Abstract

Given a single image of a room and a sentence such as ``a person walks to the black armchair and sits down,'' producing a 3D motion sequence of a virtual person performing the described action is out of reach for existing methods. Scene-conditioned methods need hours of paired (scene, motion, language) capture. Zero-shot methods need a pre-scanned 3D scene. Image-based methods produce only static poses. We propose a training-free pipeline that synthesizes scene-aware human-scene interaction (HSI) motion from a single RGB image and a free-form text prompt, with no HSI-specific training and no pre-scanned 3D scene. The key observation is that every needed capability already exists as a frozen foundation model: the bottleneck is composition, not capability. Our method, ENACT, uses a vision-language model (VLM) for high-level reasoning. The VLM parses the input image and prompt into one structured keyframe per sentence, listing the action verb, the body part, the target object, a six-bin contact yaw, an image-edit prompt, and a foot-grounding flag. The keyframe then drives six frozen specialists: a monocular 3D model for the scene point cloud, an open-vocabulary segmenter for object localization, a single-view mesh reconstructor for object geometry, an instruction-following image editor for a synthetic interaction image, an image-conditioned human-mesh recoverer for pose initialization, and a 2D-grounded 3D contact predictor for object affordance. Per-keyframe contact poses are optimized under a whole-body diffusion prior, and a constraint-conditioned motion diffuser connects successive keyframes into one continuous motion. ENACT achieves the lowest mean and maximum scene penetration and the lowest foot sliding among all baselines on a standard HSI benchmark, despite consuming the smallest input. ENACT also generalizes to synthetic datasets and to in-the-wild phone images, including chained multi-step interactions.

Abstract

Digital platforms increasingly influence the visibility and emotional dissemination of political messages; however, their ability to convert engagement into electoral influence is uneven and contingent upon context. Drawing on computational analysis of Facebook and Instagram during the 2024 Andhra Pradesh Assembly elections, it analyses how platform-specific features, emotive content, and temporal patterns influence voter engagement and political prominence. The paper looks at three important aspects of digital campaigning. First, it looks at posting patterns and campaign intensity, focusing on how frequently political actor's post. Second, it looks how different types of posts, such as those about welfare, leadership, or direct political attacks generate higher levels of interaction among voters. Third, it studies how the campaign narratives change over time, rise and fall of key themes in response to political events and moments.
This study analyzes over 30,000 social media posts posted between June 2023 and May 2024 by the two principal political parties in Andhra Pradesh, i.e., the Telugu Desam Party(TDP) and the Yuvajana Sramika Rythu Congress Party(YSRCP), along with their respective leaders, N. Chandrababu Naidu and Y. S. Jagan Mohan Reddy. After collecting the raw data, the raw data sets were sorted chronologically and split by predefined timelines. The time frame has been divided into three timelines or campaign phases, considering the various political endpoints. Engagement rate was calculated using standardized measures, and posts were filtered to exclude non-political content through a rule-based classification process, allowing the analysis to focus exclusively on substantive political communication. For a better understanding of the voters' perspective, we also conducted sentiment and thematic analysis of the most relevant comments. This allows us to study how voters interpret the campaign messages.
The results indicate that digital campaigning is predominantly centered around particular political events, announcements, controversies, and campaign milestones. Posts with emotional content and personal attacking of the leaders have a high engagement rate. It was also evident that Instagram, which is visual-centric, has a much higher engagement rate than Facebook. Facebook supported longer political messaging and more sustained interaction in comment sections.
By using computational tools to analyze engagement dynamics, the study enables the field of electoral politics to move beyond descriptive explanations of digital campaigning to more detailed examinations of political communication and digitally structured participation. This approach opens new avenues for research on electoral politics by demonstrating that digital engagement can be systematically studied as a political process.

Abstract

Neural Style Transfer (NST) traditionally applies a fixed style to a target image, offering limited control over its degree, progression, or variations. Recent methods have introduced various control mechanisms like spatial attention, entropy regularization, and latent space manipulation. However, these approaches lack interpretable, human-understandable controls for fine-grained stylistic control as intermediate stylization is typically achieved by adjusting content and style losses. Other works explore interpolation across feature layers, but they operate through abstract parameters that require domain expertise and often lack intuitive control, as the relationship between parameters and stylization remains unclear due to the black-box nature of the architecture. In this paper, we introduce CAV Styler, a human-interpretable style transfer approach. We exploit human interpretable concepts such as textures, blurs, and style details to enable fine-grained control over stylization. To our knowledge, ours is among the first in image-to-image style transfer to offer explicit user guidance through semantically meaningful parameters. Our method demonstrates superior controllability compared to existing approaches while maintaining competitive stylization quality, opening new possibilities for intuitive and precise artistic style manipulation.

Abstract

Existing translation models are typically trained on sentence-level and formal text, limiting their ability to capture everyday conversational dialogue phenomena such as informality, speaker interaction, and discourse
coherence.
Most existing Indic translation resources and evaluation benchmarks focus on
sentence-level or formal text, making it difficult to assess translation quality of the dialogue phenomena.
In this work, we introduce textit{BaatCheet}, a multilingual dialogue corpus named after
the Hindi term for textit{conversation} or textit{chitchat},
containing approximately 49,000 dialogues for dialogue translation across five
translation directions.
We fine-tune five open-source LLMs across seven training data configurations
and find that fine-tuning yields substantial gains over zero- and few-shot
baselines.
To comprehensively evaluate dialogue translation quality,
we employ multiple evaluation strategies, including automatic metrics, LLM-as-judge, and human assessments using an SQM-guided Direct Assessment (DA) Protocol.

Abstract

Gaussian Processes (GPs) are widely used as surrogates for black-box functions in sequential decision-making problems such as Bayesian optimization (BO), level set estimation (LSE), and Bayesian active learning (BAL). GP performance critically depends on kernels, and standard kernels can lead to suboptimal decisions under misspecification. To address this, we introduce HACK GPs (Hedge Adaptive Cumulative Kernels), a method that views kernel selection as an online learning with expert advice problem. HACK treats each candidate kernel as a GP "expert" and updates a distribution over experts online using AdaHedge, based on a loss received as a proxy for their ability to fit the function and align with the task objective. We provide two variants of HACK: (i) Mixture of Gaussians (MoG) and (ii) categorical sampling. We establish general guarantees showing that, under a loss-gap condition, the weight concentrates on the best kernel and the resulting acquisition function is close to that of the best expert. Empirically, we observe robust performance across BO, LSE, and BAL compared to standard kernels such as Squared Exponential and Matern-5/2, as well as simple ensemble baselines.

Abstract

A fundamental tension in coded Private Information Retrieval (PIR) lies in balancing the storage overhead placed on servers against the communication and query complexity demanded from the user. The Woodruff-Yekhanin (WY) scheme~cite{WY05} and cite{DGY11, DG15, GKS25} achieve low query complexity by letting servers compute high-order directional derivatives over a fully replicated Reed-Muller codeword, while the Lavauzelle-Nardi (LN) scheme~cite{LN21} minimizes storage to a single encoded curve per server, at the price of maximum query complexity. We establish that a genuine intermediate multiplicity-storage tradeoff exists emph{if and only if} the line-degree parameter $omega geq 2$. For $omega = 1$ (affine-line-based coding), enabling even first-order multiplicity forces complete WY-like codeword replication. For Weighted Reed-Muller (WRM) codes with weight $eta = omega geq 2$, the non-surjectivity of polynomials of degree at least 2 over finite fields creates algebraic "slack" that supports fractional co-location: a server stores $t = s+1$ parallel-class curves, and a Vandermonde inversion lets the user extract the desired derivative while keeping the private query direction hidden. Conditional on a natural Polynomial Image Independence Conjecture (PIIC), whose $s=1$ case is the classical Weil bound, the marginal storage cost of each additional multiplicity unit follows a geometric series that halves at every step. The protocol achieves strict $omega$-privacy: any coalition of up to $omega$ servers learns nothing about the target index.

Abstract

In the modern era, metasurfaces have gained significant attention for their potential in light manipulation, including focusing, bending, blocking, and wavefront control, as well as designing ultra-thin optical components. Metasurfaces comprising highindex dielectric nanostructures have emerged as an inevitable platform for high-performance flat optical devices. Among these, a dichroic beam splitter is a crucial optical component that selectively reflects and transmits light based on the wavelength, enabling precise control over beam routing. In this work, we demonstrate a polarization- and angle-tolerant metasurface-based dichroic beam splitter that operates as a harmonic separator, efficiently separating the fundamental wavelength (1560 nm) and its second harmonic (780 nm) wavelength. Using a 2D array of gallium phosphide (GaP) cylindrical nanostructures, the proposed device exhibits high transmission at 1560 nm while simultaneously achieving near-unity reflection at 780 nm over a bandwidth of approximately 168 nm. This device finds application in fluorescence microscopy, multispectral imaging and advanced laser systems due to its precise spectral selectivity in the near-infrared regime. Beyond these applications, our result highlights a versatile design route to compact dichroic beam splitters that can replace conventional bulky optical devices, with promising prospects for on-chip photonics and portable optical elements.

Abstract

Editing articulated objects within a single image requires precise kinematic control--specifying exactly what moves, along which axis, and by how much. Existing text- and drag-based image editing methods provide only weak or ambiguous control over articulated object structure, often failing to localize the correct movable part or execute precise articulation extents, particularly under occlusion. We propose RigIT, a novel framework that enables precise object articulation by directly "rigging" parts within a 2D image. Our method extracts the underlying geometry of a user-selected part to fit a 3D kinematic proxy, represented as an oriented bounding box (OBB). Users interactively manipulate this explicit 3D handle, which the system projects into a 2D wireframe conditioning signal. A fine-tuned diffusion model then processes this signal, guided by a localized articulation-aware loss, to generate photorealistic edits. Remarkably, despite being trained exclusively on a small synthetic dataset of six categories, RigIT generalizes robustly to real-world, in-the-wild images and unseen object classes. Extensive evaluations on both synthetic and real-world benchmarks demonstrate that our approach significantly outperforms state-of-the-art text and drag-based baselines, achieving over 90% user preference for preserving articulation intent and scene consistency. All code, trained models and dataset will be publicly released at the https://rigit-project.github.io/ .

Abstract

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Abstract

A distributed storage system with graph-based replication consists of a collection of databases and the files they contain. The databases (or servers) are represented as the vertices of a graph, while each file is stored in a distinct pair of servers and is represented by an edge of this graph. Private information retrieval (G-PIR) on such a graph-based storage system involves a client which seeks to retrieve a desired file via a query-response protocol, without leaking the identity of
the desired file index to any database. The goal of G-PIR is to maximize the rate (reciprocal of the total normalized download) under the privacy constraint. Prior work on GPIR has involved perfect information-theoretic privacy (i.e., null leakage). However, if the privacy constraint is relaxed, then PIR protocols could be designed that have even higher rates. We
term such protocols as Graph-based Weak Private Information Retrieval (G-WPIR) protocols and initiate their formal study in this work. We propose a G-WPIR scheme for arbitrary graphs, and identify the trade-offs it achieves between rate and privacy, under two well known leakage metrics: mutual information leakage and maximal leakage. Our protocol employs minimal subpacketization (representing a file-size constraint) and employs a simple probabilistic query realization to obtain the smooth trade-off. We extend this protocol with some modifications to two special classes of graphs, the complete graphs and the complete
bipartite graphs, and identify the corresponding rate-privacy trade-offs achieved.