
Distortions introduced during the reproduction of digital images can lead to substantial changes in their color composition. The motivations for altering images range from practical purposes, such as image compression and color quantization to reduce file size, to more aesthetic applications like style transfer using generative AI. In this work, we investigate how the reproduction of color images affects material appearance, in particular, the perception of gloss and translucency. We applied different image quality distortions to natural images of glossy and translucent objects. Additionally, we Ghiblified them – a recent viral social media phenomenon of mimicking the Japanese anime style using generative AI style transfer. Afterward, we conducted a series of user studies to evaluate the fidelity of gloss and translucency reproduction. The experimental results represent how the reproductions are perceived by image quality metrics and open up a new direction for material appearance studies.

The integration of deterministic protocol-specified chatbots with generative AI bridges the gap between precise, protocol-driven logic and conversational flexibility. This paper introduces MachineQuizzing, a chatbot designed to enhance learning in machine learning through gamified quizzes and real-time explanations. Leveraging platforms like Dialogflow for structured logic and Gemini for generative capabilities, the chatbot demonstrates how the integration of these technologies can enhance conversational experience.

Regression-based radiance field reconstruction strategies, such as neural radiance fields (NeRFs) and, physics-based, 3D Gaussian splatting (3DGS), have gained popularity in novel view synthesis and scene representation. These methods parameterize a high-dimensional function that represents a radiance field, from a low-dimensional camera input. However, these problems are ill-posed and struggle to represent high (spatial) frequency data; manifesting as reconstruction artifacts when estimating high frequency details such as small hairs, fibers, or reflective surfaces. Here we show that classical spherical sampling around a target, often referred to as sampling a bounded scene, inhomogeneously samples the targets Fourier domain, resulting in spectral bias in the collected samples. We generalize the ill-posed problems of view-synthesis and scene representation as expressions of projection tomograpy and explore the upper-bound reconstruction limits of regression-based and integration-based strategies. We introduce a physics-based sampling strategy that we directly apply to 3DGS, and demonstrate high fidelity 3D anisotropic radiance field reconstructions with reconstruction PSNR scores as high as 44.04 dB and SSIM scores of 0.99, following the same metric analysis as defined in Mip-NeRF360.