pwc-archive/papers-with-abstracts huggingface dataset:
D3DR: Lighting-Aware Object Insertion in Gaussian Splatting
Authors: Vsevolod Skorokhodov, Nikita Durasov, Pascal Fua
Date: 2025-03-09 00:00:00
Tasks: 3DGS, Denoising, Novel View Synthesis, Object, SSIM

Gaussian Splatting has become a popular technique for various 3D Computer Vision tasks, including novel view synthesis, scene reconstruction, and dynamic scene rendering. However, the challenge of natural-looking object insertion, where the object's appearance seamlessly matches the scene, remains unsolved. In this work, we propose a method, dubbed D3DR, for inserting a 3DGS-parametrized object into 3DGS scenes while correcting its lighting, shadows, and other visual artifacts to ensure consistency, a problem that has not been successfully addressed before. We leverage advances in diffusion models, which, trained on real-world data, implicitly understand correct scene lighting. After inserting the object, we optimize a diffusion-based Delta Denoising Score (DDS)-inspired objective to adjust its 3D Gaussian parameters for proper lighting correction. Utilizing diffusion model personalization techniques to improve optimization quality, our approach ensures seamless object insertion and natural appearance. Finally, we demonstrate the method's effectiveness by comparing it to existing approaches, achieving 0.5 PSNR and 0.15 SSIM improvements in relighting quality.
CoDa-4DGS: Dynamic Gaussian Splatting with Context and Deformation Awareness for Autonomous Driving
Authors: Rui Song, Chenwei Liang, Yan Xia, Walter Zimmer, Hu Cao, Holger Caesar, Andreas Festag, Alois Knoll
Date: 2025-03-09 00:00:00
Tasks: 2D Semantic Segmentation, 4D reconstruction, Autonomous Driving, Novel View Synthesis, Semantic Segmentation

Dynamic scene rendering opens new avenues in autonomous driving by enabling closed-loop simulations with photorealistic data, which is crucial for validating end-to-end algorithms. However, the complex and highly dynamic nature of traffic environments presents significant challenges in accurately rendering these scenes. In this paper, we introduce a novel 4D Gaussian Splatting (4DGS) approach, which incorporates context and temporal deformation awareness to improve dynamic scene rendering. Specifically, we employ a 2D semantic segmentation foundation model to self-supervise the 4D semantic features of Gaussians, ensuring meaningful contextual embedding. Simultaneously, we track the temporal deformation of each Gaussian across adjacent frames. By aggregating and encoding both semantic and temporal deformation features, each Gaussian is equipped with cues for potential deformation compensation within 3D space, facilitating a more precise representation of dynamic scenes. Experimental results show that our method improves 4DGS's ability to capture fine details in dynamic scene rendering for autonomous driving and outperforms other self-supervised methods in 4D reconstruction and novel view synthesis. Furthermore, CoDa-4DGS deforms semantic features with each Gaussian, enabling broader applications.
Color Alignment in Diffusion
Authors: Ka Chun Shum, Binh-Son Hua, Duc Thanh Nguyen, Sai-Kit Yeung
Date: 2025-03-09 00:00:00
Proceeding: CVPR 2025 1
Tasks: Diversity, Image Generation

Diffusion models have shown great promise in synthesizing visually appealing images. However, it remains challenging to condition the synthesis at a fine-grained level, for instance, synthesizing image pixels following some generic color pattern. Existing image synthesis methods often produce contents that fall outside the desired pixel conditions. To address this, we introduce a novel color alignment algorithm that confines the generative process in diffusion models within a given color pattern. Specifically, we project diffusion terms, either imagery samples or latent representations, into a conditional color space to align with the input color distribution. This strategy simplifies the prediction in diffusion models within a color manifold while still allowing plausible structures in generated contents, thus enabling the generation of diverse contents that comply with the target color pattern. Experimental results demonstrate our state-of-the-art performance in conditioning and controlling of color pixels, while maintaining on-par generation quality and diversity in comparison with regular diffusion models.
DiffAtlas: GenAI-fying Atlas Segmentation via Image-Mask Diffusion
Authors: Hantao Zhang, Yuhe Liu, Jiancheng Yang, Weidong Guo, Xinyuan Wang, Pascal Fua
Date: 2025-03-09 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Accurate medical image segmentation is crucial for precise anatomical delineation. Deep learning models like U-Net have shown great success but depend heavily on large datasets and struggle with domain shifts, complex structures, and limited training samples. Recent studies have explored diffusion models for segmentation by iteratively refining masks. However, these methods still retain the conventional image-to-mask mapping, making them highly sensitive to input data, which hampers stability and generalization. In contrast, we introduce DiffAtlas, a novel generative framework that models both images and masks through diffusion during training, effectively ``GenAI-fying'' atlas-based segmentation. During testing, the model is guided to generate a specific target image-mask pair, from which the corresponding mask is obtained. DiffAtlas retains the robustness of the atlas paradigm while overcoming its scalability and domain-specific limitations. Extensive experiments on CT and MRI across same-domain, cross-modality, varying-domain, and different data-scale settings using the MMWHS and TotalSegmentator datasets demonstrate that our approach outperforms existing methods, particularly in limited-data and zero-shot modality segmentation. Code is available at https://github.com/M3DV/DiffAtlas.
Revisiting Invariant Learning for Out-of-Domain Generalization on Multi-Site Mammogram Datasets
Authors: Hung Q. Vo, Samira Zare, Son T. Ly, Lin Wang, Chika F. Ezeana, Xiaohui Yu, Kelvin K. Wong, Stephen T. C. Wong, Hien V. Nguyen
Date: 2025-03-09 00:00:00
Tasks: Cancer Classification, Domain Generalization

Despite significant progress in robust deep learning techniques for mammogram breast cancer classification, their reliability in real-world clinical development settings remains uncertain. The translation of these models to clinical practice faces challenges due to variations in medical centers, imaging protocols, and patient populations. To enhance their robustness, invariant learning methods have been proposed, prioritizing causal factors over misleading features. However, their effectiveness in clinical development and impact on mammogram classification require investigation. This paper reassesses the application of invariant learning for breast cancer risk estimation based on mammograms. Utilizing diverse multi-site public datasets, it represents the first study in this area. The objective is to evaluate invariant learning's benefits in developing robust models. Invariant learning methods, including Invariant Risk Minimization and Variance Risk Extrapolation, are compared quantitatively against Empirical Risk Minimization. Evaluation metrics include accuracy, average precision, and area under the curve. Additionally, interpretability is examined through class activation maps and visualization of learned representations. This research examines the advantages, limitations, and challenges of invariant learning for mammogram classification, guiding future studies to develop generalized methods for breast cancer prediction on whole mammograms in out-of-domain scenarios.
Gaussian RBFNet: Gaussian Radial Basis Functions for Fast and Accurate Representation and Reconstruction of Neural Fields
Authors: Abdelaziz Bouzidi, Hamid Laga, Hazem Wannous
Date: 2025-03-09 00:00:00
Tasks: 3D geometry, 3D Reconstruction, Novel View Synthesis

Neural fields such as DeepSDF and Neural Radiance Fields have recently revolutionized novel-view synthesis and 3D reconstruction from RGB images and videos. However, achieving high-quality representation, reconstruction, and rendering requires deep neural networks, which are slow to train and evaluate. Although several acceleration techniques have been proposed, they often trade off speed for memory. Gaussian splatting-based methods, on the other hand, accelerate the rendering time but remain costly in terms of training speed and memory needed to store the parameters of a large number of Gaussians. In this paper, we introduce a novel neural representation that is fast, both at training and inference times, and lightweight. Our key observation is that the neurons used in traditional MLPs perform simple computations (a dot product followed by ReLU activation) and thus one needs to use either wide and deep MLPs or high-resolution and high-dimensional feature grids to parameterize complex nonlinear functions. We show in this paper that by replacing traditional neurons with Radial Basis Function (RBF) kernels, one can achieve highly accurate representation of 2D (RGB images), 3D (geometry), and 5D (radiance fields) signals with just a single layer of such neurons. The representation is highly parallelizable, operates on low-resolution feature grids, and is compact and memory-efficient. We demonstrate that the proposed novel representation can be trained for 3D geometry representation in less than 15 seconds and for novel view synthesis in less than 15 mins. At runtime, it can synthesize novel views at more than 60 fps without sacrificing quality.
Investigating Image Manifolds of 3D Objects: Learning, Shape Analysis, and Comparisons
Authors: Benjamin Beaudett, Shenyuan Liang, Anuj Srivastava
Date: 2025-03-09 00:00:00

Despite high-dimensionality of images, the sets of images of 3D objects have long been hypothesized to form low-dimensional manifolds. What is the nature of such manifolds? How do they differ across objects and object classes? Answering these questions can provide key insights in explaining and advancing success of machine learning algorithms in computer vision. This paper investigates dual tasks -- learning and analyzing shapes of image manifolds -- by revisiting a classical problem of manifold learning but from a novel geometrical perspective. It uses geometry-preserving transformations to map the pose image manifolds, sets of images formed by rotating 3D objects, to low-dimensional latent spaces. The pose manifolds of different objects in latent spaces are found to be nonlinear, smooth manifolds. The paper then compares shapes of these manifolds for different objects using Kendall's shape analysis, modulo rigid motions and global scaling, and clusters objects according to these shape metrics. Interestingly, pose manifolds for objects from the same classes are frequently clustered together. The geometries of image manifolds can be exploited to simplify vision and image processing tasks, to predict performances, and to provide insights into learning methods.
VideoPhy-2: A Challenging Action-Centric Physical Commonsense Evaluation in Video Generation
Authors: Hritik Bansal, Clark Peng, Yonatan Bitton, Roman Goldenberg, Aditya Grover, Kai-Wei Chang
Date: 2025-03-09 00:00:00
Tasks: Video Generation

Large-scale video generative models, capable of creating realistic videos of diverse visual concepts, are strong candidates for general-purpose physical world simulators. However, their adherence to physical commonsense across real-world actions remains unclear (e.g., playing tennis, backflip). Existing benchmarks suffer from limitations such as limited size, lack of human evaluation, sim-to-real gaps, and absence of fine-grained physical rule analysis. To address this, we introduce VideoPhy-2, an action-centric dataset for evaluating physical commonsense in generated videos. We curate 200 diverse actions and detailed prompts for video synthesis from modern generative models. We perform human evaluation that assesses semantic adherence, physical commonsense, and grounding of physical rules in the generated videos. Our findings reveal major shortcomings, with even the best model achieving only 22% joint performance (i.e., high semantic and physical commonsense adherence) on the hard subset of VideoPhy-2. We find that the models particularly struggle with conservation laws like mass and momentum. Finally, we also train VideoPhy-AutoEval, an automatic evaluator for fast, reliable assessment on our dataset. Overall, VideoPhy-2 serves as a rigorous benchmark, exposing critical gaps in video generative models and guiding future research in physically-grounded video generation. The data and code is available at https://videophy2.github.io/.
Sub-Image Recapture for Multi-View 3D Reconstruction
Authors: Yanwei Wang
Date: 2025-03-10 00:00:00
Tasks: 3D Reconstruction, Multi-View 3D Reconstruction

3D reconstruction of high-resolution target remains a challenge task due to the large memory required from the large input image size. Recently developed learning based algorithms provide promising reconstruction performance than traditional ones, however, they generally require more memory than the traditional algorithms and facing scalability issue. In this paper, we developed a generic approach, sub-image recapture (SIR), to split large image into smaller sub-images and process them individually. As a result of this framework, the existing 3D reconstruction algorithms can be implemented based on sub-image recapture with significantly reduced memory and substantially improved scalability
GUIDE-CoT: Goal-driven and User-Informed Dynamic Estimation for Pedestrian Trajectory using Chain-of-Thought
Authors: Sungsik Kim, Janghyun Baek, Jinkyu Kim, Jaekoo Lee
Date: 2025-03-10 00:00:00
Tasks: Pedestrian Trajectory Prediction, Prediction, Trajectory Prediction

While Large Language Models (LLMs) have recently shown impressive results in reasoning tasks, their application to pedestrian trajectory prediction remains challenging due to two key limitations: insufficient use of visual information and the difficulty of predicting entire trajectories. To address these challenges, we propose Goal-driven and User-Informed Dynamic Estimation for pedestrian trajectory using Chain-of-Thought (GUIDE-CoT). Our approach integrates two innovative modules: (1) a goal-oriented visual prompt, which enhances goal prediction accuracy combining visual prompts with a pretrained visual encoder, and (2) a chain-of-thought (CoT) LLM for trajectory generation, which generates realistic trajectories toward the predicted goal. Moreover, our method introduces controllable trajectory generation, allowing for flexible and user-guided modifications to the predicted paths. Through extensive experiments on the ETH/UCY benchmark datasets, our method achieves state-of-the-art performance, delivering both high accuracy and greater adaptability in pedestrian trajectory prediction. Our code is publicly available at https://github.com/ai-kmu/GUIDE-CoT.
Improving Visual Place Recognition with Sequence-Matching Receptiveness Prediction
Authors: Somayeh Hussaini, Tobias Fischer, Michael Milford
Date: 2025-03-10 00:00:00
Tasks: Visual Place Recognition

In visual place recognition (VPR), filtering and sequence-based matching approaches can improve performance by integrating temporal information across image sequences, especially in challenging conditions. While these methods are commonly applied, their effects on system behavior can be unpredictable and can actually make performance worse in certain situations. In this work, we present a new supervised learning approach that learns to predict the per-frame sequence matching receptiveness (SMR) of VPR techniques, enabling the system to selectively decide when to trust the output of a sequence matching system. The approach is agnostic to the underlying VPR technique. Our approach predicts SMR-and hence significantly improves VPR performance-across a large range of state-of-the-art and classical VPR techniques (namely CosPlace, MixVPR, EigenPlaces, SALAD, AP-GeM, NetVLAD and SAD), and across three benchmark VPR datasets (Nordland, Oxford RobotCar, and SFU-Mountain). We also provide insights into a complementary approach that uses the predictor to replace discarded matches, as well as ablation studies, including an analysis of the interactions between our SMR predictor and the selected sequence length. We will release our code upon acceptance.
MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classification
Authors: Xiangyan Qu, Jing Yu, Jiamin Zhuang, Gaopeng Gou, Gang Xiong, Qi Wu
Date: 2025-03-10 00:00:00
Tasks: Attribute, image-classification, Image Classification, Transfer Learning, Zero-Shot Image Classification, Zero-Shot Learning

Zero-shot learning (ZSL) aims to train a model on seen classes and recognize unseen classes by knowledge transfer through shared auxiliary information. Recent studies reveal that documents from encyclopedias provide helpful auxiliary information. However, existing methods align noisy documents, entangled in visual and non-visual descriptions, with image regions, yet solely depend on implicit learning. These models fail to filter non-visual noise reliably and incorrectly align non-visual words to image regions, which is harmful to knowledge transfer. In this work, we propose a novel multi-attribute document supervision framework to remove noises at both document collection and model learning stages. With the help of large language models, we introduce a novel prompt algorithm that automatically removes non-visual descriptions and enriches less-described documents in multiple attribute views. Our proposed model, MADS, extracts multi-view transferable knowledge with information decoupling and semantic interactions for semantic alignment at local and global levels. Besides, we introduce a model-agnostic focus loss to explicitly enhance attention to visually discriminative information during training, also improving existing methods without additional parameters. With comparable computation costs, MADS consistently outperforms the SOTA by 7.2% and 8.2% on average in three benchmarks for document-based ZSL and GZSL settings, respectively. Moreover, we qualitatively offer interpretable predictions from multiple attribute views.
From Image- to Pixel-level: Label-efficient Hyperspectral Image Reconstruction
Authors: Yihong Leng, Jiaojiao Li, Haitao Xu, Rui Song
Date: 2025-03-10 00:00:00
Tasks: Image Reconstruction, Mamba, Spectral Super-Resolution, Super-Resolution

Current hyperspectral image (HSI) reconstruction methods primarily rely on image-level approaches, which are time-consuming to form abundant high-quality HSIs through imagers. In contrast, spectrometers offer a more efficient alternative by capturing high-fidelity point spectra, enabling pixel-level HSI reconstruction that balances accuracy and label efficiency. To this end, we introduce a pixel-level spectral super-resolution (Pixel-SSR) paradigm that reconstructs HSI from RGB and point spectra. Despite its advantages, Pixel-SSR presents two key challenges: 1) generalizability to novel scenes lacking point spectra, and 2) effective information extraction to promote reconstruction accuracy. To address the first challenge, a Gamma-modeled strategy is investigated to synthesize point spectra based on their intrinsic properties, including nonnegativity, a skewed distribution, and a positive correlation. Furthermore, complementary three-branch prompts from RGB and point spectra are extracted with a Dynamic Prompt Mamba (DyPro-Mamba), which progressively directs the reconstruction with global spatial distributions, edge details, and spectral dependency. Comprehensive evaluations, including horizontal comparisons with leading methods and vertical assessments across unsupervised and image-level supervised paradigms, demonstrate that ours achieves competitive reconstruction accuracy with efficient label consumption.
ActiveInitSplat: How Active Image Selection Helps Gaussian Splatting
Authors: Konstantinos D. Polyzos, Athanasios Bacharis, Saketh Madhuvarasu, Nikos Papanikolopoulos, Tara Javidi
Date: 2025-03-10 00:00:00
Tasks: Computational Efficiency, SSIM

Gaussian splatting (GS) along with its extensions and variants provides outstanding performance in real-time scene rendering while meeting reduced storage demands and computational efficiency. While the selection of 2D images capturing the scene of interest is crucial for the proper initialization and training of GS, hence markedly affecting the rendering performance, prior works rely on passively and typically densely selected 2D images. In contrast, this paper proposes `ActiveInitSplat', a novel framework for active selection of training images for proper initialization and training of GS. ActiveInitSplat relies on density and occupancy criteria of the resultant 3D scene representation from the selected 2D images, to ensure that the latter are captured from diverse viewpoints leading to better scene coverage and that the initialized Gaussian functions are well aligned with the actual 3D structure. Numerical tests on well-known simulated and real environments demonstrate the merits of ActiveInitSplat resulting in significant GS rendering performance improvement over passive GS baselines, in the widely adopted LPIPS, SSIM, and PSNR metrics.
Towards Generalization of Tactile Image Generation: Reference-Free Evaluation in a Leakage-Free Setting
Authors: Cagri Gungor, Derek Eppinger, Adriana Kovashka
Date: 2025-03-10 00:00:00
Tasks: Image Generation

Tactile sensing, which relies on direct physical contact, is critical for human perception and underpins applications in computer vision, robotics, and multimodal learning. Because tactile data is often scarce and costly to acquire, generating synthetic tactile images provides a scalable solution to augment real-world measurements. However, ensuring robust generalization in synthesizing tactile images-capturing subtle, material-specific contact features-remains challenging. We demonstrate that overlapping training and test samples in commonly used datasets inflate performance metrics, obscuring the true generalizability of tactile models. To address this, we propose a leakage-free evaluation protocol coupled with novel, reference-free metrics-TMMD, I-TMMD, CI-TMMD, and D-TMMD-tailored for tactile generation. Moreover, we propose a vision-to-touch generation method that leverages text as an intermediate modality by incorporating concise, material-specific descriptions during training to better capture essential tactile features. Experiments on two popular visuo-tactile datasets, Touch and Go and HCT, show that our approach achieves superior performance and enhanced generalization in a leakage-free setting.
ProBench: Judging Multimodal Foundation Models on Open-ended Multi-domain Expert Tasks
Authors: Yan Yang, Dongxu Li, HaoNing Wu, Bei Chen, Liu Liu, Liyuan Pan, Junnan Li
Date: 2025-03-10 00:00:00

Solving expert-level multimodal tasks is a key milestone towards general intelligence. As the capabilities of multimodal large language models (MLLMs) continue to improve, evaluation of such advanced multimodal intelligence becomes necessary yet challenging. In this work, we introduce ProBench, a benchmark of open-ended user queries that require professional expertise and advanced reasoning. ProBench consists of 4,000 high-quality samples independently submitted by professionals based on their daily productivity demands. It spans across 10 fields and 56 sub-fields, including science, arts, humanities, coding, mathematics, and creative writing. Experimentally, we evaluate and compare 24 latest models using MLLM-as-a-Judge. Our results reveal that although the best open-source models rival the proprietary ones, ProBench presents significant challenges in visual perception, textual understanding, domain knowledge and advanced reasoning, thus providing valuable directions for future multimodal AI research efforts.