GenAI4ELab/papercli-papers huggingface dataset:
Dual-S3D: Hierarchical Dual-Path Selective SSM-CNN for High-Fidelity Implicit Reconstruction
Authors: Luoxi Zhang; Pragyan Shrestha; Yu Zhou; Chun Xie; Itaru Kitahara
Venue: ICCV 2025 (main)

Abstract: Single-view 3D reconstruction aims to recover the complete 3D geometry and appearance of objects from a single RGB image. Due to incomplete image information and ambiguity, this task remains challenging. Existing methods struggle with the trade-off between local detail and global topology, and with interference from early RGB-depth fusion in signed distance function optimization. To address these challenges, we propose Dual-S3D, a novel framework for single-view 3D reconstruction. Our method employs a hierarchical dual-path feature extraction strategy based on stages that utilize convolutional neural networks to anchor local geometric details. In contrast, subsequent stages leverage a Transformer integrated with selective state-space model to capture global topology, enhancing scene understanding and feature representation. Additionally, we design an auxiliary branch that progressively fuses precomputed depth features with pixel-level features to decouple visual and geometric cues effectively. Extensive experiments on the 3D-FRONT and Pix3D datasets demonstrate that our approach significantly outperforms existing methods--reducing chamfer distance by 51%, increasing F-score by 33.6%, and improving normal consistency by 10.3%--thus achieving state-of-the-art reconstruction quality.
SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling
Authors: Xianglong He; Zi-Xin Zou; Chia-Hao Chen; Yuan-Chen Guo; Ding Liang; Chun Yuan; Wanli Ouyang; Yan-Pei Cao; Yangguang Li
Venue: ICCV 2025 (main)

Abstract: Creating high-fidelity 3D meshes with arbitrary topology, including open surfaces and complex interiors, remains a significant challenge. Existing implicit field methods often require costly and detail-degrading watertight conversion, while other approaches struggle with high resolutions. This paper introduces SparseFlex, a novel sparse-structured isosurface representation that enables differentiable mesh reconstruction at resolutions up to 1024^3 directly from rendering losses. SparseFlex combines the accuracy of Flexicubes with a sparse voxel structure, focusing computation on surface-adjacent regions and efficiently handling open surfaces. Crucially, we introduce a frustum-aware sectional voxel training strategy that activates only relevant voxels during rendering, dramatically reducing memory consumption and enabling high-resolution training. This also allows, for the first time, the reconstruction of mesh interiors using only rendering supervision. Building upon this, we demonstrate a complete shape modeling pipeline by training a variational autoencoder (VAE) and a rectified flow transformer for high-quality 3D shape generation. Our experiments show state-of-the-art reconstruction accuracy, with a  82% reduction in Chamfer Distance and a  88% increase in F-score compared to previous methods, and demonstrate the generation of high-resolution, detailed 3D shapes with arbitrary topology. By enabling high-resolution, differentiable mesh reconstruction and generation with rendering losses, SparseFlex significantly advances the state-of-the-art in 3D shape representation and modeling.
Multimodal LLM Guided Exploration and Active Mapping using Fisher Information
Authors: Wen Jiang; Boshu Lei; Katrina Ashton; Kostas Daniilidis
Venue: ICCV 2025 (main)

Abstract: We present an active mapping system that could plan for long-horizon exploration goals and short-term actions with a 3D Gaussian Splatting (3DGS) representation. Existing methods either did not take advantage of recent developments in multimodal Large Language Models (LLM) or did not consider challenges in localization uncertainty which is critical in embodied agents. We propose employing multimodal LLMs for long-horizon planning in conjunction with detailed motion planning using our information-based algorithm. By leveraging high-quality view synthesis from our 3DGS representation, our method employs a multimodal LLM as a zero-shot planner for long-horizon exploration goals from the semantic perspective. We also introduce an uncertainty-aware path proposal and selection algorithm that balances the dual objectives of maximizing the information gain for the environment while minimizing the cost of localization errors. Experiments conducted on the Gibson and Habitat-Matterport 3D datasets demonstrate state-of-the-art results of the proposed method.
Lidar Waveforms are Worth 40x128x33 Words
Authors: Dominik Scheuble; Hanno Holzhüter; Steven Peters; Mario Bijelic; Felix Heide
Venue: ICCV 2025 (main)

Abstract: Lidar has become crucial for autonomous driving, providing high-resolution 3D scans that are key for accurate scene understanding. To this end, lidar sensors measure the time-resolved full waveforms from the returning laser light, which a subsequent digital signal processor (DSP) converts to point clouds by identifying peaks in the waveform. Conventional automotive lidar DSPs process each waveform individually, ignoring potentially valuable context from neighboring waveforms. As a result, lidar point clouds are prone to artifacts from low signal-to-noise ratio (SNR) regions, highly reflective objects, and environmental conditions like fog. While leveraging neighboring waveforms is investigated extensively in transient imaging, applications remain limited to scientific or experimental hardware. In this work, we propose a learned DSP that directly processes full waveforms using a transformer architecture, leveraging features from adjacent waveforms to generate high-fidelity multi-echo point clouds. To assess our method, we capture data in real-world driving scenarios and a weather chamber with a conventional automotive lidar. Trained on synthetic and real data, the method improves Chamfer distance by 32cm and 20cm compared to conventional peak finding and existing transient imaging approaches, respectively. This translates to maximum range improvements of up to 17m in fog and 14m in nominal real-world conditions.
LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation
Authors: Donald Shenaj; Ondrej Bohdal; Mete Ozay; Pietro Zanuttigh; Umberto Michieli
Venue: ICCV 2025 (main)

Abstract: Recent advancements in image generation models have enabled personalized image creation with both user-defined subjects (content) and styles. Prior works achieved personalization by merging corresponding low-rank adapters (LoRAs) through optimization-based methods, which are computationally demanding and unsuitable for real-time use on resource-constrained devices like smartphones. To address this, we introduce LoRA.rar, a method that not only improves image quality but also achieves a remarkable speedup of over 4000x in the merging process. We collect a dataset of style and subject LoRAs and pre-train a hypernetwork on a diverse set of content-style LoRA pairs, learning an efficient merging strategy that generalizes to new, unseen content-style pairs, enabling fast, high-quality personalization. Moreover, we identify limitations in existing evaluation metrics for content-style quality and propose a new protocol using multimodal large language models (MLLMs) for more accurate assessment. Our method significantly outperforms the current state of the art in both content and style fidelity, as validated by MLLM assessments and human evaluations.
X-Fusion: Introducing New Modality to Frozen Large Language Models
Authors: Sicheng Mo; Thao Nguyen; Xun  Huang; Siddharth Srinivasan Iyer; Yijun Li; Yuchen Liu; Abhishek  Tandon; Eli Shechtman; Krishna Kumar  Singh; Yong Jae  Lee; Bolei  Zhou; Yuheng Li
Venue: ICCV 2025 (main)

Abstract: We propose X-Fusion, a framework that extends pretrained Large Language Models (LLMs) for multimodal tasks while preserving their language capabilities. X-Fusion employs a dual-tower design with modality-specific weights, keeping the LLM's parameters frozen while integrating vision-specific information for both understanding and generation. We find that incorporating understanding-focused data improves generation quality, reducing image data noise enhances overall performance, and feature alignment accelerates convergence for smaller models but has minimal impact on larger ones. Our findings provide valuable insights into building efficient unified multimodal models.
KOEnsAttack: Towards Efficient Data-Free Black-Box Adversarial Attacks via Knowledge-Orthogonalized Substitute Ensembles
Authors: Chaoyong Yang; Jia-Li Yin; Bin Chen; Zhaozhe Hu; Xiaolei Liu; Wei Lin
Venue: ICCV 2025 (main)

Abstract: Data-free black-box attacks aim to attack a model without access to either the model parameters or training data. Existing methods use a generator to synthesize training samples and then train a substitute model to imitate the victim model. The adversarial examples (AEs) are finally generated using the substitute model to transfer to the victim model. To this end, how to generate diverse training samples for substitute model training and improve the transferability of AEs from the substitute model to victim model become the core challenges. In this paper, we propose a Knowledge-Orthogonalized Ensemble Attack, dubbed KOEnsAttack, to accomplish these two goals. We first use dual networks as the ensemble substitute model, and then propose a sample hardness enhancement to transform the samples from the generator into hard samples that exist in the controversial regions of the dual models for promoting the sample diversity. Next, during the substitute model training, we design a knowledge orthogonalization module to guide the dual networks in learning complementary and useful information from the black-box, thereby enhancing the transferability of adversarial samples generated on the final ensemble model. Extensive experiments on several datasets are conducted to evaluate the effectiveness of our method. The results show that the proposed method can achieve superior performance compared with the state-of-the-art competitors.
Multimodal Large Language Model-Guided ISP Hyperparameter Optimization with Dynamic Preference Learning
Authors: Xinyu Sun; Zhikun Zhao; Congyan Lang; Bing Li; Juan Wang
Venue: ICCV 2025 (main)

Abstract: The image signal processing (ISP) pipeline is responsible for converting the RAW images collected from the sensor into high-quality RGB images. It contains a series of image processing modules and associated ISP hyperparameters. Recent learning-based approaches aim to automate ISP hyperparameter optimization using solely image data. However, their unimodal nature limits their ability to capture richer contextual information, reducing robustness and adaptability across diverse application scenarios. To address this limitation, we propose a Multimodal Large Language Model (MLLM)-guided ISP hyperparameter optimization framework, which integrates textual insights generated by MLLMs into the optimization process. By incorporating both high-level semantic cues and low-level image quality descriptors, our method enhances contextual understanding and task adaptability. Additionally, we introduce a Dynamic Pair Generation (DPG) refinement strategy based on Direct Preference Optimization (DPO), facilitating efficient preference alignment without the need for extensive human-labeled data. This two-stage framework not only improves the directional consistency of optimization but also significantly reduces the computational and data preparation overhead. We validate our proposed methods on both high-level and low-level vision tasks, demonstrating superior performance compared to existing methods.
SALAD -- Semantics-Aware Logical Anomaly Detection
Authors: Matic Fučka; Vitjan Zavrtanik; Danijel Skočaj
Venue: ICCV 2025 (main)

Abstract: Recent surface anomaly detection methods excel at identifying structural anomalies, such as dents and scratches, but struggle with logical anomalies, such as irregular or missing object components. The best-performing logical anomaly detection approaches rely on aggregated pretrained features or handcrafted descriptors (most often derived from composition maps), which discard spatial and semantic information, leading to suboptimal performance. We propose SALAD, a semantics-aware discriminative logical anomaly detection method that incorporates a newly proposed composition branch to explicitly model the distribution of object composition maps, consequently learning important semantic relationships. Additionally, we introduce a novel procedure for extracting composition maps that requires no hand-made labels or category-specific information, in contrast to previous methods. By effectively modelling the composition map distribution, SALAD significantly improves upon state-of-the-art methods on the standard benchmark for logical anomaly detection, MVTec LOCO, achieving an impressive image-level AUROC of 96.1%. Code: https://github.com/MaticFuc/SALAD
Federated Continuous Category Discovery and Learning
Authors: Lixu Wang; Chenxi Liu; Junfeng Guo; Qingqing Ye; Heng Huang; Haibo Hu; Wei Dong
Venue: ICCV 2025 (main)

Abstract: Federated Learning (FL) studies often assume a static data distribution, whereas real-world scenarios involve dynamic changes. To address this gap, we study Federated Continuous Category Discovery and Learning (FC^2DL), an essential yet underexplored problem that enables FL models to evolve continuously by discovering and learning novel data categories. The key challenge in FC^2DL lies in merging and aligning categories discovered and learned by different clients, all while maintaining privacy. To tackle this, we propose the Global Prototype Alignment (GPA) framework. GPA first estimates the number of categories and constructs global prototypes by locating high-density regions in the representation space through bi-level clustering. To mitigate pseudo-label noise, GPA then employs a semantic-weighted loss to capture correlations between global prototypes and the novel data. This semantic weighting strategy is also used for contrastive loss, facilitating unsupervised novel-category learning. Besides, GPA incorporates a mixup-based mechanism for both data and models, effectively mitigating interference between known and novel categories while alleviating forgetting. Extensive experiments across multiple datasets demonstrate GPA's superiority over state-of-the-art baseline approaches. Notably, GPA achieves absolute gains of 5.7% to 13.1% in novel category accuracy while preserving known category performance. Furthermore, GPA is highly adaptable, equipping various mainstream FL algorithms with category discovery and learning capabilities, underscoring its potential for real-world deployment.
Motion Synthesis with Sparse and Flexible Keyjoint Control
Authors: Inwoo Hwang; Jinseok Bae; Donggeun Lim; Young Min Kim
Venue: ICCV 2025 (main)

Abstract: Creating expressive character animations is labor-intensive, requiring intricate manual adjustment of animators across space and time. Previous works on controllable motion generation often rely on a predefined set of dense spatio-temporal specifications (e.g., dense pelvis trajectories with exact per-frame timing), limiting practicality for animators. To process high-level intent and intuitive control in diverse scenarios, we propose a practical controllable motions synthesis framework that respects sparse and flexible keyjoint signals. Our approach employs a decomposed diffusion-based motion synthesis framework that first synthesizes keyjoint movements from sparse input control signals and then synthesizes full-body motion based on the completed keyjoint trajectories. The low-dimensional keyjoint movements can easily adapt to various control signal types, such as end-effector position for diverse goal-driven motion synthesis, or incorporate functional constraints on a subset of keyjoints. Additionally, we introduce a time-agnostic control formulation, eliminating the need for frame-specific timing annotations and enhancing control flexibility. Then, the shared second stage can synthesize a natural whole-body motion that precisely satisfies the task requirement from dense keyjoint movements. We demonstrate the effectiveness of sparse and flexible keyjoint control through comprehensive experiments on diverse datasets and scenarios.
EVOLVE: Event-Guided Deformable Feature Transfer and Dual-Memory Refinement for Low-Light Video Object Segmentation
Authors: Jong-Hyeon Baek; Jiwon Oh; Yeong Jun Koh
Venue: ICCV 2025 (main)

Abstract: Video Object Segmentation (VOS) in low-light scenarios remains highly challenging due to significant texture loss and severe noise, which often lead to unreliable image feature generation and degraded segmentation performance. To address this issue, we propose EVOLVE, a novel event-guided deformable feature transfer and dual-memory refinement framework for low-light VOS. EVOLVE addresses spatial misalignment between frames and improves object representation by utilizing event-driven cues. The event-guided deformable feature transfer (EDFT) module enhances feature alignment through event-driven deformable convolutions, where offsets derived from event features enable motion-aware spatial adjustments, leading to more precise propagation of object features in reference frames. Furthermore, the dual-memory object transformer (DMOT) iteratively refines object representations by maintaining and updating both image-based and event-based memory representations. Through its memory refinement module (MRM), DMOT selectively enhances relevant object features while suppressing background noise, resulting in stable and temporally coherent segmentation results. Extensive experiments on low-light VOS benchmarks demonstrate that EVOLVE achieves state-of-the-art segmentation performance, surpassing both event-based and image-based VOS methods in accuracy and computational efficiency.
ARGUS: Hallucination and Omission Evaluation in Video-LLMs
Authors: Ruchit Rawal; Reza Shirkavand; Heng Huang; Gowthami Somepalli; Tom Goldstein
Venue: ICCV 2025 (main)

Abstract: Video large language models have not yet been widely deployed, largely due to their tendency to hallucinate. Typical benchmarks for Video-LLMs rely simply on multiple choice questions. Unfortunately, VideoLLMs hallucinate far more aggressively on freeform text generation tasks like video captioning than they do on multiple choice verification tasks. To address this weakness, we propose ARGUS, a VideoLLM benchmark that measures freeform video captioning performance. By comparing VideoLLM outputs to human ground truth captions, ARGUS quantifies dual metrics. First, we measure the rate of hallucinations in the form of incorrect statements about video content or temporal relationships. Second, we measure the rate at which the model omits important descriptive details. Together, these metrics form a comprehensive view of video captioning.
DiTaiListener: Controllable High Fidelity Listener Video Generation with Diffusion
Authors: Maksim Siniukov; Di Chang; Minh Tran; Hongkun Gong; Ashutosh Chaubey; Mohammad Soleymani
Venue: ICCV 2025 (main)

Abstract: Generating naturalistic and nuanced listener motions for extended interactions remains an open problem. Existing methods often rely on low-dimensional motion codes for facial behavior generation followed by photorealistic rendering, limiting both visual fidelity and expressive richness. To address these challenges, we introduce DiTaiListener, powered by a video diffusion model with multimodal conditions. Our approach first generates short segments of listener responses conditioned on the speaker's speech and facial motions with DiTaiListener-Gen. It then refines the transitional frames via DiTaiListener-Edit for a seamless transition. Specifically, DiTaiListener-Gen adapts a Diffusion Transformer (DiT) for the task of listener head portrait generation by introducing a Causal Temporal Multimodal Adapter (CTM-Adapter) to process speakers' auditory and visual cues. CTM-Adapter integrates speakers' input in a causal manner into the video generation process to ensure temporally coherent listener responses. For long-form video generation, we introduce DiTaiListener-Edit, a transition refinement video-to-video diffusion model. The model fuses video segments into smooth and continuous videos, ensuring temporal consistency in facial expressions and image quality when merging short video segments produced by DiTaiListener-Gen. Quantitatively, DiTaiListener achieves the state-of-the-art performance on benchmark datasets in both photorealism (+73.8% in FID on RealTalk) and motion representation (+6.1% in FD metric on VICO) spaces. User studies confirm the superior performance of DiTaiListener, with the model being the clear preference in terms of feedback, diversity, and smoothness, outperforming competitors by a significant margin. See the project page for more results: https://ihp-lab.github.io/DiTaiListener/
Humans as a Calibration Pattern: Dynamic 3D Scene Reconstruction from Unsynchronized and Uncalibrated Videos
Authors: Changwoon Choi; Jeongjun Kim; Geonho Cha; Minkwan Kim; Dongyoon Wee; Young Min Kim
Venue: ICCV 2025 (main)

Abstract: Recent works on dynamic 3D neural field reconstruction assume the input from synchronized multi-view videos whose poses are known. The input constraints are often not satisfied in real-world setups, making the approach impractical. We show that unsynchronized videos from unknown poses can generate dynamic neural fields as long as the videos capture human motion. Humans are one of the most common dynamic subjects captured in videos, and their shapes and poses can be estimated using state-of-the-art libraries. While noisy, the estimated human shape and pose parameters provide a decent initialization point to start the highly non-convex and under-constrained problem of training a consistent dynamic neural representation. Given the shape and pose parameters of humans in individual frames, we formulate methods to calculate the time offsets between videos, followed by camera pose estimations that analyze the 3D joint positions. Then, we train the dynamic neural fields employing multiresolution grids while we concurrently refine both time offsets and camera poses. The setup still involves optimizing many parameters; therefore, we introduce a robust progressive learning strategy to stabilize the process. Experiments show that our approach achieves accurate spatio-temporal calibration and high-quality scene reconstruction in challenging conditions.