The field of computer graphics is moving towards more realistic and efficient rendering techniques. Researchers are exploring new methods to simulate complex appearance, such as specular reflections and highlights, and to model the behavior of materials in a more accurate and efficient way. Neural rendering is becoming a key area of focus, with innovations in neural radiance fields, dynamic coefficient decomposition, and neural materials learned by sampling microgeometry. Additionally, there is a growing interest in developing more efficient and compact representations for neural rendering tasks, such as vertex features for neural global illumination. These advancements have the potential to significantly improve the quality and realism of rendered images and scenes. Noteworthy papers include: CoDe-NeRF, which presents a neural rendering framework based on dynamic coefficient decomposition, and PureSample, which introduces a novel neural BRDF representation that allows learning a material's behavior purely by sampling forward random walks on the microgeometry. Vertex Features for Neural Global Illumination is also notable for its generalized formulation of learnable representation for neural rendering tasks involving explicit mesh surfaces.