  The landscape of computer graphics has undergone significant transformations
with the recent advances of differentiable rendering models. These rendering
models often rely on heuristic designs that may not fully align with the final
rendering objectives. We address this gap by pioneering \textit{evolutive
rendering models}, a methodology where rendering models possess the ability to
evolve and adapt dynamically throughout the rendering process. In particular,
we present a comprehensive learning framework that enables the optimization of
three principal rendering elements, including the gauge transformations, the
ray sampling mechanisms, and the primitive organization. Central to this
framework is the development of differentiable versions of these rendering
elements, allowing for effective gradient backpropagation from the final
rendering objectives. A detailed analysis of gradient characteristics is
performed to facilitate a stable and goal-oriented elements evolution. Our
extensive experiments demonstrate the large potential of evolutive rendering
models for enhancing the rendering performance across various domains,
including static and dynamic scene representations, generative modeling, and
texture mapping.
