This paper introduces the concept of augmented conversation, which aims to
support co-located in-person conversations via embedded speech-driven
on-the-fly referencing in augmented reality (AR). Today computing technologies
like smartphones allow quick access to a variety of references during the
conversation. However, these tools often create distractions, reducing eye
contact and forcing users to focus their attention on phone screens and
manually enter keywords to access relevant information. In contrast, AR-based
on-the-fly referencing provides relevant visual references in real-time, based
on keywords extracted automatically from the spoken conversation. By embedding
these visual references in AR around the conversation partner, augmented
conversation reduces distraction and friction, allowing users to maintain eye
contact and supporting more natural social interactions. To demonstrate this
concept, we developed \system, a Hololens-based interface that leverages
real-time speech recognition, natural language processing and gaze-based
interactions for on-the-fly embedded visual referencing. In this paper, we
explore the design space of visual referencing for conversations, and describe
our our implementation -- building on seven design guidelines identified
through a user-centered design process. An initial user study confirms that our
system decreases distraction and friction in conversations compared to
smartphone searches, while providing highly useful and relevant information.

介绍了增强对话的概念，通过增强现实中的嵌入式语音驱动的即时引用来支持面对面会话，并降低干扰和摩擦，允许用户保持眼神接触，提供更自然的社交互动。

增强现实中嵌入语音驱动的即时引用的增强型对话

Augmented Conversation with Embedded Speech-Driven On-the-Fly  Referencing in AR

Image editing has been a long-standing challenge in the research community
with its far-reaching impact on numerous applications. Recently, text-driven
methods started to deliver promising results in domains like human faces, but
their applications to more complex domains have been relatively limited. In
this work, we explore the task of fashion style editing, where we aim to
manipulate the fashion style of human imagery using text descriptions.
Specifically, we leverage a generative human prior and achieve fashion style
editing by navigating its learned latent space. We first verify that the
existing text-driven editing methods fall short for our problem due to their
overly simplified guidance signal, and propose two directions to reinforce the
guidance: textual augmentation and visual referencing. Combined with our
empirical findings on the latent space structure, our Fashion Style Editing
framework (FaSE) successfully projects abstract fashion concepts onto human
images and introduces exciting new applications to the field.

该研究通过使用文本描述来操纵人类形象的时尚风格编辑，探索了时尚风格编辑的任务，并提出了两个加强指导信号的方向：文本扩充和视觉参考。通过基于人类先验的生成模型和对潜在空间结构的经验发现，在时尚风格编辑领域取得了令人兴奋的新应用。