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About

Taras Kucherenko is a researcher specializing in human-computer interaction, social robotics, and AI-driven nonverbal behavior generation, with a particular focus on the automatic synthesis of co-speech gestures. His most influential work centers on developing deep learning frameworks that translate speech signals into naturalistic gesture motion for virtual agents and humanoid robots. His 2019 paper "Analyzing Input and Output Representations for Speech-Driven Gesture Generation" (152 citations) established foundational methods for data-driven gesture synthesis, while his comprehensive 2023 review (93 citations) has become an essential reference consolidating the rapidly evolving landscape of co-speech gesture generation research. Beyond gestures, Kucherenko has made meaningful contributions to motion capture data processing, notably through neural network approaches to reconstructing missing marker data—critical infrastructure for animation and robotics pipelines. His work on learning nonverbal behavior from real-world video data and benchmarking beat gesture generation reflects a strong commitment to bridging machine learning with believable embodied interaction. His early interdisciplinary work also touched on social robotics for dementia detection, demonstrating broad humanitarian ambitions. Across his career, Kucherenko's research has meaningfully advanced the goal of creating socially intelligent, gesture-capable artificial agents.