基于快速神经风格迁移的虚拟微笑预览连贯渲染

Valentin Vasiliu, Gábor Sörös
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引用次数: 3

摘要

增强现实中的连贯渲染涉及到虚拟内容与真实内容无缝融合的合成。不幸的是,在虚拟渲染过程中捕获或建模每个真实方面通常是不可行的,或者成本太高。我们提出了一种后处理方法,提高了在牙科虚拟试戴应用程序中渲染叠加的外观。受艺术风格迁移研究的启发,我们将原始帧和默认渲染帧结合在一个自编码器神经网络中,以获得更自然的输出。具体来说,我们将原始框架作为样式应用于渲染框架作为内容,并对每一对新框架重复此过程。我们的方法只需要一个向前传递,我们的浅架构确保快速执行,我们的内部反馈循环固有地强制时间一致性。
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Coherent Rendering of Virtual Smile Previews with Fast Neural Style Transfer
Coherent rendering in augmented reality deals with synthesizing virtual content that seamlessly blends in with the real content. Unfortunately, capturing or modeling every real aspect in the virtual rendering process is often unfeasible or too expensive. We present a post-processing method that improves the look of rendered overlays in a dental virtual try-on application. We combine the original frame and the default rendered frame in an autoencoder neural network in order to obtain a more natural output, inspired by artistic style transfer research. Specifically, we apply the original frame as style on the rendered frame as content, repeating the process with each new pair of frames. Our method requires only a single forward pass, our shallow architecture ensures fast execution, and our internal feedback loop inherently enforces temporal consistency.
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