LAPIG：具有表面适应与风格化的语言引导投影仪图像生成
LAPIG: Language Guided Projector Image Generation with Surface Adaptation and Stylization
- Southwest University（西南大学）
- Stony Brook University（石溪大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
LAPIG提出一种语言引导的投影仪图像生成方法，通过投影表面适应和风格化，利用双网络模拟补偿过程，结合内容与饱和度损失，实现低伪影的表面风格变换。
AI中文摘要：
我们提出了LAPIG，一种具有表面适应与风格化的语言引导投影仪图像生成方法。LAPIG由投影仪-相机系统和一个带纹理的目标投影表面组成。LAPIG以用户文本提示作为输入，旨在利用投影仪变换表面风格。LAPIG的关键挑战在于，由于投影仪的物理亮度限制和表面纹理，观看者感知到的投影在暗区和亮区都可能出现色彩饱和与伪影，即使采用最先进的投影仪补偿技术，观看者仍可能看到明显的表面纹理相关伪影。因此，如何生成既遵循用户指令又显示最小表面伪影的投影图像是一个开放问题。为解决此问题，我们提出了投影表面适应（PSA），能够生成可补偿的表面风格化。我们首先训练两个网络来模拟投影仪补偿和投影-捕获过程，这使我们无需实际投影-捕获即可找到满意的投影图像，并利用梯度下降实现快速收敛。然后，我们设计内容损失和饱和度损失来指导投影图像生成，使得生成的图像在投影时没有清晰可感知的伪影。最后，将生成的图像投影以获得视觉上愉悦的表面风格变形效果。源代码和视频可在项目页面获取：https://Yu-chen-Deng.github.io/LAPIG/。
英文摘要：
We propose LAPIG, a language guided projector image generation method with surface adaptation and stylization. LAPIG consists of a projector-camera system and a target textured projection surface. LAPIG takes the user text prompt as input and aims to transform the surface style using the projector. LAPIG's key challenge is that due to the projector's physical brightness limitation and the surface texture, the viewer's perceived projection may suffer from color saturation and artifacts in both dark and bright regions, such that even with the state-of-the-art projector compensation techniques, the viewer may see clear surface texture-related artifacts. Therefore, how to generate a projector image that follows the user's instruction while also displaying minimum surface artifacts is an open problem. To address this issue, we propose projection surface adaptation (PSA) that can generate compensable surface stylization. We first train two networks to simulate the projector compensation and project-and-capture processes, this allows us to find a satisfactory projector image without real project-and-capture and utilize gradient descent for fast convergence. Then, we design content and saturation losses to guide the projector image generation, such that the generated image shows no clearly perceivable artifacts when projected. Finally, the generated image is projected for visually pleasing surface style morphing effects. The source code and video are available on the project page: https://Yu-chen-Deng.github.io/LAPIG/.
