GenAI4ELab/papercli-papers huggingface dataset:
CA-I2P: Channel-Adaptive Registration Network with Global Optimal Selection
Authors: Zhixin Cheng; Jiacheng Deng; Xinjun Li; Xiaotian Yin; Bohao Liao; Baoqun Yin; Wenfei Yang; Tianzhu Zhang
Venue: ICCV 2025 (main)

Abstract: Detection-free methods typically follow a coarse-to-fine pipeline, extracting image and point cloud features for patch-level matching and refining dense pixel-to-point correspondences. However, differences in feature channel attention between images and point clouds may lead to degraded matching results, ultimately impairing registration accuracy.Furthermore, similar structures in the scene could lead to redundant correspondences in cross-modal matching.To address these issues, we propose Channel Adaptive Adjustment Module (CAA) and Global Optimal Selection Module (GOS). CAA enhances intra-modal features and suppresses cross-modal sensitivity, while GOS replaces local selection with global optimization. Experiments on RGB-D Scenes V2 and 7-Scenes demonstrate the superiority of our method, achieving state-of-the-art performance in image-to-point cloud registration.
Exploiting Vision Language Model for Training-Free 3D Point Cloud OOD Detection via Graph Score Propagation
Authors: Tiankai Chen; Yushu Li; Adam Goodge; Fei Teng; Xulei Yang; Tianrui Li; Xun Xu
Venue: ICCV 2025 (main)

Abstract: Out-of-distribution (OOD) detection in 3D point cloud data remains a challenge, particularly in applications where safe and robust perception is critical. While existing OOD detection methods have shown progress for 2D image data, extending these to 3D environments involves unique obstacles. This paper introduces a training-free framework that leverages Vision-Language Models (VLMs) for effective OOD detection in 3D point clouds. By constructing a graph based on class prototypes and testing data, we exploit the data manifold structure to enhancing the effectiveness of VLMs for 3D OOD detection. We propose a novel Graph Score Propagation (GSP) method that incorporates prompt clustering and self-training negative prompting to improve OOD scoring with VLM. Our method is also adaptable to few-shot scenarios, providing options for practical applications. We demonstrate that GSP consistently outperforms state-of-the-art methods across synthetic and real-world datasets for 3D point cloud OOD detection.
Monocular Facial Appearance Capture in the Wild
Authors: Yingyan Xu; Kate Gadola; Prashanth Chandran; Sebastian Weiss; Markus Gross; Gaspard Zoss; Derek Bradley
Venue: ICCV 2025 (main)

Abstract: We present a new method for reconstructing the appearance properties of human faces from a lightweight capture procedure in an unconstrained environment. Our method recovers the surface geometry, diffuse albedo, specular intensity and specular roughness from a monocular video containing a simple head rotation in-the-wild. Notably, we make no simplifying assumptions on the environment lighting, and we explicitly take visibility and occlusions into account. As a result, our method can produce facial appearance maps that approach the fidelity of studio-based multi-view captures, but with a far easier and cheaper procedure.
Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis
Authors: Bowen Zhang; Sicheng Xu; Chuxin Wang; Jiaolong Yang; Feng Zhao; Dong Chen; Baining Guo
Venue: ICCV 2025 (main)

Abstract: In this paper, we present a novel framework for video-to-4D generation that creates high-quality dynamic 3D content from single video inputs. Direct 4D diffusion modeling is extremely challenging due to costly data construction and the high-dimensional nature of jointly representing 3D shape, appearance, and motion. We address these challenges by introducing a Direct 4DMesh-to-GS Variation Field VAE that directly encodes canonical Gaussian Splats (GS) and their temporal variations from 3D animation data without per-instance fitting, and compresses high-dimensional animations into a compact latent space. Building upon this efficient representation, we train a Gaussian Variation Field diffusion model with temporal-aware Diffusion Transformer conditioned on input videos and canonical GS. Trained on carefully-curated animatable 3D objects from the Objaverse dataset, our model demonstrates superior generation quality compared to existing methods. It also exhibits remarkable generalization to in-the-wild video inputs despite being trained exclusively on synthetic data, paving the way for generating high-quality animated 3D content. Project page: https://gvfdiffusion.github.io/.
Can3Tok: Canonical 3D Tokenization and Latent Modeling of Scene-Level 3D Gaussians
Authors: Quankai Gao; Iliyan Georgiev; Tuanfeng Y. Wang; Krishna Kumar Singh; Ulrich Neumann; Jae Shin Yoon
Venue: ICCV 2025 (main)

Abstract: 3D generation has made significant progress, however, it still largely remains at the object-level. Feedforward 3D scene-level generation has been rarely explored due to the lack of models capable of scaling-up latent representation learning on 3D scene-level data. Unlike object-level generative models, which are trained on well-labeled 3D data in a bounded canonical space, scene-level generations with 3D scenes represented by 3D Gaussian Splatting (3DGS) are unbounded and exhibit scale inconsistency across different scenes, making unified latent representation learning for generative purposes extremely challenging. In this paper, we introduce Can3Tok, the first 3D scene-level variational autoencoder (VAE) capable of encoding a large number of Gaussian primitives into a low-dimensional latent embedding, which effectively captures both semantic and spatial information of the inputs. Beyond model design, we propose a general pipeline for 3D scene data processing to address scale inconsistency issue. We validate our method on the recent scene-level 3D dataset DL3DV-10K, where we found that only Can3Tok successfully generalizes to novel 3D scenes, while compared methods fail to converge on even a few hundred scene inputs during training and exhibit zero generalization ability during inference. Finally, we demonstrate image-to-3DGS and text-to-3DGS generation as our applications to demonstrate it's ability to faciliate downstream generation tasks. Code will be released.
EmotiCrafter: Text-to-Emotional-Image Generation based on Valence-Arousal Model
Authors: Shengqi Dang; Yi He; Long Ling; Ziqing Qian; Nanxuan Zhao; Nan Cao
Venue: ICCV 2025 (main)

Abstract: Recent research shows that emotions can enhance users' cognition and influence information communication. While research on visual emotion analysis is extensive, limited work has been done on helping users generate emotionally rich image content. Existing work on emotional image generation relies on discrete emotion categories, making it challenging to capture complex and subtle emotional nuances accurately. Additionally, these methods struggle to control the specific content of generated images based on text prompts. In this paper, we introduce the task of continuous emotional image content generation (C-EICG) and present EmotiCrafter, a general emotional image generation model that generates images based on free text prompts and Valence-Arousal (V-A) values. It leverages a novel emotion-embedding mapping network to fuse V-A values into textual features, enabling the capture of emotions in alignment with intended input prompts. A novel loss function is also proposed to enhance emotion expression. The experimental results show that our method effectively generates images representing specific emotions with the desired content and outperforms existing techniques.
HFD-Teacher: High-Frequency Depth Distillation from Depth Foundation Models for Enhanced Depth Completion
Authors: Zhiyuan Yang; Anqi Cheng; Haiyue Zhu; Tianjiao Li; Pey Yuen Tao; Kezhi Mao
Venue: ICCV 2025 (main)

Abstract: Depth completion, the task of reconstructing dense depth maps from sparse depth and RGB images, plays a critical role in 3D scene understanding. However, existing methods often struggle to recover high-frequency details, such as regions with fine structures or weak signals, since depth sensors may fail to capture accurate depth maps in those regions, leading to imperfect supervision ground truth. To overcome this limitation, it is essential to introduce an alternative training source for the models. Emerging depth foundation models excel at producing high-frequency details from RGB images, yet their depth maps suffer from inconsistent scaling. Therefore, we propose a novel teacher-student framework that enhances depth completion by distilling high-frequency knowledge from depth foundation models across multiple scales. Our approach introduces two key innovations: Adaptive Local Wavelet Decomposition, which dynamically adjusts wavelet decomposition level based on local complexity for efficient feature extraction, and Topological Constraints, which apply persistent homology to enforce structural coherence and suppress spurious depth edges. Experiment results demonstrate that our method outperforms state-of-the-art methods, preserving high-frequency details and overall depth fidelity.
EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
Authors: Yu-Cheng Lin; Yu-Syuan Xu; Hao-Wei Chen; Hsien-Kai Kuo; Chun-Yi Lee
Venue: ICCV 2025 (main)

Abstract: Image restoration is a key task in low-level computer vision that aims to reconstruct high-quality images from degraded inputs. The emergence of Vision Mamba, which draws inspiration from the advanced state space model Mamba, marks a significant advancement in this field. Vision Mamba demonstrates excellence in modeling long-range dependencies with linear complexity, a crucial advantage for image restoration tasks. Despite its strengths, Vision Mamba encounters challenges in low-level vision tasks, including computational complexity that scales with the number of scanning sequences and local pixel forgetting. To address these limitations, this study introduces Efficient All-Around Mamba (EAMamba), an enhanced framework that incorporates a Multi-Head Selective Scan Module (MHSSM) with an all-around scanning mechanism. MHSSM efficiently aggregates multiple scanning sequences, which avoids increases in computational complexity and parameter count. The all-around scanning strategy implements multiple patterns to capture holistic information and resolves the local pixel forgetting issue. Our experimental evaluations validate these innovations across several restoration tasks, including super resolution, denoising, deblurring, and dehazing. The results validate that EAMamba achieves a significant 31-89% reduction in FLOPs while maintaining favorable performance compared to existing low-level Vision Mamba methods.
AHCPTQ: Accurate and Hardware-Compatible Post-Training Quantization for Segment Anything Model
Authors: Wenlun Zhang; Yunshan Zhong; Shimpei Ando; Kentaro Yoshioka
Venue: ICCV 2025 (main)

Abstract: The Segment Anything Model (SAM) has demonstrated strong versatility across various visual tasks. However, its large storage requirements and high computational cost pose challenges for practical deployment. Post-training quantization (PTQ) has emerged as an effective strategy for efficient deployment, but we identify two key challenges in SAM that hinder the effectiveness of existing PTQ methods: the heavy-tailed and skewed distribution of post-GELU activations, and significant inter-channel variation in linear projection activations. To address these challenges, we propose AHCPTQ, an accurate and hardware-efficient PTQ method for SAM. AHCPTQ introduces hardware-compatible Hybrid Log-Uniform Quantization (HLUQ) to manage post-GELU activations, employing log2 quantization for dense small values and uniform quantization for sparse large values to enhance quantization resolution. Additionally, AHCPTQ incorporates Channel-Aware Grouping (CAG) to mitigate inter-channel variation by progressively clustering activation channels with similar distributions, enabling them to share quantization parameters and improving hardware efficiency. The combination of HLUQ and CAG not only enhances quantization effectiveness but also ensures compatibility with efficient hardware execution. For instance, under the W4A4 configuration on the SAM-L model, AHCPTQ achieves 36.6% mAP on instance segmentation with the DINO detector, while achieving a 7.89x speedup and 8.64x energy efficiency over its floating-point counterpart in FPGA implementation.
WonderTurbo: Generating Interactive 3D World in 0.72 Seconds
Authors: Chaojun Ni; Xiaofeng Wang; Zheng Zhu; Weijie Wang; Haoyun Li; Guosheng Zhao; Jie Li; Wenkang Qin; Guan Huang; Wenjun Mei
Venue: ICCV 2025 (main)

Abstract: Interactive 3D generation is gaining momentum and capturing extensive attention for its potential to create immersive virtual experiences. However, a critical challenge in current 3D generation technologies lies in achieving real-time interactivity. To address this issue, we introduce WonderTurbo, the first real-time interactive 3D scene generation framework capable of generating novel perspectives of 3D scenes within 0.72 seconds. Specifically, WonderTurbo accelerates both geometric and appearance modeling in 3D scene generation. In terms of geometry, we propose StepSplat, an innovative method that constructs efficient 3D geometric representations through dynamic updates, each taking only 0.26 seconds. Additionally, we design QuickDepth, a lightweight depth completion module that provides consistent depth input for StepSplat, further enhancing geometric accuracy. For appearance modeling, we develop FastPaint, a 2-steps diffusion model tailored for instant inpainting, which focuses on maintaining spatial appearance consistency. Experimental results demonstrate that WonderTurbo achieves a remarkable 15xspeedup compared to baseline methods, while preserving excellent spatial consistency and delivering high-quality output.
Learning Hierarchical Line Buffer for Image Processing
Authors: Jiacheng Li; Feiran Li; Daisuke Iso
Venue: ICCV 2025 (main)

Abstract: In recent years, neural networks have achieved significant progress in offline image processing. However, in online scenarios, particularly in on-chip implementations, memory usage emerges as a critical bottleneck due to the limited memory resources of integrated image processors. In this study, we focus on reducing the memory footprint of neural networks for on-chip image processing by optimizing network design for efficient memory utilization. Specifically, we consider a typical scenario in which images outputted from an image sensor are processed sequentially using line buffers in a line-by-line manner. This setting necessitates the modeling of both intra-line and inter-line correlations--capturing dependencies among pixels within a single line group and across different line groups, respectively.To model intra-line correlations, we propose a progressive feature enhancement strategy, where line pixels are processed with expanding strip convolutions in multiple stages.For inter-line correlation modeling, we introduce a hierarchical line buffer formulation, where features extracted from previous lines are incrementally reused and compressed across multiple hierarchical levels.Comprehensive experiments on various image processing tasks, including RAW denoising, Gaussian denoising, and super-resolution, demonstrate that the proposed method achieves a superior trade-off between performance and memory efficiency than previous solutions, e.g., up to 1dB PSNR gain in RAW denoising at one-fifth of peak memory usage.
Looking in the Mirror: A Faithful Counterfactual Explanation Method for Interpreting Deep Image Classification Models
Authors: Townim Chowdhury; Vu Minh Hieu Phan; Kewen Liao; Nanyu Dong; Minh-Son To; Anton van den Hengel; Johan W. Verjans; Zhibin Liao
Venue: ICCV 2025 (main)

Abstract: Counterfactual explanations (CFE) for deep image classifiers aim to reveal how minimal input changes lead to different model decisions, providing critical insights for model interpretation and improvement. However, existing CFE methods often rely on additional image encoders and generative models to create plausible images, neglecting the classifier's own feature space and decision boundaries. As such, they do not explain the intrinsic feature space and decision boundaries learned by the classifier. To address this limitation, we propose Mirror-CFE, a novel method that generates faithful counterfactual explanations by operating directly in the classifier's feature space, treating decision boundaries as mirrors that "reflect" feature representations in the mirror. Mirror-CFE learns a mapping function from feature space to image space while preserving distance relationships, enabling smooth transitions between source images and their counterfactuals. Through extensive experiments on four image datasets, we demonstrate that Mirror-CFE achieves superior performance in validity while maintaining input resemblance compared to state-of-the-art explanation methods. Finally, mirror-CFE provides interpretable visualization of the classifier's decision process by generating step-wise transitions that reveal how features evolve as classification confidence changes.
Inference-Time Diffusion Model Distillation
Authors: Geon Yeong Park; Sang Wan Lee; Jong Chul Ye
Venue: ICCV 2025 (main)

Abstract: Diffusion distillation models effectively accelerate reverse sampling by compressing the process into fewer steps. However, these models still exhibit a performance gap compared to their pre-trained diffusion model counterparts, exacerbated by distribution shifts and accumulated errors during multi-step sampling. To address this, we introduce Distillation++, a novel inference-time distillation framework that reduces this gap by incorporating teacher-guided refinement during sampling. Inspired by recent advances in conditional sampling, our approach recasts student model sampling as a proximal optimization problem with a score distillation sampling loss (SDS). To this end, we integrate distillation optimization during reverse sampling, which can be viewed as teacher guidance that drives student sampling trajectory towards the clean manifold using pre-trained diffusion models. Thus, Distillation++ improves the denoising process in real-time without additional source data or fine-tuning. Distillation++ demonstrates substantial improvements over state-of-the-art distillation baselines, particularly in early sampling stages, positioning itself as a robust guided sampling process crafted for diffusion distillation models.
S3E: Self-Supervised State Estimation for Radar-Inertial System
Authors: Shengpeng Wang; Yulong Xie; Qing Liao; Wei Wang
Venue: ICCV 2025 (main)

Abstract: Millimeter-wave radar for state estimation is gaining significant attention for its affordability and reliability in harsh conditions. Existing localization solutions typically rely on post-processed radar point clouds as landmark points. Nonetheless, the inherent sparsity of radar point clouds, ghost points from multi-path effects, and limited angle resolution in single-chirp radar severely degrade state estimation performance. To address these issues, we propose S^3E, a Self-Supervised State Estimator that employs more richly informative radar signal spectra to bypass sparse points and fuses complementary inertial information to achieve accurate localization. S^3E fully explores the association between exteroceptive radar and proprioceptive inertial sensor to achieve complementary benefits. To deal with limited angle resolution, we introduce a novel cross-fusion technique that enhances spatial structure information by exploiting subtle rotational shift correlations across heterogeneous data. The experimental results demonstrate our method achieves robust and accurate performance without relying on localization ground truth supervision. To the best of our knowledge, this is the first attempt to achieve state estimation by fusing radar spectra and inertial data in a complementary self-supervised manner.
Rethinking Key-frame-based Micro-expression Recognition: A Robust and Accurate Framework Against Key-frame Errors
Authors: Zheyuan Zhang; Weihao Tang; Hong Chen
Venue: ICCV 2025 (main)

Abstract: Micro-expression recognition (MER) is a highly challenging task in affective computing. With the reduced-sized micro-expression (ME) input that contains key information based on key-frame indexes, key-frame-based methods have significantly improved the performance of MER. However, most of these methods focus on improving the performance with relatively accurate key-frame indexes, while ignoring the difficulty of obtaining accurate key-frame indexes and the objective existence of key-frame index errors, which impedes them from moving towards practical applications. In this paper, we propose CausalNet, a novel framework to achieve robust MER facing key-frame index errors while maintaining accurate recognition. To enhance robustness, CausalNet takes the representation of the entire ME sequence as the input. To address the information redundancy brought by the complete ME range input and maintain accurate recognition, first, the Causal Motion Position Learning Module (CMPLM) is proposed to help the model locate the muscle movement areas related to Action Units (AUs), thereby reducing the attention to other redundant areas. Second, the Causal Attention Block (CAB) is proposed to deeply learn the causal relationships between the muscle contraction and relaxation movements in MEs. Empirical experiments have demonstrated that on popular ME benchmarks, the CausalNet has achieved robust MER under different levels of key-frame index noise. Meanwhile, it has surpassed state-of-the-art (SOTA) methods on several standard MER benchmarks when using the provided annotated key-frames. Code is available at https://github.com/tony19980810/CausalNet.
Target Bias Is All You Need: Zero-Shot Debiasing of Vision-Language Models with Bias Corpus
Authors: Taeuk Jang; Hoin Jung; Xiaoqian Wang
Venue: ICCV 2025 (main)

Abstract: Vision-Language Models (VLMs) like CLIP have shown remarkable zero-shot performance by aligning different modalities in the embedding space, enabling diverse applications from image editing to visual question answering (VQA). However, these models often inherit biases from their training data, resulting in performance disparities across specific subpopulations. Traditional debiasing methods for VLMs primarily focus on specific downstream tasks using labeled datasets, which we argue is insufficient given the broad applicability of VLMs. Specifically, these methods struggle with generalizability, transferability, and feasibility due to overfitting, limited task applicability, and regulatory constraints on the use of sensitive data, making them less practical in real-world scenarios. To address these challenges, we propose a novel task-agnostic method for learning debiased image embeddings in VLMs. Our approach does not require expensive annotated datasets or curated prompts for downstream tasks, while still preserving the inherent zero-shot capabilities of these models. Instead, we leverage easily accessible information: 1) a bias text corpus generated by a large language model, and 2) a generic unsupervised vision dataset. Our method disentangles the image embedding into bias and neutral components by applying centered kernel alignment (CKA) regularization to the text-vision representational similarity, using the bias text corpus over the generic vision dataset. Experimental results validate the effectiveness of our approach across multiple tasks, offering a practical and versatile solution to debiasing VLMs.