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.

翻译：随着可微分渲染模型的最新进展，计算机图形学领域经历了重大变革。现有渲染模型通常依赖于启发式设计，这些设计可能无法完全契合最终渲染目标。为弥补这一差距，我们开创性地提出\textit{演化式渲染模型}——一种使渲染模型在整个渲染过程中具备动态演化与自适应能力的方法论。具体而言，我们提出了一个综合性学习框架，能够对三个核心渲染要素进行优化，包括规范变换、光线采样机制以及图元组织。该框架的核心在于开发这些渲染要素的可微分版本，从而实现从最终渲染目标出发的有效梯度反向传播。我们通过对梯度特性的详细分析，促进了稳定且目标导向的要素演化。大量实验表明，演化式渲染模型在静态与动态场景表示、生成建模以及纹理映射等多个领域具有显著提升渲染性能的巨大潜力。