arXiv subjects

Gal Zeevi

Publications and source records attributed to Gal Zeevi.

2 recordsLinked to original sources

arXiv2026

JUST-DUB-IT: Video Dubbing via Joint Audio-Visual Diffusion

Audio-Visual Foundation Models, which are pretrained to jointly generate sound and visual content, have recently shown an unprecedented ability to model multi-modal generation and editing, opening new opportunities for downstream tasks. Among these tasks, video dubbing could greatly benefit from such priors, yet most existing solutions still rely on complex, task-specific pipelines that struggle in real-world settings. In this work, we introduce a single-model approach that adapts a foundational audio-video diffusion model for video-to-video dubbing via a lightweight LoRA. The LoRA enables the model to condition on an input audio-video while jointly generating translated audio and synchronized facial motion. To train this LoRA, we leverage the generative model itself to synthesize paired multilingual videos of the same speaker. Specifically, we generate multilingual videos with language switches within a single clip, and then inpaint the face and audio in each half to match the language of the other half. By leveraging the rich generative prior of the audio-visual model, our approach preserves speaker identity and lip synchronization while remaining robust to complex motion and real-world dynamics. We demonstrate that our approach produces high-quality dubbed videos with improved visual fidelity, lip synchronization, and robustness compared to existing dubbing pipelines.

cs.GR↗

arXiv2024

Learning Approximation Sets for Exploratory Queries

In data exploration, executing complex non-aggregate queries over large databases can be time-consuming. Our paper introduces a novel approach to address this challenge, focusing on finding an optimized subset of data, referred to as the approximation set, for query execution. The goal is to maximize query result quality while minimizing execution time. We formalize this problem as Approximate Non-Aggregates Query Processing (ANAQP) and establish its NP-completeness. To tackle this, we propose an approximate solution using advanced Reinforcement Learning architecture, termed ASQP-RL. This approach overcomes challenges related to the large action space and the need for generalization beyond a known query workload. Experimental results on two benchmarks demonstrate the superior performance of ASQP-RL, outperforming baselines by 30% in accuracy and achieving efficiency gains of 10-35X. Our research sheds light on the potential of reinforcement learning techniques for advancing data management tasks. Experimental results on two benchmarks show that ASQP-RL significantly outperforms the baselines both in terms of accuracy (30% better) and efficiency (10-35X). This research provides valuable insights into the potential of RL techniques for future advancements in data management tasks.

cs.DB↗