pwc-archive/papers-with-abstracts huggingface dataset:
Micro-macro Gaussian Splatting with Enhanced Scalability for Unconstrained Scene Reconstruction
Authors: Yihui Li, Chengxin Lv, Hongyu Yang, Di Huang
Date: 2025-06-16 00:00:00
Tasks: 3D Reconstruction, Diversity

Reconstructing 3D scenes from unconstrained image collections poses significant challenges due to variations in appearance. In this paper, we propose Scalable Micro-macro Wavelet-based Gaussian Splatting (SMW-GS), a novel method that enhances 3D reconstruction across diverse scales by decomposing scene representations into global, refined, and intrinsic components. SMW-GS incorporates the following innovations: Micro-macro Projection, which enables Gaussian points to sample multi-scale details with improved diversity; and Wavelet-based Sampling, which refines feature representations using frequency-domain information to better capture complex scene appearances. To achieve scalability, we further propose a large-scale scene promotion strategy, which optimally assigns camera views to scene partitions by maximizing their contributions to Gaussian points, achieving consistent and high-quality reconstructions even in expansive environments. Extensive experiments demonstrate that SMW-GS significantly outperforms existing methods in both reconstruction quality and scalability, particularly excelling in large-scale urban environments with challenging illumination variations. Project is available at https://github.com/Kidleyh/SMW-GS.
Multiview Geometric Regularization of Gaussian Splatting for Accurate Radiance Fields
Authors: Jungeon Kim, Geonsoo Park, Seungyong Lee
Date: 2025-06-16 00:00:00

Recent methods, such as 2D Gaussian Splatting and Gaussian Opacity Fields, have aimed to address the geometric inaccuracies of 3D Gaussian Splatting while retaining its superior rendering quality. However, these approaches still struggle to reconstruct smooth and reliable geometry, particularly in scenes with significant color variation across viewpoints, due to their per-point appearance modeling and single-view optimization constraints. In this paper, we propose an effective multiview geometric regularization strategy that integrates multiview stereo (MVS) depth, RGB, and normal constraints into Gaussian Splatting initialization and optimization. Our key insight is the complementary relationship between MVS-derived depth points and Gaussian Splatting-optimized positions: MVS robustly estimates geometry in regions of high color variation through local patch-based matching and epipolar constraints, whereas Gaussian Splatting provides more reliable and less noisy depth estimates near object boundaries and regions with lower color variation. To leverage this insight, we introduce a median depth-based multiview relative depth loss with uncertainty estimation, effectively integrating MVS depth information into Gaussian Splatting optimization. We also propose an MVS-guided Gaussian Splatting initialization to avoid Gaussians falling into suboptimal positions. Extensive experiments validate that our approach successfully combines these strengths, enhancing both geometric accuracy and rendering quality across diverse indoor and outdoor scenes.
Abstract, Align, Predict: Zero-Shot Stance Detection via Cognitive Inductive Reasoning
Authors: Jun Ma, Fuqiang Niu, Dong Li, Jinzhou Cao, Genan Dai, BoWen Zhang
Date: 2025-06-16 00:00:00
Tasks: Stance Detection, Zero-Shot Stance Detection

Zero-shot stance detection (ZSSD) aims to identify the stance of text toward previously unseen targets, a setting where conventional supervised models often fail due to reliance on labeled data and shallow lexical cues. Inspired by human cognitive reasoning, we propose the Cognitive Inductive Reasoning Framework (CIRF), which abstracts transferable reasoning schemas from unlabeled text and encodes them as concept-level logic. To integrate these schemas with input arguments, we introduce a Schema-Enhanced Graph Kernel Model (SEGKM) that dynamically aligns local and global reasoning structures. Experiments on SemEval-2016, VAST, and COVID-19-Stance benchmarks show that CIRF establishes new state-of-the-art results, outperforming strong ZSSD baselines by 1.0, 4.5, and 3.3 percentage points in macro-F1, respectively, and achieving comparable accuracy with 70\% fewer labeled examples. We will release the full code upon publication.
Amplitude equations for stored spatially heterogeneous states
Authors: Akke Mats Houben
Date: 2025-06-16 00:00:00

Coupled evolution equations for the amplitudes of heterogeneous states (memories) stored in the connectivity of distributed systems with distance-constrained non-local interactions are derived. The resulting system of coupled amplitude equations describes the spatio-temporal dynamics of the pattern amplitudes. It is shown that these types of distributed systems perform pattern completion and selection, and that the distance restricted connections afford spatio-temporal memory pattern dynamics taking the form of patterning fronts.
Computational lower bounds in latent models: clustering, sparse-clustering, biclustering
Authors: Bertrand Even, Christophe Giraud, Nicolas Verzelen
Date: 2025-06-16 00:00:00
Tasks: Clustering

In many high-dimensional problems, like sparse-PCA, planted clique, or clustering, the best known algorithms with polynomial time complexity fail to reach the statistical performance provably achievable by algorithms free of computational constraints. This observation has given rise to the conjecture of the existence, for some problems, of gaps -- so called statistical-computational gaps -- between the best possible statistical performance achievable without computational constraints, and the best performance achievable with poly-time algorithms. A powerful approach to assess the best performance achievable in poly-time is to investigate the best performance achievable by polynomials with low-degree. We build on the seminal paper of Schramm and Wein (2022) and propose a new scheme to derive lower bounds on the performance of low-degree polynomials in some latent space models. By better leveraging the latent structures, we obtain new and sharper results, with simplified proofs. We then instantiate our scheme to provide computational lower bounds for the problems of clustering, sparse clustering, and biclustering. We also prove matching upper-bounds and some additional statistical results, in order to provide a comprehensive description of the statistical-computational gaps occurring in these three problems.
Audio-Visual Driven Compression for Low-Bitrate Talking Head Videos
Authors: Riku Takahashi, Ryugo Morita, Jinjia Zhou
Date: 2025-06-16 00:00:00
Tasks: Neural Rendering, Video Compression

Talking head video compression has advanced with neural rendering and keypoint-based methods, but challenges remain, especially at low bit rates, including handling large head movements, suboptimal lip synchronization, and distorted facial reconstructions. To address these problems, we propose a novel audio-visual driven video codec that integrates compact 3D motion features and audio signals. This approach robustly models significant head rotations and aligns lip movements with speech, improving both compression efficiency and reconstruction quality. Experiments on the CelebV-HQ dataset show that our method reduces bitrate by 22% compared to VVC and by 8.5% over state-of-the-art learning-based codec. Furthermore, it provides superior lip-sync accuracy and visual fidelity at comparable bitrates, highlighting its effectiveness in bandwidth-constrained scenarios.
Quantitative Comparison of Fine-Tuning Techniques for Pretrained Latent Diffusion Models in the Generation of Unseen SAR Image Concepts
Authors: Solène Debuysère, Nicolas Trouvé, Nathan Letheule, Olivier Lévêque, Elise Colin
Date: 2025-06-16 00:00:00
Tasks: Image Generation

This work investigates the adaptation of large pre-trained latent diffusion models to a radically new imaging domain: Synthetic Aperture Radar (SAR). While these generative models, originally trained on natural images, demonstrate impressive capabilities in text-to-image synthesis, they are not natively adapted to represent SAR data, which involves different physics, statistical distributions, and visual characteristics. Using a sizeable SAR dataset (on the order of 100,000 to 1 million images), we address the fundamental question of fine-tuning such models for this unseen modality. We explore and compare multiple fine-tuning strategies, including full model fine-tuning and parameter-efficient approaches like Low-Rank Adaptation (LoRA), focusing separately on the UNet diffusion backbone and the text encoder components. To evaluate generative quality, we combine several metrics: statistical distance from real SAR distributions, textural similarity via GLCM descriptors, and semantic alignment assessed with a CLIP model fine-tuned on SAR data. Our results show that a hybrid tuning strategy yields the best performance: full fine-tuning of the UNet is better at capturing low-level SAR-specific patterns, while LoRA-based partial tuning of the text encoder, combined with embedding learning of the <SAR> token, suffices to preserve prompt alignment. This work provides a methodical strategy for adapting foundation models to unconventional imaging modalities beyond natural image domains.
MAMMA: Markerless & Automatic Multi-Person Motion Action Capture
Authors: Hanz Cuevas-Velasquez, Anastasios Yiannakidis, Soyong Shin, Giorgio Becherini, Markus Höschle, Joachim Tesch, Taylor Obersat, Tsvetelina Alexiadis, Michael J. Black
Date: 2025-06-16 00:00:00
Tasks: Markerless Motion Capture

We present MAMMA, a markerless motion-capture pipeline that accurately recovers SMPL-X parameters from multi-view video of two-person interaction sequences. Traditional motion-capture systems rely on physical markers. Although they offer high accuracy, their requirements of specialized hardware, manual marker placement, and extensive post-processing make them costly and time-consuming. Recent learning-based methods attempt to overcome these limitations, but most are designed for single-person capture, rely on sparse keypoints, or struggle with occlusions and physical interactions. In this work, we introduce a method that predicts dense 2D surface landmarks conditioned on segmentation masks, enabling person-specific correspondence estimation even under heavy occlusion. We employ a novel architecture that exploits learnable queries for each landmark. We demonstrate that our approach can handle complex person--person interaction and offers greater accuracy than existing methods. To train our network, we construct a large, synthetic multi-view dataset combining human motions from diverse sources, including extreme poses, hand motions, and close interactions. Our dataset yields high-variability synthetic sequences with rich body contact and occlusion, and includes SMPL-X ground-truth annotations with dense 2D landmarks. The result is a system capable of capturing human motion without the need for markers. Our approach offers competitive reconstruction quality compared to commercial marker-based motion-capture solutions, without the extensive manual cleanup. Finally, we address the absence of common benchmarks for dense-landmark prediction and markerless motion capture by introducing two evaluation settings built from real multi-view sequences. We will release our dataset, benchmark, method, training code, and pre-trained model weights for research purposes.
A large-scale, physically-based synthetic dataset for satellite pose estimation
Authors: Szabolcs Velkei, Csaba Goldschmidt, Károly Vass
Date: 2025-06-15 00:00:00
Tasks: Benchmarking, Dataset Generation, Deep Learning, Pose Estimation, Semantic Segmentation

The Deep Learning Visual Space Simulation System (DLVS3) introduces a novel synthetic dataset generator and a simulation pipeline specifically designed for training and testing satellite pose estimation solutions. This work introduces the DLVS3-HST-V1 dataset, which focuses on the Hubble Space Telescope (HST) as a complex, articulated target. The dataset is generated using advanced real-time and offline rendering technologies, integrating high-fidelity 3D models, dynamic lighting (including secondary sources like Earth reflection), and physically accurate material properties. The pipeline supports the creation of large-scale, richly annotated image sets with ground-truth 6-DoF pose and keypoint data, semantic segmentation, depth, and normal maps. This enables the training and benchmarking of deep learning-based pose estimation solutions under realistic, diverse, and challenging visual conditions. The paper details the dataset generation process, the simulation architecture, and the integration with deep learning frameworks, and positions DLVS3 as a significant step toward closing the domain gap for autonomous spacecraft operations in proximity and servicing missions.
Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining
Authors: Yongqian Peng, Zhouran Zhang, Longhui Zhang, Fengyuan Zhao, Yahao Li, Yicong Ye, Shuxin Bai
Date: 2025-06-14 00:00:00
Tasks: Contrastive Learning, Language Modeling, Language Modelling, Property Prediction

Machine learning has revolutionized materials design, yet predicting complex properties like alloy ductility remains challenging due to the influence of processing conditions and microstructural features that resist quantification through traditional reductionist approaches. Here, we present an innovative information fusion architecture that integrates domain-specific texts from materials science literature with quantitative physical descriptors to overcome these limitations. Our framework employs MatSciBERT for advanced textual comprehension and incorporates contrastive learning to automatically extract implicit knowledge regarding processing parameters and microstructural characteristics. Through rigorous ablation studies and comparative experiments, the model demonstrates superior performance, achieving coefficient of determination (R2) values of 0.849 and 0.680 on titanium alloy validation set and refractory multi-principal-element alloy test set. This systematic approach provides a holistic framework for property prediction in complex material systems where quantitative descriptors are incomplete and establishes a foundation for knowledge-guided materials design and informatics-driven materials discovery.
Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders
Authors: Paulin de Schoulepnikoff, Gorka Muñoz-Gil, Hendrik Poulsen Nautrup, Hans J. Briegel
Date: 2025-06-13 00:00:00
Tasks: Interpretable Machine Learning, Representation Learning, scientific discovery

Interpretable machine learning is rapidly becoming a crucial tool for scientific discovery. Among existing approaches, variational autoencoders (VAEs) have shown promise in extracting the hidden physical features of some input data, with no supervision nor prior knowledge of the system at study. Yet, the ability of VAEs to create meaningful, interpretable representations relies on their accurate approximation of the underlying probability distribution of their input. When dealing with quantum data, VAEs must hence account for its intrinsic randomness and complex correlations. While VAEs have been previously applied to quantum data, they have often neglected its probabilistic nature, hindering the extraction of meaningful physical descriptors. Here, we demonstrate that two key modifications enable VAEs to learn physically meaningful latent representations: a decoder capable of faithfully reproduce quantum states and a probabilistic loss tailored to this task. Using benchmark quantum spin models, we identify regimes where standard methods fail while the representations learned by our approach remain meaningful and interpretable. Applied to experimental data from Rydberg atom arrays, the model autonomously uncovers the phase structure without access to prior labels, Hamiltonian details, or knowledge of relevant order parameters, highlighting its potential as an unsupervised and interpretable tool for the study of quantum systems.
Spectra-to-Structure and Structure-to-Spectra Inference Across the Periodic Table
Authors: Yufeng Wang, Peiyao Wang, Lu Ma, Yuewei Lin, Qun Liu, Haibin Ling
Date: 2025-06-13 00:00:00

X-ray Absorption Spectroscopy (XAS) is a powerful technique for probing local atomic environments, yet its interpretation remains limited by the need for expert-driven analysis, computationally expensive simulations, and element-specific heuristics. Recent advances in machine learning have shown promise for accelerating XAS interpretation, but many existing models are narrowly focused on specific elements, edge types, or spectral regimes. In this work, we present XAStruct, a learning framework capable of both predicting XAS spectra from crystal structures and inferring local structural descriptors from XAS input. XAStruct is trained on a large-scale dataset spanning over 70 elements across the periodic table, enabling generalization to a wide variety of chemistries and bonding environments. The model includes the first machine learning approach for predicting neighbor atom types directly from XAS spectra, as well as a unified regression model for mean nearest-neighbor distance that requires no element-specific tuning. While we explored integrating the two pipelines into a single end-to-end model, empirical results showed performance degradation. As a result, the two tasks were trained independently to ensure optimal accuracy and task-specific performance. By combining deep neural networks for complex structure-property mappings with efficient baseline models for simpler tasks, XAStruct offers a scalable and extensible solution for data-driven XAS analysis and local structure inference. The source code will be released upon paper acceptance.
Vid-CamEdit: Video Camera Trajectory Editing with Generative Rendering from Estimated Geometry
Authors: Junyoung Seo, Jisang Han, Jaewoo Jung, Siyoon Jin, Joungbin Lee, Takuya Narihira, Kazumi Fukuda, Takashi Shibuya, Donghoon Ahn, Shoukang Hu, Seungryong Kim, Yuki Mitsufuji
Date: 2025-06-16 00:00:00
Tasks: Novel View Synthesis

We introduce Vid-CamEdit, a novel framework for video camera trajectory editing, enabling the re-synthesis of monocular videos along user-defined camera paths. This task is challenging due to its ill-posed nature and the limited multi-view video data for training. Traditional reconstruction methods struggle with extreme trajectory changes, and existing generative models for dynamic novel view synthesis cannot handle in-the-wild videos. Our approach consists of two steps: estimating temporally consistent geometry, and generative rendering guided by this geometry. By integrating geometric priors, the generative model focuses on synthesizing realistic details where the estimated geometry is uncertain. We eliminate the need for extensive 4D training data through a factorized fine-tuning framework that separately trains spatial and temporal components using multi-view image and video data. Our method outperforms baselines in producing plausible videos from novel camera trajectories, especially in extreme extrapolation scenarios on real-world footage.
UltraVideo: High-Quality UHD Video Dataset with Comprehensive Captions
Authors: Zhucun Xue, Jiangning Zhang, Teng Hu, Haoyang He, Yinan Chen, Yuxuan Cai, Yabiao Wang, Chengjie Wang, Yong liu, Xiangtai Li, DaCheng Tao
Date: 2025-06-16 00:00:00
Tasks: 4k, 8k, Video Generation

The quality of the video dataset (image quality, resolution, and fine-grained caption) greatly influences the performance of the video generation model. The growing demand for video applications sets higher requirements for high-quality video generation models. For example, the generation of movie-level Ultra-High Definition (UHD) videos and the creation of 4K short video content. However, the existing public datasets cannot support related research and applications. In this paper, we first propose a high-quality open-sourced UHD-4K (22.4\% of which are 8K) text-to-video dataset named UltraVideo, which contains a wide range of topics (more than 100 kinds), and each video has 9 structured captions with one summarized caption (average of 824 words). Specifically, we carefully design a highly automated curation process with four stages to obtain the final high-quality dataset: \textit{i)} collection of diverse and high-quality video clips. \textit{ii)} statistical data filtering. \textit{iii)} model-based data purification. \textit{iv)} generation of comprehensive, structured captions. In addition, we expand Wan to UltraWan-1K/-4K, which can natively generate high-quality 1K/4K videos with more consistent text controllability, demonstrating the effectiveness of our data curation.We believe that this work can make a significant contribution to future research on UHD video generation. UltraVideo dataset and UltraWan models are available at https://xzc-zju.github.io/projects/UltraVideo.
MultiViT2: A Data-augmented Multimodal Neuroimaging Prediction Framework via Latent Diffusion Model
Authors: Bi Yuda, Jia Sihan, Gao Yutong, Abrol Anees, Fu Zening, Calhoun Vince
Date: 2025-06-16 00:00:00
Tasks: Data Augmentation, Prediction

Multimodal medical imaging integrates diverse data types, such as structural and functional neuroimaging, to provide complementary insights that enhance deep learning predictions and improve outcomes. This study focuses on a neuroimaging prediction framework based on both structural and functional neuroimaging data. We propose a next-generation prediction model, \textbf{MultiViT2}, which combines a pretrained representative learning base model with a vision transformer backbone for prediction output. Additionally, we developed a data augmentation module based on the latent diffusion model that enriches input data by generating augmented neuroimaging samples, thereby enhancing predictive performance through reduced overfitting and improved generalizability. We show that MultiViT2 significantly outperforms the first-generation model in schizophrenia classification accuracy and demonstrates strong scalability and portability.
Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models
Authors: Gregory Bellchambers
Date: 2025-06-16 00:00:00
Tasks: Colorization, Denoising, Image Restoration, Super-Resolution

The success of diffusion models has driven interest in performing conditional sampling via training-free guidance of the denoising process to solve image restoration and other inverse problems. A popular class of methods, based on Diffusion Posterior Sampling (DPS), attempts to approximate the intractable posterior score function directly. In this work, we present a novel expression for the exact posterior score for purely denoising tasks that is tractable in terms of the unconditional score function. We leverage this result to analyze the time-dependent error in the DPS score for denoising tasks and compute step sizes on the fly to minimize the error at each time step. We demonstrate that these step sizes are transferable to related inverse problems such as colorization, random inpainting, and super resolution. Despite its simplicity, this approach is competitive with state-of-the-art techniques and enables sampling with fewer time steps than DPS.