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Pengembangan dan Evaluasi Agen Virtual dengan Model Generasi Gestur berbasis Aturan Sederhana
Abstract
ABSTRAK
Studi pengembangan gestur sebelumnya telah menyoroti manfaat pendekatan berbasis deep learning untuk menghasilkan gerakan yang mirip manusia, namun, pendekatan tersebut memerlukan dastaset besar dan komputasi intensif. Model milik penulis membedakan antara dialog pendek dan panjang, menghasilkan gerakan spesifik konteks untuk percapakan pendek (salam, perpisahan, persetujuan/tidak setuju) dan gerakan berbasis emosi untuk dialog yang lebih panjang (netral, bahagia, agresif). Penulis membandingkan kinerja sistem dengan ground truth gestures, random gestures, dan idling gestures, menggunakan metrik dari GENEA Challenge. Pendekatan ini bertujuan untuk memberikan alternatif yang lebih efisien dibandingkan model deep learning. Temuan penulis diharapkan dapat berkontribusi pada pengembangan generasi gestur yang menarik meningkatkan pemahaman pengguna dalam interaksi manusia-komputer.
Kata kunci: Generasi Gestur, Interaksi Manusia-Komputer, Ukuran Dialog
ABSTRACT
While previous gesture generation studies have highlighted the benefits of deep learning-based approaches for generating human-like gestures, these often require large datasets and intensive computation. Our model differentiates between short and long dialogues, generating context-specific gestures for short exchanges (e.g., greetings, farewells, agreement/disagreement) and emotionbased gestures for longer dialogues (neutral, happy, aggressive). We compare the system's performance against ground truth gestures, random gestures, and idling gestures using metrics from the GENEA Challenge. This approach aims to provide a more efficient alternative to deep learning models. Our findings are expected to contribute to the development of more engaging, responsive virtual assistants, improving user comprehension in human-computer interaction.
Keywords: Gesture Generation, Human-Computer Interaction, Dialogue Size
Keywords
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DOI: https://doi.org/10.26760/elkomika.v12i4.953
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ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638
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