8/31/2023 0 Comments Translucent objects![]() Our results suggest StyleGAN2-ADA has the potential to learn a representation of translucent appearances similar to that of humans, and it is useful to explore its latent space to disentangle the material-related features. Second, observers can discriminate a range of translucency from the generated images from opaque to transparent, similar to that of the real photographs. We have talked about how light reflects (bounces) of some objects and is refracted (bent) by other objects. As light travels, you know it hits many objects. We find observers can correctly judge the vast majority of real photographs (73% of the real images are correctly judged by at least 9 observers) but they make substantial mistakes for the generated images (60% fake images are falsely judged to be real by at least 2 observers and 7% fake images are falsely judged by more than 5 observers). HOW LIGHT INTERACTS WITH OTHER OBJECTS SOL 5.3C Transparent, Translucent, and Opaque IT ALL DEPENDS. Scattering makes the problem of estimating shape of translucent objects difficult, because reflective. Ten observers sequentially completed both experiments. Translucent objects strongly scatter incident light. In Experiment 2, observers rated the level of translucency of the material for the same 500 images on a 5-point-scale. Transparent objects outside the Depth range will be rendered pitch black if viewed through the transparent object that the Depth is set for. In Experiment 1, we sampled 250 images from the real photographs and another 250 from the generated images, and asked observers to judge whether the soap in the image is real or fake after a brief 300ms presentation. Translucent Objects: The objects that allow some light rays but not all to. We then conducted human psychophysical experiments to measure the perceived quality of the model’s output. Examples of transparent objects are air, water, diamond, clear glass, and lense. The images were trained with StyleGAN2-ADA, a generative network that is trained on limited data with data-augmentation. ![]() We created a dataset of 3000 photographs of soaps with a variety of translucent appearances. Here, we investigate to what extent Generative Adversarial Networks (GANs) trained on unlabeled photographs of translucent materials can produce perceptually realistic images and achieve diverse appearances, without knowing about the physical parameters. Non-uniform Density: If there is non-uniform distribution of matter in a material, irregular refraction and transmission can occur in them. However, it is challenging to acquire accurate physical parameters to render materials with realistic appearance. Plastic What Makes a Material Translucent Translucency can arise due to the following properties of the material: Crystallographic Defects: Crystal structure defects can give rise to light scattering. Most previous studies used rendered images as stimuli. ![]() Translucent materials have a wide variety of appearances due to variability of scattering, geometry, and lighting condition. ![]()
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