pwc-archive/papers-with-abstracts huggingface dataset:
In-2-4D: Inbetweening from Two Single-View Images to 4D Generation
Authors: Sauradip Nag, Daniel Cohen-Or, Hao Zhang, Ali Mahdavi-Amiri
Date: 2025-04-11 00:00:00

We propose a new problem, In-2-4D, for generative 4D (i.e., 3D + motion) inbetweening from a minimalistic input setting: two single-view images capturing an object in two distinct motion states. Given two images representing the start and end states of an object in motion, our goal is to generate and reconstruct the motion in 4D. We utilize a video interpolation model to predict the motion, but large frame-to-frame motions can lead to ambiguous interpretations. To overcome this, we employ a hierarchical approach to identify keyframes that are visually close to the input states and show significant motion, then generate smooth fragments between them. For each fragment, we construct the 3D representation of the keyframe using Gaussian Splatting. The temporal frames within the fragment guide the motion, enabling their transformation into dynamic Gaussians through a deformation field. To improve temporal consistency and refine 3D motion, we expand the self-attention of multi-view diffusion across timesteps and apply rigid transformation regularization. Finally, we merge the independently generated 3D motion segments by interpolating boundary deformation fields and optimizing them to align with the guiding video, ensuring smooth and flicker-free transitions. Through extensive qualitative and quantitiave experiments as well as a user study, we show the effectiveness of our method and its components. The project page is available at https://in-2-4d.github.io/
Single View Garment Reconstruction Using Diffusion Mapping Via Pattern Coordinates
Authors: Ren Li, Cong Cao, Corentin Dumery, Yingxuan You, Hao Li, Pascal Fua
Date: 2025-04-11 00:00:00
Tasks: 3D geometry, Garment Reconstruction, Mixed Reality, Virtual Try-on

Reconstructing 3D clothed humans from images is fundamental to applications like virtual try-on, avatar creation, and mixed reality. While recent advances have enhanced human body recovery, accurate reconstruction of garment geometry -- especially for loose-fitting clothing -- remains an open challenge. We present a novel method for high-fidelity 3D garment reconstruction from single images that bridges 2D and 3D representations. Our approach combines Implicit Sewing Patterns (ISP) with a generative diffusion model to learn rich garment shape priors in a 2D UV space. A key innovation is our mapping model that establishes correspondences between 2D image pixels, UV pattern coordinates, and 3D geometry, enabling joint optimization of both 3D garment meshes and the corresponding 2D patterns by aligning learned priors with image observations. Despite training exclusively on synthetically simulated cloth data, our method generalizes effectively to real-world images, outperforming existing approaches on both tight- and loose-fitting garments. The reconstructed garments maintain physical plausibility while capturing fine geometric details, enabling downstream applications including garment retargeting and texture manipulation.
Designing Child-Friendly AI Interfaces: Six Developmentally-Appropriate Design Insights from Analysing Disney Animation
Authors: Nomisha Kurian
Date: 2025-04-11 00:00:00

To build AI interfaces that children can intuitively understand and use, designers need a design grammar that truly serves children's developmental needs. This paper bridges Artificial Intelligence design for children -- an emerging field still defining its best practices -- and children's animation, a well-established field with decades of experience in engaging young viewers through emotionally resonant, cognitively accessible storytelling. Pairing Piagetian developmental theory with design pattern extraction from 52 works of Disney animation, the paper presents six design insights transferable to child-centred AI interface design: (1) emotional expressiveness and visual clarity, (2) musical and auditory scaffolding, (3) audiovisual synchrony for emotional comfort, (4) sidekick-style personas, (5) support for symbolic play and imaginative exploration, and (6) predictable and scaffolded interaction structures. These strategies -- long refined in Disney animation -- function as multimodal scaffolds for attention, understanding, and emotional attunement, thereby forming a structured design grammar familiar to children and transferable to AI interface design. By reframing cinematic storytelling as design logic for AI, the paper offers heuristics for crafting intuitive AI interfaces that align with children's cognitive stages and emotional needs. The work contributes to design theory by showing how sensory, affective and narrative techniques can inform developmentally attuned AI design for children. Future directions include empirical testing, cultural adaptation, and participatory co-design.
Human strategies for correcting `human-robot' errors during a laundry sorting task
Authors: Pepita Barnard, Maria J Galvez Trigo, Dominic Price, Sue Cobb, Gisela Reyes-Cruz, Gustavo Berumen, David Branson III, Mojtaba A. Khanesar, Mercedes Torres Torres, Michel Valstar
Date: 2025-04-11 00:00:00

Mental models and expectations underlying human-human interaction (HHI) inform human-robot interaction (HRI) with domestic robots. To ease collaborative home tasks by improving domestic robot speech and behaviours for human-robot communication, we designed a study to understand how people communicated when failure occurs. To identify patterns of natural communication, particularly in response to robotic failures, participants instructed Laundrobot to move laundry into baskets using natural language and gestures. Laundrobot either worked error-free, or in one of two error modes. Participants were not advised Laundrobot would be a human actor, nor given information about error modes. Video analysis from 42 participants found speech patterns, included laughter, verbal expressions, and filler words, such as ``oh'' and ``ok'', also, sequences of body movements, including touching one's own face, increased pointing with a static finger, and expressions of surprise. Common strategies deployed when errors occurred, included correcting and teaching, taking responsibility, and displays of frustration. The strength of reaction to errors diminished with exposure, possibly indicating acceptance or resignation. Some used strategies similar to those used to communicate with other technologies, such as smart assistants. An anthropomorphic robot may not be ideally suited to this kind of task. Laundrobot's appearance, morphology, voice, capabilities, and recovery strategies may have impacted how it was perceived. Some participants indicated Laundrobot's actual skills were not aligned with expectations; this made it difficult to know what to expect and how much Laundrobot understood. Expertise, personality, and cultural differences may affect responses, however these were not assessed.
ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use
Authors: Kaixin Li, Ziyang Meng, Hongzhan Lin, Ziyang Luo, Yuchen Tian, Jing Ma, Zhiyong Huang, Tat-Seng Chua
Date: 2025-04-04 00:00:00

Recent advancements in Multi-modal Large Language Models (MLLMs) have led to significant progress in developing GUI agents for general tasks such as web browsing and mobile phone use. However, their application in professional domains remains under-explored. These specialized workflows introduce unique challenges for GUI perception models, including high-resolution displays, smaller target sizes, and complex environments. In this paper, we introduce ScreenSpot-Pro, a new benchmark designed to rigorously evaluate the grounding capabilities of MLLMs in high-resolution professional settings. The benchmark comprises authentic high-resolution images from a variety of professional domains with expert annotations. It spans 23 applications across five industries and three operating systems. Existing GUI grounding models perform poorly on this dataset, with the best model achieving only 18.9%. Our experiments reveal that strategically reducing the search area enhances accuracy. Based on this insight, we propose ScreenSeekeR, a visual search method that utilizes the GUI knowledge of a strong planner to guide a cascaded search, achieving state-of-the-art performance with 48.1% without any additional training. We hope that our benchmark and findings will advance the development of GUI agents for professional applications. Code, data and leaderboard can be found at https://gui-agent.github.io/grounding-leaderboard.
Joint Transmit Waveform and Receive Filter Design for ISAC System with Jamming
Authors: Yuan Shu, Chenhao Qi, Shiwen Mao
Date: 2025-04-11 00:00:00
Tasks: ISAC

In this paper, to suppress jamming in the complex electromagnetic environment, we propose a joint transmit waveform and receive filter design framework for integrated sensing and communications (ISAC). By jointly optimizing the transmit waveform and receive filters, we aim at minimizing the multiuser interference (MUI), subject to the constraints of the target mainlobe, jamming mainlobe and peak sidelobe level of the receive filter output as well as the transmit power of the ISAC base station. We propose two schemes to solve the problem, including joint transmit waveform and matched filter design (JTMD) and joint transmit waveform and mismatched filter design (JTMMD) schemes. For both schemes, we adopt the alternating direction method of multipliers to iteratively optimize the transmit waveform and receive filters, where the number of targets as well as the range and angles of each target can also be estimated. Simulation results show that both the JTMD and JTMMD schemes achieve superior performance in terms of communication MUI and radar detection performance.
High-dimensional Clustering and Signal Recovery under Block Signals
Authors: Wu Su, Yumou Qiu
Date: 2025-04-11 00:00:00
Tasks: Clustering

This paper studies computationally efficient methods and their minimax optimality for high-dimensional clustering and signal recovery under block signal structures. We propose two sets of methods, cross-block feature aggregation PCA (CFA-PCA) and moving average PCA (MA-PCA), designed for sparse and dense block signals, respectively. Both methods adaptively utilize block signal structures, applicable to non-Gaussian data with heterogeneous variances and non-diagonal covariance matrices. Specifically, the CFA method utilizes a block-wise U-statistic to aggregate and select block signals non-parametrically from data with unknown cluster labels. We show that the proposed methods are consistent for both clustering and signal recovery under mild conditions and weaker signal strengths than the existing methods without considering block structures of signals. Furthermore, we derive both statistical and computational minimax lower bounds (SMLB and CMLB) for high-dimensional clustering and signal recovery under block signals, where the CMLBs are restricted to algorithms with polynomial computation complexity. The minimax boundaries partition signals into regions of impossibility and possibility. No algorithm (or no polynomial time algorithm) can achieve consistent clustering or signal recovery if the signals fall into the statistical (or computational) region of impossibility. We show that the proposed CFA-PCA and MA-PCA methods can achieve the CMLBs for the sparse and dense block signal regimes, respectively, indicating the proposed methods are computationally minimax optimal. A tuning parameter selection method is proposed based on post-clustering signal recovery results. Simulation studies are conducted to evaluate the proposed methods. A case study on global temperature change demonstrates their utility in practice.
Programs as Singularities
Authors: Daniel Murfet, Will Troiani
Date: 2025-04-10 00:00:00
Tasks: Bayesian Inference, Learning Theory

We develop a correspondence between the structure of Turing machines and the structure of singularities of real analytic functions, based on connecting the Ehrhard-Regnier derivative from linear logic with the role of geometry in Watanabe's singular learning theory. The correspondence works by embedding ordinary (discrete) Turing machine codes into a family of noisy codes which form a smooth parameter space. On this parameter space we consider a potential function which has Turing machines as critical points. By relating the Taylor series expansion of this potential at such a critical point to combinatorics of error syndromes, we relate the local geometry to internal structure of the Turing machine. The potential in question is the negative log-likelihood for a statistical model, so that the structure of the Turing machine and its associated singularity is further related to Bayesian inference. Two algorithms that produce the same predictive function can nonetheless correspond to singularities with different geometries, which implies that the Bayesian posterior can discriminate between distinct algorithmic implementations, contrary to a purely functional view of inference. In the context of singular learning theory our results point to a more nuanced understanding of Occam's razor and the meaning of simplicity in inductive inference.
Pobogot -- An Open-Hardware Open-Source Low Cost Robot for Swarm Robotics
Authors: Alessia Loi, Loona Macabre, Jérémy Fersula, Keivan Amini, Leo Cazenille, Fabien Caura, Alexandre Guerre, Stéphane Gourichon, Olivier Dauchot, Nicolas Bredeche
Date: 2025-04-11 00:00:00

This paper describes the Pogobot, an open-source and open-hardware platform specifically designed for research involving swarm robotics. Pogobot features vibration-based locomotion, infrared communication, and an array of sensors in a cost-effective package (approx. 250~euros/unit). The platform's modular design, comprehensive API, and extensible architecture facilitate the implementation of swarm intelligence algorithms and distributed online reinforcement learning algorithms. Pogobots offer an accessible alternative to existing platforms while providing advanced capabilities including directional communication between units. More than 200 Pogobots are already being used on a daily basis at Sorbonne Universit\'e and PSL to study self-organizing systems, programmable active matter, discrete reaction-diffusion-advection systems as well as models of social learning and evolution.
Ready, Bid, Go! On-Demand Delivery Using Fleets of Drones with Unknown, Heterogeneous Energy Storage Constraints
Authors: Mohamed S. Talamali, Genki Miyauchi, Thomas Watteyne, Micael S. Couceiro, Roderich Gross
Date: 2025-04-11 00:00:00

Unmanned Aerial Vehicles (UAVs) are expected to transform logistics, reducing delivery time, costs, and emissions. This study addresses an on-demand delivery , in which fleets of UAVs are deployed to fulfil orders that arrive stochastically. Unlike previous work, it considers UAVs with heterogeneous, unknown energy storage capacities and assumes no knowledge of the energy consumption models. We propose a decentralised deployment strategy that combines auction-based task allocation with online learning. Each UAV independently decides whether to bid for orders based on its energy storage charge level, the parcel mass, and delivery distance. Over time, it refines its policy to bid only for orders within its capability. Simulations using realistic UAV energy models reveal that, counter-intuitively, assigning orders to the least confident bidders reduces delivery times and increases the number of successfully fulfilled orders. This strategy is shown to outperform threshold-based methods which require UAVs to exceed specific charge levels at deployment. We propose a variant of the strategy which uses learned policies for forecasting. This enables UAVs with insufficient charge levels to commit to fulfilling orders at specific future times, helping to prioritise early orders. Our work provides new insights into long-term deployment of UAV swarms, highlighting the advantages of decentralised energy-aware decision-making coupled with online learning in real-world dynamic environments.
Graph Based Deep Reinforcement Learning Aided by Transformers for Multi-Agent Cooperation
Authors: Michael Elrod, Niloufar Mehrabi, Rahul Amin, Manveen Kaur, Long Cheng, Jim Martin, Abolfazl Razi
Date: 2025-04-11 00:00:00
Tasks: Deep Reinforcement Learning, Disaster Response, graph construction

Mission planning for a fleet of cooperative autonomous drones in applications that involve serving distributed target points, such as disaster response, environmental monitoring, and surveillance, is challenging, especially under partial observability, limited communication range, and uncertain environments. Traditional path-planning algorithms struggle in these scenarios, particularly when prior information is not available. To address these challenges, we propose a novel framework that integrates Graph Neural Networks (GNNs), Deep Reinforcement Learning (DRL), and transformer-based mechanisms for enhanced multi-agent coordination and collective task execution. Our approach leverages GNNs to model agent-agent and agent-goal interactions through adaptive graph construction, enabling efficient information aggregation and decision-making under constrained communication. A transformer-based message-passing mechanism, augmented with edge-feature-enhanced attention, captures complex interaction patterns, while a Double Deep Q-Network (Double DQN) with prioritized experience replay optimizes agent policies in partially observable environments. This integration is carefully designed to address specific requirements of multi-agent navigation, such as scalability, adaptability, and efficient task execution. Experimental results demonstrate superior performance, with 90% service provisioning and 100% grid coverage (node discovery), while reducing the average steps per episode to 200, compared to 600 for benchmark methods such as particle swarm optimization (PSO), greedy algorithms and DQN.
On the Design of Diffusion-based Neural Speech Codecs
Authors: Pietro Foti, Andreas Brendel
Date: 2025-04-11 00:00:00
Tasks: Audio Generation, Image Generation

Recently, neural speech codecs (NSCs) trained as generative models have shown superior performance compared to conventional codecs at low bitrates. Although most state-of-the-art NSCs are trained as Generative Adversarial Networks (GANs), Diffusion Models (DMs), a recent class of generative models, represent a promising alternative due to their superior performance in image generation relative to GANs. Consequently, DMs have been successfully applied for audio and speech coding among various other audio generation applications. However, the design of diffusion-based NSCs has not yet been explored in a systematic way. We address this by providing a comprehensive analysis of diffusion-based NSCs divided into three contributions. First, we propose a categorization based on the conditioning and output domains of the DM. This simple conceptual framework allows us to define a design space for diffusion-based NSCs and to assign a category to existing approaches in the literature. Second, we systematically investigate unexplored designs by creating and evaluating new diffusion-based NSCs within the conceptual framework. Finally, we compare the proposed models to existing GAN and DM baselines through objective metrics and subjective listening tests.
Palmprint De-Identification Using Diffusion Model for High-Quality and Diverse Synthesis
Authors: Licheng Yan, Bob Zhang, Andrew Beng Jin Teoh, Lu Leng, Shuyi Li, Yuqi Wang, Ziyuan Yang
Date: 2025-04-11 00:00:00
Tasks: De-identification

Palmprint recognition techniques have advanced significantly in recent years, enabling reliable recognition even when palmprints are captured in uncontrolled or challenging environments. However, this strength also introduces new risks, as publicly available palmprint images can be misused by adversaries for malicious activities. Despite this growing concern, research on methods to obscure or anonymize palmprints remains largely unexplored. Thus, it is essential to develop a palmprint de-identification technique capable of removing identity-revealing features while retaining the image's utility and preserving non-sensitive information. In this paper, we propose a training-free framework that utilizes pre-trained diffusion models to generate diverse, high-quality palmprint images that conceal identity features for de-identification purposes. To ensure greater stability and controllability in the synthesis process, we incorporate a semantic-guided embedding fusion alongside a prior interpolation mechanism. We further propose the de-identification ratio, a novel metric for intuitive de-identification assessment. Extensive experiments across multiple palmprint datasets and recognition methods demonstrate that our method effectively conceals identity-related traits with significant diversity across de-identified samples. The de-identified samples preserve high visual fidelity and maintain excellent usability, achieving a balance between de-identification and retaining non-identity information.
Dimension reduction for derivative-informed operator learning: An analysis of approximation errors
Authors: Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
Date: 2025-04-11 00:00:00
Tasks: Dimensionality Reduction, Experimental Design, Operator learning

We study the derivative-informed learning of nonlinear operators between infinite-dimensional separable Hilbert spaces by neural networks. Such operators can arise from the solution of partial differential equations (PDEs), and are used in many simulation-based outer-loop tasks in science and engineering, such as PDE-constrained optimization, Bayesian inverse problems, and optimal experimental design. In these settings, the neural network approximations can be used as surrogate models to accelerate the solution of the outer-loop tasks. However, since outer-loop tasks in infinite dimensions often require knowledge of the underlying geometry, the approximation accuracy of the operator's derivatives can also significantly impact the performance of the surrogate model. Motivated by this, we analyze the approximation errors of neural operators in Sobolev norms over infinite-dimensional Gaussian input measures. We focus on the reduced basis neural operator (RBNO), which uses linear encoders and decoders defined on dominant input/output subspaces spanned by reduced sets of orthonormal bases. To this end, we study two methods for generating the bases; principal component analysis (PCA) and derivative-informed subspaces (DIS), which use the dominant eigenvectors of the covariance of the data or the derivatives as the reduced bases, respectively. We then derive bounds for errors arising from both the dimension reduction and the latent neural network approximation, including the sampling errors associated with the empirical estimation of the PCA/DIS. Our analysis is validated on numerical experiments with elliptic PDEs, where our results show that bases informed by the map (i.e., DIS or output PCA) yield accurate reconstructions and generalization errors for both the operator and its derivatives, while input PCA may underperform unless ranks and training sample sizes are sufficiently large.