用户用语言生成3D世界时，空间表达难、迭代受阻，需更直观的交互设计。
From Prompts to Worlds: How Users Iterate, Explore, and Make Sense of AI-Generated 3D Environments
- 用户能说清主题氛围，却难描述布局尺度，反映语言到空间的天然局限。
- 沉浸感短暂出现但无法持续，因输出与预期匹配时才产生临时代入感。
- 修改失败主因是反馈不透明、操作成本高，而非用户能力不足。
文本生成3D环境系统可从自然语言提示创建可导航的空间，但不同于图文生成，其评估需依赖对空间连贯性、尺度和可通行性的具身探索。我们首次对商用文本转3D平台进行实证研究，结合口头报告、行为观察及经验证的可用性、沉浸感与参与度量表。发现：第一，表达不对称：用户易传达语义意图（如主题、氛围），但难以指定空间结构（如布局、尺度），反映的是语言到空间的固有局限而非技能问题。第二，情景式沉浸感：当输出符合预期时会产生短暂沉浸，但不会积累为持续的场所幻觉。第三，结构化迭代失效：优化失败源于交互障碍——发现性差、反馈模糊、时间成本高，而非用户自身限制。三者共同构成循环，导致空间错配持续存在，引发间歇性沉浸与持续意义建构。我们重新将文本转3D交互视为协商式意义建构，而非线性提示过程，主张有效系统需融合输入模态、透明反馈与低成本迭代。
原文摘要 · Abstract (English)
Text-to-3D generative AI systems create navigable environments from natural language prompts, but unlike text-to-image generation, evaluation requires embodied exploration of spatial coherence, scale, and navigability. We present the first empirical study of a commercial text-to-3D platform, combining think-aloud protocols, behavioral observation, and validated measures of usability, presence, and engagement. We report three findings. First, asymmetric expressibility: users readily convey semantic intent (themes, atmosphere) but struggle to specify spatial structure (layout, scale), reflecting a language-to-space limitation rather than a skill deficit. Second, episodic presence: immersion arises when expectations align with outputs but does not accumulate into sustained place illusion. Third, structural iteration breakdowns: refinement fails due to interaction barriers - poor discoverability, opaque feedback, and high temporal costs - rather than user limitations. Together, these dynamics form a reinforcing cycle in which spatial mismatches persist, producing episodic presence and ongoing sensemaking. We reframe text-to-3D interaction as negotiated meaning-making rather than linear prompting, and argue that effective systems require hybrid input modalities, transparent feedback, and low-cost iteration.
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