通过声学基元建模与驱动人体声场
Modeling and Driving Human Body Soundfields through Acoustic Primitives
AI总结：
提出利用声学基元构建人体声场渲染框架，实现基于姿态和头戴音频的高质量三维空间音频生成，较先前方法表示更紧凑且近场渲染更优。
摘要：
尽管在过去几年中，逼真的三维人体模型的渲染和动画技术已经成熟并达到了令人印象深刻的品质，但与此类全身模型相关的空间音频建模在很大程度上至今仍被忽视。在这项工作中，我们提出了一个框架，能够实现高质量的空间音频生成，可渲染由人体产生的完整三维声场，包括语音、脚步声、手与身体互动以及其他声音。给定以三维人体姿态和头戴式麦克风音频形式呈现的基本视听表示，我们证明了我们能够在三维空间中任意点高效且准确地渲染完整的声学场景。为了实现近场和实时声音渲染，我们借鉴了图形神经渲染中的体积基元思想，并将其迁移到声学领域。与先前方法相比，我们的声学基元使声场表示小了一个数量级，并克服了近场渲染中的缺陷。
英文摘要：
While rendering and animation of photorealistic 3D human body models have matured and reached an impressive quality over the past years, modeling the spatial audio associated with such full body models has been largely ignored so far. In this work, we present a framework that allows for high-quality spatial audio generation, capable of rendering the full 3D soundfield generated by a human body, including speech, footsteps, hand-body interactions, and others. Given a basic audio-visual representation of the body in form of 3D body pose and audio from a head-mounted microphone, we demonstrate that we can render the full acoustic scene at any point in 3D space efficiently and accurately. To enable near-field and realtime rendering of sound, we borrow the idea of volumetric primitives from graphical neural rendering and transfer them into the acoustic domain. Our acoustic primitives result in an order of magnitude smaller soundfield representations and overcome deficiencies in near-field rendering compared to previous approaches.
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