Lighting effects such as shadows or reflections are key in making synthetic images realistic and visually appealing. To generate such effects, traditional computer graphics uses a physically-based renderer along with 3D geometry. To compensate for the lack of geometry in 2D Image compositing, recent deep learning-based approaches introduced a pixel height representation to generate soft shadows and reflections. However, the lack of geometry limits the quality of the generated soft shadows and constrain reflections to pure specular ones. We introduce PixHt-Lab, a system leveraging an explicit mapping from pixel height representation to 3D space. Using this mapping, PixHt-Lab reconstructs both the cutout and background geometry and renders realistic, diverse, lighting effects for image compositing. Given a surface with physically-based materials, we can render reflections with varying glossiness. To generate more realistic soft shadows, we further propose to use 3D-aware buffer channels to guide a neural renderer. Both quantitative and qualitative evaluations demonstrate that PixHt-Lab significantly improves soft shadow generation.
翻译:光学效应,如影子或反射,是使合成图像现实和视觉吸引力的关键。为了产生这种效果,传统的计算机图形使用基于物理的成像器和3D几何。为了弥补2D图像合成中缺乏几何学,最近的深层学习方法引入了像素高度表示法,以产生软影子和反射。然而,由于缺乏几何法,产生的软影子的质量受到限制,将反射限制到纯光影。我们引入了PixHt-Lab,这是一个利用像素高度表示法到3D空间的清晰映射的系统。使用这个映射法, PixHt-Lab 重建了剪切图和背景几何方法,并为图像合成产生现实、多样、照明的效果。鉴于有物理材料的表面,我们可以以不同的浮度来进行反射。为了产生更现实的软影子,我们进一步提议使用3D-觉缓冲通道来引导神经成型。两种定量和定性评估都表明, PixHt-Lab 大大改进软影生成。</s>