arXiv subjects

Aditya Patel

Publications and source records attributed to Aditya Patel.

4 recordsLinked to original sources

arXiv2026

CoolPath Tool: A Thermal Comfort Path Planning Tool for Urban Mobility

Extreme heat poses a growing challenge for active transportation in cities where conventional weather reporting (e.g. limited air temperature measurement for the whole city) fails to capture the large microclimate variations that pedestrians and cyclists experience. We present a novel walking and biking route planning tool (''CoolPath Tool'') that selects paths based on thermal comfort using the Universal Thermal Climate Index (UTCI) rather than just distance or travel time. This system combines high-resolution thermal modeling with real-time route mapping. We generate city-scale UTCI maps using GPU version of Solar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, to account for urban features (buildings, trees,) and weather conditions. The urban features are pre-mapped using satellite data products and the routes are the roadways. For any given origin and destination, our tool calculates the average UTCI along each possible route and recommends the ''coolest'' route, i.e. the path with the lowest heat stress (often the most shaded or otherwise thermally comfortable), while still being reasonably direct. We demonstrate this in a case study for Austin, Texas. The approach identifies routes that significantly reduce pedestrians' heat exposure (often recommending routes with a much larger proportion of shade). Such thermally-informed route planning has important public well-being, and economic implications: by helping people avoid dangerous heat hotspots and sun-exposed areas, it can reduce the risk of heat-related illness and make walking or biking a safer choice even on hot days. This tool is part of the Austin Digital Twin efforts developed as part the UT-City CoLab needs. The work while demonstrated for Austin, TX is scalable, and transferrable to other cities globally.

physics.soc-ph↗

arXiv2025

Scaling Zero-Shot Reference-to-Video Generation

Reference-to-video (R2V) generation aims to synthesize videos that align with a text prompt while preserving the subject identity from reference images. However, current R2V methods are hindered by the reliance on explicit reference image-video-text triplets, whose construction is highly expensive and difficult to scale. We bypass this bottleneck by introducing Saber, a scalable zero-shot framework that requires no explicit R2V data. Trained exclusively on video-text pairs, Saber employs a masked training strategy and a tailored attention-based model design to learn identity-consistent and reference-aware representations. Mask augmentation techniques are further integrated to mitigate copy-paste artifacts common in reference-to-video generation. Moreover, Saber demonstrates remarkable generalization capabilities across a varying number of references and achieves superior performance on the OpenS2V-Eval benchmark compared to methods trained with R2V data.

cs.CV↗

arXiv2025

TUNA: Taming Unified Visual Representations for Native Unified Multimodal Models

Unified multimodal models (UMMs) aim to jointly perform multimodal understanding and generation within a single framework. We present TUNA, a native UMM that builds a unified continuous visual representation by cascading a VAE encoder with a representation encoder. This unified representation space allows end-to-end processing of images and videos for both understanding and generation tasks. Compared to prior UMMs with decoupled representations, TUNA's unified visual space avoids representation format mismatches introduced by separate encoders, outperforming decoupled alternatives in both understanding and generation. Moreover, we observe that stronger pretrained representation encoders consistently yield better performance across all multimodal tasks, highlighting the importance of the representation encoder. Finally, in this unified setting, jointly training on both understanding and generation data allows the two tasks to benefit from each other rather than interfere. Our extensive experiments on multimodal understanding and generation benchmarks show that TUNA achieves state-of-the-art results in image and video understanding, image and video generation, and image editing, demonstrating the effectiveness and scalability of its unified representation design.

cs.CV↗

arXiv2024

Learning Flow Fields in Attention for Controllable Person Image Generation

Controllable person image generation aims to generate a person image conditioned on reference images, allowing precise control over the person's appearance or pose. However, prior methods often distort fine-grained textural details from the reference image, despite achieving high overall image quality. We attribute these distortions to inadequate attention to corresponding regions in the reference image. To address this, we thereby propose learning flow fields in attention (Leffa), which explicitly guides the target query to attend to the correct reference key in the attention layer during training. Specifically, it is realized via a regularization loss on top of the attention map within a diffusion-based baseline. Our extensive experiments show that Leffa achieves state-of-the-art performance in controlling appearance (virtual try-on) and pose (pose transfer), significantly reducing fine-grained detail distortion while maintaining high image quality. Additionally, we show that our loss is model-agnostic and can be used to improve the performance of other diffusion models.

cs.CV↗