorization tasks demonstrate state-of-the-art performance. This work establishes an effective paradigm for adapting intensive image priors, pretrained on high-quality images, to video restoration while addressing the critical challenge of flicker artifacts. The source code has been open-sourced and is available at https://github.com/fudan-generative-vision/DicFace.
Dual-Expert Consistency Model for Efficient and High-Quality Video Generation
Authors: Zhengyao Lv; Chenyang Si; Tianlin Pan; Zhaoxi Chen; Kwan-Yee K. Wong; Yu Qiao; Ziwei Liu
Venue: ICCV 2025 (main)

Abstract: Diffusion Models have achieved remarkable results in video synthesis but require iterative denoising steps, leading to substantial computational overhead. Consistency Models have made significant progress in accelerating diffusion models. However, directly applying them to video diffusion models often results in severe degradation of temporal consistency and appearance details. In this paper, by analyzing the training dynamics of Consistency Models, we identify a key conflicting learning dynamics during the distillation process: there is a significant discrepancy in the optimization gradients and loss contributions across different timesteps. This discrepancy prevents the distilled student model from achieving an optimal state, leading to compromised temporal consistency and degraded appearance details.To address this issue, we propose a parameter-efficient Dual-Expert Consistency Model (DCM), where a semantic expert focuses on learning semantic layout and motion, while a detail expert specializes in fine detail refinement. Furthermore, we introduce Temporal Coherence Loss to improve motion consistency for the semantic expert and apply GAN and Feature Matching Loss to enhance the synthesis quality of the detail expert. Our approach achieves state-of-the-art visual quality with significantly reduced sampling steps, demonstrating the effectiveness of expert specialization in video diffusion model distillation. Our code and models will be made publicly available.
GeoFormer: Geometry Point Encoder for 3D Object Detection with Graph-based Transformer
Authors: Xin Jin; Haisheng Su; Cong Ma; Kai Liu; Wei Wu; Fei Hui; Junchi Yan
Venue: ICCV 2025 (main)

Abstract: Lidar-based 3D detection is one of the most popular research fields in autonomous driving. 3D detectors typically detect specific targets in a scene according to the pattern formed by the spatial distribution of point clouds. However, existing voxel-based methods usually adopt MLP and global pooling (e.g., PointNet, CenterPoint) as voxel feature encoder, which makes it less effective to extract detailed spatial structure information from raw points, leading to information loss and inferior performance. In this paper, we propose a novel graph-based transformer to encode voxel features by condensing the full and detailed point's geometry, termed as GeoFormer. We first represent points within a voxel as a graph, based on relative distances to capture its spatial geometry. Then, We introduce a geometry-guided transformer architecture to encode voxel features, where the adjacent geometric clues are used to re-weight point feature similarities, enabling more effective extraction of geometric relationships between point pairs at varying distances. We highlight that GeoFormer is a plug-and-play module which can be seamlessly integrated to enhance the performance of existing voxel-based detectors. Extensive experiments conducted on three popular outdoor datasets demonstrate that our GeoFormer achieves the start-of-the-art performance on both effectiveness and robustness comparisons.
Unraveling the Effects of Synthetic Data on End-to-End Autonomous Driving
Authors: Junhao Ge; Zuhong Liu; Longteng Fan; Yifan Jiang; Jiaqi Su; Yiming Li; Zhejun Zhang; Siheng Chen
Venue: ICCV 2025 (main)

Abstract: End-to-end (E2E) autonomous driving (AD) models require diverse, high-quality data to perform well across various driving scenarios. However, collecting large-scale real-world data is expensive and time-consuming, making high-fidelity synthetic data essential for enhancing data diversity and model robustness. Existing driving simulators for synthetic data generation have significant limitations: game-engine-based simulators struggle to produce realistic sensor data, while NeRF-based and diffusion-based methods face efficiency challenges. Additionally, recent simulators designed for closed-loop evaluation provide limited interaction with other vehicles, failing to simulate complex real-world traffic dynamics. To address these issues, we introduce SceneCrafter, a realistic, interactive, and efficient AD simulator based on 3D Gaussian Splatting (3DGS). SceneCrafter not only efficiently generates realistic driving logs across diverse traffic scenarios but also enables robust closed-loop evaluation of end-to-end models. Experimental results demonstrate that SceneCrafter serves as both a reliable evaluation platform and a efficient data generator that significantly improves end-to-end model generalization.
Dense2MoE: Restructuring Diffusion Transformer to MoE for Efficient Text-to-Image Generation
Authors: Youwei Zheng; Yuxi Ren; Xin Xia; Xuefeng Xiao; Xiaohua Xie
Venue: ICCV 2025 (main)

Abstract: Diffusion Transformer (DiT) has demonstrated remarkable performance in text-to-image generation; however, its large parameter size results in substantial inference overhead. Existing parameter compression methods primarily focus on pruning, but aggressive pruning often leads to severe performance degradation due to reduced model capacity. To address this limitation, we pioneer the transformation of a dense DiT into a Mixture of Experts (MoE) for structured sparsification, reducing the number of activated parameters while preserving model capacity. Specifically, we replace the Feed-Forward Networks (FFNs) in DiT Blocks with MoE layers, reducing the number of activated parameters in the FFNs by 62.5%.Furthermore, we propose the Mixture of Blocks (MoB) to selectively activate DiT blocks, thereby further enhancing sparsity.To ensure an effective dense-to-MoE conversion, we design a multi-step distillation pipeline, incorporating Taylor metric-based expert initialization, knowledge distillation with load balancing, and group feature loss for MoB optimization. We transform large diffusion transformers (e.g., FLUX.1 [dev]) into an MoE structure, reducing activated parameters by 60% while maintaining original performance and surpassing pruning-based approaches in extensive experiments. Overall, Dense2MoE establishes a new paradigm for efficient text-to-image generation.
KinMo: Kinematic-aware Human Motion Understanding and Generation
Authors: Pengfei Zhang; Pinxin Liu; Pablo Garrido; Hyeongwoo Kim; Bindita Chaudhuri
Venue: ICCV 2025 (main)

Abstract: Current human motion synthesis frameworks rely on global action descriptions, creating a modality gap that limits both motion understanding and generation capabilities. A single coarse description, such as "run", fails to capture essential details like variations in speed, limb positioning, and kinematic dynamics, leading to significant ambiguities between text and motion modalities. To address this challenge, we introduce KinMo, a unified framework built on a hierarchical describable motion representation that extends beyond global action by incorporating kinematic group movements and their interactions.We design an automated annotation pipeline to generate high-quality, fine-grained descriptions for this decomposition, resulting in the KinMo dataset. To leverage these structured descriptions, we propose Hierarchical Text-Motion Alignment, improving spatial understanding by integrating additional motion details. Furthermore, we introduce a coarse-to-fine generation procedure to demonstrate how enhanced spatial understanding benefits motion synthesis. Experimental results show that KinMo significantly improves motion understanding, demonstrated by enhanced text-motion retrieval performance and enabling more fine-grained motion generation and editing capabilities. Project Page: https://andypinxinliu.github.io/KinMo
NeuFrameQ: Neural Frame Fields for Scalable and Generalizable Anisotropic Quadrangulation
Authors: Ying-Tian Liu; Jiajun Li; Yu-Tao Liu; Xin Yu; Yuan-Chen Guo; Yan-Pei Cao; Ding Liang; Ariel Shamir; Song-Hai Zhang
Venue: ICCV 2025 (main)

Abstract: Quad meshes play a crucial role in computer graphics applications, yet automatically generating high-quality quad meshes remains challenging. Traditional quadrangulation approaches rely on local geometric features and manual constraints, often producing suboptimal mesh layouts that fail to capture global shape semantics. We introduce NeuFrameQ, a novel learning-based framework for scalable and generalizable mesh quadrangulation via frame field prediction. We first create a large-scale dataset of high-quality quad meshes with various shapes to serve as priors of domain knowledge. Empowered by this dataset, we employ a connectivity-agnostic learning approach that operates on point clouds with normals, enabling robust processing of complex geometries. By decomposing frame field prediction into direction regression and magnitude estimation tasks, we effectively handle the ill-posed nature in frame field estimation. We also employ the polyvector representation and attention mechanism in both tasks to handle the inherent ambiguities in frame field representation. Extensive experiments demonstrate that NeuFrameQ produces high-quality quad meshes with superior semantic alignment, also for geometries derived from neural fields. Our method significantly advances the state of the art in automatic quad mesh generation, bridging the gap between neural content creation and production-ready geometric assets.
Keyframe-oriented Vision Token Pruning: Enhancing Efficiency of Large Vision Language Models on Long-Form Video Processing
Authors: Yudong Liu; Jingwei Sun; Yueqian Lin; Jianyi Zhang; Jingyang Zhang; Ming Yin; Qinsi Wang; Hai Li; Yiran Chen
Venue: ICCV 2025 (main)

Abstract: Vision language models (VLMs) demonstrate strong capabilities in jointly processing visual and textual data. However, they often incur substantial computational overhead due to redundant visual information, particularly in long-form video scenarios. Existing approaches predominantly focus on either vision token pruning, which may overlook spatio-temporal dependencies, or keyframe selection, which identifies informative frames but discards others, thus disrupting contextual continuity. In this work, we propose KVTP (Keyframe-oriented Vision Token Pruning), a novel framework that overcomes the drawbacks of token pruning and keyframe selection. By adaptively assigning pruning rates based on frame relevance to the query, KVTP effectively retains essential contextual information while significantly reducing redundant computation. To thoroughly evaluate the long-form video understanding capacities of VLMs, we curated and reorganized subsets from VideoMME, EgoSchema, and NextQA into a unified benchmark named SparseKV-QA that highlights real-world scenarios with sparse but crucial events. Our experiments with VLMs of various scales show that KVTP can reduce token usage by 80% without compromising spatiotemporal and contextual consistency, significantly cutting computation while maintaining the performance. These results demonstrate our approach's effectiveness in efficient long-video processing, facilitating more scalable VLM deployment.
LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos
Authors: Chin-Yang Lin; Cheng Sun; Fu-En Yang; Min-Hung Chen; Yen-Yu Lin; Yu-Lun Liu
Venue: ICCV 2025 (main)

Abstract: LongSplat addresses critical challenges in novel view synthesis (NVS) from casually captured long videos characterized by irregular camera motion, unknown camera poses, and expansive scenes. Current methods often suffer from pose drift, inaccurate geometry initialization, and severe memory limitations. To address these issues, we introduce LongSplat, a robust unposed 3D Gaussian Splatting framework featuring: (1) Incremental Joint Optimization that concurrently optimizes camera poses and 3D Gaussians to avoid local minima and ensure global consistency; (2) a robust Pose Estimation Module leveraging learned 3D priors; and (3) an efficient Octree Anchor Formation mechanism that converts dense point clouds into anchors based on spatial density. Extensive experiments on challenging benchmarks demonstrate that LongSplat achieves state-of-the-art results, substantially improving rendering quality, pose accuracy, and computational efficiency compared to prior approaches. Project page: https://linjohnss.github.io/longsplat/
AVAM: a Universal Training-free Adaptive Visual Anchoring Embedded into Multimodal Large Language Model for Multi-image Question Answering
Authors: Kang Zeng; Guojin Zhong; Jintao Cheng; Jin Yuan; Zhiyong Li
Venue: ICCV 2025 (main)

Abstract: The advancement of Multimodal Large Language Models (MLLMs) has driven significant progress in Visual Question Answering (VQA), evolving from Single to Multi Image VQA (MVQA). However, the increased number of images in MVQA inevitably introduces substantial visual redundancy that is irrelevant to question answering, negatively impacting both accuracy and efficiency. To address this issue, existing methods lack flexibility in controlling the number of compressed visual tokens and tend to produce discrete visual fragments, which hinder MLLMs' ability to comprehend images holistically. In this paper, we propose a straightforward yet universal Adaptive Visual Anchoring strategy, which can be seamlessly integrated into existing MLLMs, offering significant accuracy improvements through adaptive compression. Meanwhile, to balance the results derived from both global and compressed visual input, we further introduce a novel collaborative decoding mechanism, enabling optimal performance. Extensive experiments validate the effectiveness of our method, demonstrating consistent performance improvements across various MLLMs. The code will be publicly available.
WikiAutoGen: Towards Multi-Modal Wikipedia-Style Article Generation
Authors: Zhongyu Yang; Jun Chen; Dannong Xu; Junjie Fei; Xiaoqian Shen; Liangbing Zhao; Chun-Mei Feng; Mohamed Elhoseiny
Venue: ICCV 2025 (main)

Abstract: Knowledge discovery and collection are intelligence-intensive tasks that traditionally require significant human effort to ensure high-quality outputs. Recent research has explored multi-agent frameworks for automating Wikipedia-style article generation by retrieving and synthesizing information from the internet. However, these methods primarily focus on text-only generation, overlooking the importance of multimodal content in enhancing informativeness and engagement. In this work, we introduce WikiAutoGen, a novel system for automated multimodal Wikipedia-style article generation. Unlike prior approaches, WikiAutoGen retrieves and integrates relevant images alongside text, enriching both the depth and visual appeal of generated content. To further improve factual accuracy and comprehensiveness, we propose a multi-perspective self-reflection mechanism, which critically assesses retrieved content from diverse viewpoints to enhance reliability, breadth, and coherence, etc. Additionally, we introduce WikiSeek, a benchmark comprising Wikipedia articles with topics paired with both textual and image-based representations, designed to evaluate multimodal knowledge generation on more challenging topics. Experimental results show that WikiAutoGen outperforms previous methods by 8%-29% on our WikiSeek benchmark, producing more accurate, coherent, and visually enriched Wikipedia-style articles. We show some of our generated examples in https://anonymous.4open.science/r/WikiAutoGen-C3C4
GMMamba: Group Masking Mamba for Whole Slide Image Classification
Authors: Tingting Zheng; Hongxun Yao; Kui Jiang; Yi Xiao; Sicheng Zhao
Venue: ICCV 2025 (main)

Abstract: Recent advances in selective state space models (Mamba) have shown great promise in whole slide image (WSI) classification. Despite this, WSIs contain explicit local redundancy (similar patches) and irrelevant regions (uninformative instances), posing significant challenges for Mamba-based multi-instance learning (MIL) methods in capturing global representations. Furthermore, bag-level approaches struggle to extract critical features from all instances, while group-level methods fail to adequately account for tumor dispersion and intrinsic correlations across groups, leading to suboptimal global representations. To address these issues, we propose group masking Mamba (GMMamba), a novel framework that combines two elaborate modules: (1) intra-group masking Mamba (IMM) for selective instance exploration within groups, and (2) cross-group super-feature sampling (CSS) to ameliorate long-range relation learning. Specifically, IMM adaptively predicts sparse masks to filter out features with low attention scores (i.e., uninformative patterns) during bidirectional Mamba modeling, facilitating the removal of instance redundancies for compact local representation. For improved bag prediction, the CSS module further aggregates sparse group representations into discriminative features, effectively grasping comprehensive dependencies among dispersed and sparse tumor regions inherent in large-scale WSIs. Extensive experiments on four datasets demonstrate that GMMamba outperforms the state-of-the-art ACMIL by 2.2% and 6.4% in accuracy on the TCGA-BRCA and TCGA-ESCA datasets, respectively.
S3R-GS: Streamlining the Pipeline for Large-Scale Street Scene Reconstruction
Authors: Guangting Zheng; Jiajun Deng; Xiaomeng Chu; Yu Yuan; Houqiang Li; Yanyong Zhang
Venue: ICCV 2025 (main)

Abstract: Recently, 3D Gaussian Splatting (3DGS) has reshaped the field of photorealistic 3D reconstruction, achieving impressive rendering quality and speed. However, when applied to large-scale street scenes, existing methods suffer from rapidly escalating per-viewpoint reconstruction costs as scene size increases, leading to significant computational overhead.After revisiting the conventional pipeline, we identify three key factors accounting for this issue: unnecessary local-to-global transformations, excessive 3D-to-2D projections, and inefficient rendering of distant content. To address these challenges, we propose S3R-GS, a 3DGS framework that Streamlines the pipeline for large-scale Street Scene Reconstruction, effectively mitigating these limitations. Moreover, most existing street 3DGS methods rely on ground-truth 3D bounding boxes to separate dynamic and static components, but 3D bounding boxes are difficult to obtain, limiting real-world applicability. To address this, we propose an alternative solution with 2D boxes, which are easier to annotate or can be predicted by off-the-shelf vision foundation models. Such designs together make S3R-GS readily adapt to large, in-the-wild scenarios.Extensive experiments demonstrate that S3R-GS enhances rendering quality and significantly accelerates reconstruction. Remarkably, when applied to videos from the challenging Argoverse2 dataset, it achieves state-of-the-art PSNR and SSIM, reducing reconstruction time to below 50%--and even 20%--of competing methods. Code is available at https://github.com/Tom-zgt/S3R-GS.
LIRA: Reasoning Reconstruction via Multimodal Large Language Models
Authors: Zhen Zhou; Tong Wang; Yunkai Ma; Xiao Tan; Fengshui Jing
Venue: ICCV 2025 (main)

Abstract: Existing language instruction-guided online 3D reconstruction systems mainly rely on explicit instructions or queryable maps, showing inadequate capability to handle implicit and complex instructions. In this paper, we first introduce a reasoning reconstruction task. This task inputs an implicit instruction involving complex reasoning and an RGB-D sequence, and outputs incremental 3D reconstruction of instances that conform to the instruction. To handle this task, we propose LIRA: Language Instructed Reconstruction Assistant. It leverages a multimodal large language model to actively reason about the implicit instruction and obtain instruction-relevant 2D candidate instances and their attributes. Then, candidate instances are back-projected into the incrementally reconstructed 3D geometric map, followed by instance fusion and target instance inference. In LIRA, to achieve higher instance fusion quality, we propose TIFF, a Text-enhanced Instance Fusion module operating within Fragment bounding volume, which is learning-based and fuses multiple keyframes simultaneously. Since the evaluation system for this task is not well established, we propose a benchmark ReasonRecon comprising the largest collection of scene-instruction data samples involving implicit reasoning. Experiments demonstrate that LIRA outperforms existing methods in the reasoning reconstruction task and is capable of running in real time. Code and benchmark are available at https://github.com/zhen6618/LIRA.