The field of computer graphics and physics-based modeling is moving towards more realistic and accurate representations of real-world phenomena. Researchers are focusing on developing innovative methods for inverse design, physically-based modeling, and generative refinement. One of the key directions is the use of diffusion models and generative priors to advance electromagnetic inverse design and human avatar modeling. Another area of interest is the development of novel frameworks for shadow synthesis, caustic design, and surface light source representation. These advancements have the potential to enable rapid exploration of complex metasurface architectures, accelerate the development of next-generation photonic and wireless communication systems, and improve the realism and accuracy of computer-generated images. Notable papers include: PALM, a large-scale dataset for learning multi-subject hand priors, which enables realistic and relightable single-image hand avatar personalization. Diffusion-Based Electromagnetic Inverse Design of Scattering Structured Media, which presents a conditional diffusion model for electromagnetic inverse design that generates structured media geometries directly from target differential scattering cross-section profiles.