GenAI4ELab/papercli-papers huggingface dataset:
LGA-Net: Learning Local and Global Affinities for Sparse Scribble based Image Colorization
Authors: Hongjin Lyu; Bo Li; Paul L. Rosin; Yu-Kun Lai
Venue: ICCV 2025 (main)

Abstract: Image colorization is a typical ill-posed problem. Among various colorization methods, scribble-based methods have a unique advantage that allows users to accurately resolve ambiguities and modify the colors of any objects to suit their specific tastes. However, due to the time-consuming scribble drawing process, users tend to draw sparse scribbles instead of dense and detailed scribbles, which makes it challenging for existing methods, especially for regions with no immediate scribbles. Facing the above problems, this paper proposes a novel colorization algorithm named Local and Global Affinity Net (LGA-Net) that formulates the scribble-based colorization task as an affinity propagation process at both local and global levels. Instead of predicting color values directly, our neural network learns to predict local and global affinity relationships between pixels for a given grayscale input, describing how colors should be propagated, which are independent of the scribbles. Given reliable affinity relationships, the color propagation process is formulated as a maximum a posteriori problem. Both local and global affinities are represented using a weighted graph and enabled by a graph Laplacian regularizer to ensure accurate color propagation. Extensive experiments demonstrate that LGA-Net produces state-of-the-art colorization results when using sparse scribbles.
Benchmarking Burst Super-Resolution for Polarization Images: Noise Dataset and Analysis
Authors: Inseung Hwang; Kiseok Choi; Hyunho Ha; Min H. Kim
Venue: ICCV 2025 (main)

Abstract: Snapshot polarization imaging calculates polarization states from linearly polarized subimages. To achieve this, a polarization camera employs a double Bayer-patterned sensor to capture both color and polarization. It demonstrates low light efficiency and low spatial resolution, resulting in increased noise and compromised polarization measurements. Although burst super-resolution effectively reduces noise and enhances spatial resolution, applying it to polarization imaging poses challenges due to the lack of tailored datasets and reliable ground truth noise statistics. To address these issues, we introduce PolarNS and PolarBurstSR, two innovative datasets developed specifically for polarization imaging. PolarNS provides characterization of polarization noise statistics, facilitating thorough analysis, while PolarBurstSR functions as a benchmark for burst superresolution in polarization images. These datasets, collected under various real-world conditions, enable comprehensive evaluation. Additionally, we present a model for analyzing polarization noise to quantify noise propagation, tested on a large dataset captured in a darkroom environment. As part of our application, we compare the latest burst superresolution models, highlighting the advantages of training tailored to polarization compared to RGB-based methods. This work establishes a benchmark for polarization burst super-resolution and offers critical insights into noise propagation, thereby enhancing polarization image reconstruction. Both code and dataset are publicly available on https://github.com/KAIST-VCLAB/polarns.
JointDiT: Enhancing RGB-Depth Joint Modeling with Diffusion Transformers
Authors: Kwon Byung-Ki; Qi Dai; Lee Hyoseok; Chong Luo; Tae-Hyun Oh
Venue: ICCV 2025 (main)

Abstract: We present JointDiT, a diffusion transformer that models the joint distribution of RGB and depth. By leveraging the architectural benefit and outstanding image prior of the state-of-the-art diffusion transformer, JointDiT not only generates high-fidelity images but also produces geometrically plausible and accurate depth maps. This solid joint distribution modeling is achieved through two simple yet effective techniques that we propose, i.e., adaptive scheduling weights, which depend on the noise levels of each modality, and the unbalanced timestep sampling strategy. With these techniques, we train our model across all noise levels for each modality, enabling JointDiT to naturally handle various combinatorial generation tasks, including joint generation, depth estimation, and depth-conditioned image generation by simply controlling the timesteps of each branch. JointDiT demonstrates outstanding joint generation performance. Furthermore, it achieves comparable results in depth estimation and depth-conditioned image generation, suggesting that joint distribution modeling can serve as a replaceable alternative to conditional generation.
Discretized Gaussian Representation for Tomographic Reconstruction
Authors: Shaokai Wu; Yuxiang Lu; Yapan Guo; Wei Ji; Suizhi Huang; Fengyu Yang; Shalayiding Sirejiding; Qichen He; Jing Tong; Yanbiao Ji; Yue Ding; Hongtao Lu
Venue: ICCV 2025 (main)

Abstract: Computed Tomography (CT) enables detailed cross-sectional imaging but continues to face challenges in balancing reconstruction quality and computational efficiency. While deep learning-based methods have significantly improved image quality and noise reduction, they typically require large-scale training data and intensive computation. Recent advances in scene reconstruction, such as Neural Radiance Fields and 3D Gaussian Splatting, offer alternative perspectives but are not well-suited for direct volumetric CT reconstruction. In this work, we propose Discretized Gaussian Representation (DGR), a novel framework that reconstructs the 3D volume directly using a set of discretized Gaussian functions in an end-to-end manner. To further enhance efficiency, we introduce Fast Volume Reconstruction, a highly parallelized technique that aggregates Gaussian contributions into the voxel grid with minimal overhead. Extensive experiments on both real-world and synthetic datasets demonstrate that DGR achieves superior reconstruction quality and runtime performance across various CT reconstruction scenarios. Our code is publicly available at https://github.com/wskingdom/DGR.
ScoreHOI: Physically Plausible Reconstruction of Human-Object Interaction via Score-Guided Diffusion
Authors: Ao Li; Jinpeng Liu; Yixuan Zhu; Yansong Tang
Venue: ICCV 2025 (main)

Abstract: Joint reconstruction of human-object interaction marks a significant milestone in comprehending the intricate interrelations between humans and their surrounding environment. Nevertheless, previous optimization methods often struggle to achieve physically plausible reconstruction results due to the lack of prior knowledge about human-object interactions. In this paper, we introduce ScoreHOI, an effective diffusion-based optimizer that introduces diffusion priors for the precise recovery of human-object interactions. By harnessing the controllability within score-guided sampling, the diffusion model can reconstruct a conditional distribution of human and object pose given the image observation and object feature. During inference, the ScoreHOI effectively improves the reconstruction results by guiding the denoising process with specific physical constraints. Furthermore, we propose a contact-driven iterative refinement approach to enhance the contact plausibility and improve the reconstruction accuracy. Extensive evaluations on standard benchmarks demonstrate ScoreHOI's superior performance over state-of-the-art methods, highlighting its ability to achieve a precise and robust improvement in joint human-object interaction reconstruction.
CMB-ML: A Cosmic Microwave Background Dataset for the Oldest Possible Computer Vision Task
Authors: James Amato; Yunan Xie; Leonel Medina-Varela; Ammar Aljerwi; Adam McCutcheon; T. Seth Rippentrop; Kristian Gonzalez; Jacques Delabrouille; Mustapha Ishak; Nicholas Ruozzi
Venue: ICCV 2025 (main)

Abstract: The Cosmic Microwave Background (CMB) radiation is a pillar of modern cosmology that gives rise to a better understanding of the fundamental parameters of the universe. While the astrophysics community has developed computational methods to extract this signal from data, these methods have limited scalability, and several groups have proposed the adoption of computer vision based models for CMB signal extraction. However, these diverse models are difficult to compare: the underlying datasets and evaluations are inconsistent and have not been made publicly available. We propose CMB-ML, a dataset and library that integrates dataset creation, model inference, and result evaluation into a pipeline to fill this gap and to make the problem accessible to researchers outside of cosmology. The library and links for data are available at github.com/CMB-ML/cmb-ml.
Deeply Supervised Flow-Based Generative Models
Authors: Inkyu Shin; Chenglin Yang; Liang-Chieh Chen
Venue: ICCV 2025 (main)

Abstract: Flow-based generative models have charted an impressive path across multiple visual generation tasks by adhering to a simple principle: learning velocity representations of a linear interpolant. However, we observe that training velocity solely from the final layer's output under-utilizes the rich inter-layer representations, potentially impeding model convergence. To address this limitation, we introduce DeepFlow, a novel framework that enhances velocity representation through inter-layer communication. DeepFlow partitions transformer layers into balanced branches with deep supervision and inserts a lightweight Velocity Refiner with Acceleration (VeRA) block between adjacent branches, which aligns the intermediate velocity features within transformer blocks. Powered by the improved deep supervision via the internal velocity alignment, DeepFlow converges 8x faster on ImageNet-256x256 with equivalent performance and further reduces FID by 2.6 while halving training time compared to previous flow-based models without a classifier-free guidance. DeepFlow also outperforms baselines in text-to-image generation tasks, as evidenced by evaluations on MS-COCO and zero-shot GenEval. The code will be made publicly available.
Towards Efficient General Feature Prediction in Masked Skeleton Modeling
Authors: Shengkai Sun; Zefan Zhang; Jianfeng Dong; Zhiyong Cheng; Xiaojun Chang; Meng Wang
Venue: ICCV 2025 (main)

Abstract: Recent advances in the masked autoencoder (MAE) paradigm have significantly propelled self-supervised skeleton-based action recognition. However, most existing approaches limit reconstruction targets to raw joint coordinates or their simple variants, resulting in computational redundancy and limited semantic representation. To address this, we propose a novel General Feature Prediction framework (GFP) for efficient mask skeleton modeling. Our key innovation is replacing conventional low-level reconstruction with high-level feature prediction that spans from local motion patterns to global semantic representations. Specifically, we introduce a collaborative learning framework where a lightweight target generation network dynamically produces diversified supervision signals across spatial-temporal hierarchies, avoiding reliance on pre-computed offline features. The framework incorporates constrained optimization to ensure feature diversity while preventing model collapse. Experiments on NTU RGB+D 60, NTU RGB+D 120 and PKU-MMD demonstrate the benefits of our approach: Computational efficiency (with 6.2x faster training than standard masked skeleton modeling methods) and superior representation quality, achieving state-of-the-art performance in various downstream tasks.
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction
Authors: Edgar Sucar; Zihang Lai; Eldar Insafutdinov; Andrea Vedaldi
Venue: ICCV 2025 (main)

Abstract: DUSt3R has recently demonstrated that many tasks in multi-view geometry, including estimating camera intrinsics and extrinsics, reconstructing 3D scenes, and establishing image correspondences, can be reduced to predicting a pair of viewpoint-invariant point maps, i.e., pixel-aligned point clouds defined in a common reference frame. While this formulation is elegant and powerful, it is limited to static scenes. To overcome this limitation, we introduce the concept of Dynamic Point Maps (DPM), which extends standard point maps to support 4D tasks such as motion segmentation, scene flow estimation, 3D object tracking, and 2D correspondence. Our key insight is that, when time is introduced, several possible spatial and temporal references can be used to define the point maps. We identify a minimal subset of these combinations that can be regressed by a network to solve the aforementioned tasks. We train a DPM predictor on a mixture of synthetic and real data and evaluate it across diverse benchmarks, including video depth prediction, dynamic point cloud reconstruction, 3D scene flow, and object pose tracking, achieving state-of-the-art performance.
Language Decoupling with Fine-grained Knowledge Guidance for Referring Multi-object Tracking
Authors: Guangyao Li; Siping Zhuang; Yajun Jian; Yan Yan; Hanzi Wang
Venue: ICCV 2025 (main)

Abstract: Referring Multi-Object Tracking (RMOT) aims to detect and track specific objects based on natural language expressions. Previous methods typically rely on sentence-level vision-language alignment, often failing to exploit fine-grained linguistic cues that are crucial for distinguishing objects with similar characteristics. Notably, these cues play distinct roles at different tracking stages and should be leveraged accordingly to provide more explicit guidance. In this work, we propose DKGTrack, a novel RMOT method that enhances language comprehension for precise object tracking by decoupling language expressions into localized descriptions and motion states. To improve the accuracy of language-guided object identification, we introduce a Static Semantic Enhancement (SSE) module, which enhances region-level vision-language alignment through hierarchical cross-modal feature interaction, providing more discriminative object representations for tracking. Furthermore, we propose a Motion Perception Alignment (MPA) module that explicitly aligns object queries with motion descriptions, enabling accurate object trajectory prediction across frames. Experimental results on multiple RMOT benchmarks demonstrate the effectiveness of our method, which achieves competitive performance in challenging tracking scenarios.
Timestep-Aware Diffusion Model for Extreme Image Rescaling
Authors: Ce Wang; Zhenyu Hu; Wanjie Sun; Zhenzhong Chen
Venue: ICCV 2025 (main)

Abstract: Image rescaling aims to learn the optimal low-resolution (LR) image that can be accurately reconstructed to its original high-resolution (HR) counterpart, providing an efficient image processing and storage method for ultra-high definition media. However, extreme downscaling factors pose significant challenges to the upscaling process due to its highly ill-posed nature, causing existing image rescaling methods to struggle in generating semantically correct structures and perceptual friendly textures. In this work, we propose a novel framework called Timestep-Aware Diffusion Model (TADM) for extreme image rescaling, which performs rescaling operations in the latent space of a pre-trained autoencoder and effectively leverages powerful natural image priors learned by a pre-trained text-to-image diffusion model. Specifically, TADM adopts a pseudo-invertible module to establish the bidirectional mapping between the latent features of the HR image and the target-sized LR image. Then, the rescaled latent features are enhanced by a pre-trained diffusion model to generate more faithful details. Considering the spatially non-uniform degradation caused by the rescaling operation, we propose a novel time-step alignment strategy, which can adaptively allocate the generative capacity of the diffusion model based on the quality of the reconstructed latent features. Extensive experiments demonstrate the superiority of TADM over previous methods in both quantitative and qualitative evaluations. The code will be available at: https://github.com/wwangcece/TADM.
COVTrack: Continuous Open-Vocabulary Tracking via Adaptive Multi-Cue Fusion
Authors: Zekun Qian; Ruize Han; Zhixiang Wang; Junhui Hou; Wei Feng
Venue: ICCV 2025 (main)

Abstract: Open-Vocabulary Multi-Object Tracking (OVMOT) aims to detect and track diverse object categories in videos, including both seen (base) and unseen (novel) categories. Current methods rely on appearance features from generated image pairs or utilize the discontinuous annotations of the video dataset (TAO) for training, primarily due to the lack of available continuous annotated video datasets for OVMOT. This limitation affects their effectiveness, since continuous target trajectories are necessary for robust tracker learning. In this work, we propose the C-TAO dataset, which provides a continuous version of TAO, thereby constructing the first continuous annotated training dataset for OVMOT. This addresses the previous limitations in training data availability. Additionally, we introduce COVTrack, a unified framework that effectively integrates motion and semantic features with appearance features, in which the multi-cue feature aggregation strategy dynamically aggregates and balances these features, based on the confidence estimation from both intra-frame and inter-frame contexts. Our proposed framework significantly improves OVMOT performance, establishing COVTrack as a state-of-the-art solution on OVMOT benchmarks.
Toward Material-Agnostic System Identification from Videos
Authors: Yizhou Zhao; Haoyu Chen; Chunjiang Liu; Zhenyang Li; Charles Herrmann; Junhwa Hur; Yinxiao Li; Ming-Hsuan Yang; Bhiksha Raj; Min Xu
Venue: ICCV 2025 (main)

Abstract: System identification from videos aims to recover object geometry and governing physical laws. Existing methods integrate differentiable rendering with simulation but rely on predefined material priors, limiting their ability to handle unknown ones. We introduce MASIV, the first vision-based framework for material-agnostic system identification. Unlike existing approaches that depend on hand-crafted constitutive laws, MASIV employs learnable neural constitutive models, inferring object dynamics without assuming a scene-specific material prior. However, the absence of full particle state information imposes unique challenges, leading to unstable optimization and physically implausible behaviors. To address this, we introduce dense geometric guidance by reconstructing continuum particle trajectories, providing temporally rich motion constraints beyond sparse visual cues. Comprehensive experiments show that MASIV achieves state-of-the-art performance in geometric accuracy, rendering quality, and generalization ability.
Referring Expression Comprehension for Small Objects
Authors: Kanoko Goto; Takumi Hirose; Mahiro Ukai; Shuhei Kurita; Nakamasa Inoue
Venue: ICCV 2025 (main)

Abstract: Referring expression comprehension (REC) aims to localize the target object described by a natural language expression.Recent advances in vision-language learning have led to significant performance improvements in REC tasks.However, localizing extremely small objects remains a considerable challenge despite its importance in real-world applications such as autonomous driving.To address this issue, we introduce a novel dataset and method for REC targeting small objects.First, we present the small object REC (SOREC) dataset, which consists of 100,000 pairs of referring expressions and corresponding bounding boxes for small objects in driving scenarios.Second, we propose the progressive-iterative zooming adapter (PIZA), an adapter module for parameter-efficient fine-tuning that enables models to progressively zoom in and localize small objects.In a series of experiments, we apply PIZA to GroundingDINO and demonstrate a significant improvement in accuracy on the SOREC dataset.Our dataset, codes and pre-trained models are publicly available on the project page.
Robust 3D-Masked Part-level Editing in 3D Gaussian Splatting with Regularized Score Distillation Sampling
Authors: Hayeon Kim; Ji Ha Jang; Se Young Chun
Venue: ICCV 2025 (main)

Abstract: Recent advances in 3D neural representations and instance-level editing models have enabled the efficient creation of high-quality 3D content. However, achieving precise local 3D edits remains challenging, especially for Gaussian Splatting, due to inconsistent multi-view 2D part segmentations and inherently ambiguous nature of Score Distillation Sampling (SDS) loss. To address these limitations, we propose RoMaP, a novel local 3D Gaussian editing framework that enables precise and drastic part-level modifications. First, we introduce a robust 3D mask generation module with our 3D-Geometry Aware Label Prediction (3D-GALP), which uses spherical harmonics (SH) coefficients to model view-dependent label variations and soft-label property, yielding accurate and consistent part segmentations across viewpoints. Second, we propose a regularized SDS loss that combines the standard SDS loss with additional regularizers. In particular, an L1 anchor loss is introduced via our Scheduled Latent Mixing and Part (SLaMP) editing method, which generates high-quality part-edited 2D images and confines modifications only to the target region while preserving contextual coherence. Additional regularizers, such as Gaussian prior removal, further improve flexibility by allowing changes beyond the existing context, and robust 3D masking prevents unintended edits. Experimental results demonstrate that our RoMaP achieves state-of-the-art local 3D editing on both reconstructed and generated Gaussian scenes and objects qualitatively and quantitatively, making it possible for more robust and flexible part-level 3D Gaussian editing. Code is available at https://janeyeon.github.io/romap.
InstaDrive: Instance-Aware Driving World Models for Realistic and Consistent Video Generation
Authors: Zhuoran Yang; Xi Guo; Chenjing Ding; Chiyu Wang; Wei Wu; Yanyong Zhang
Venue: ICCV 2025 (main)

Abstract: Autonomous driving relies on robust models trained on high-quality, large-scale multi-view driving videos for tasks like perception and planning. While world models offer a cost-effective solution for generating realistic driving videos, they struggle to maintain instance-level temporal consistency and spatial geometric fidelity. To address these challenges, we propose **InstaDrive**, a novel framework that enhances driving video realism through two key advancements: (1) Instance Flow Guider module, which extracts and propagates instance features across frames to enforce temporal consistency, preserving instance identity over time. (2) Spatial Geometric Aligner module, which improves spatial reasoning, ensures precise instance positioning, and explicitly models occlusion hierarchies. By incorporating these instance-aware mechanisms, InstaDrive achieves state-of-the-art video generation quality and enhances downstream autonomous driving tasks on the nuScenes dataset. Additionally, we utilize CARLA's autopilot to procedurally and stochastically simulate rare yet safety-critical driving scenarios across diverse maps and regions, enabling rigorous safety evaluation for autonomous systems.