Expectation-Maximization Style Algorithm for Task-Driven Differentiable Renderer Optimization

Author contact: yan.yang.research [at] proton.me The author is working on minimal code (backpropagation tuning the Gray Scott parameters to match a type of patterns) here: https://github.com/Yan-Yang-bot/bp2renderer. This technical note outlines a conceptual EM-style alternating optimization framework for task-driven differentiable renderer tuning. It proposes a theoretically plausible algorithm and discusses open issues such as training stability and long-range gradient propagation, without formal proof or experimental validation. The document serves as an archival record of a partially developed idea from an unfinished PhD-era project. This technical note presents substantial original ideas and formulations. If you intend to use or extend these ideas in academic publications, please reach out to discuss appropriate citation or collaboration arrangements.