将路径追踪与生成模型统一为连续采样过程，实现物理合理且可训练的图像渐进生成。
Bridging Rendering and Generative Modeling with Monte Carlo Transport Scheduling
- 把路径追踪视为连续采样驱动的传输过程，用采样方差定义时间坐标。
- 在可控基准上实现稳定图像精炼，支持任意步长中断与恢复。
- 为冻结的生成采样器提供物理先验，适合渲染与生成联合任务。
蒙特卡洛渲染与现代生成模型均将不确定状态转化为结构化图像，但通常被视为独立过程。本文提出蒙特卡洛传输调度框架，将渐进式路径追踪视为连续采样驱动的传输过程。关键观察是：渲染过程本身已生成物理有效的状态——嵌套蒙特卡洛估计追踪一条精炼轨迹，其自然时间坐标由采样方差决定。该视角催生一个连续训练框架，从真实渲染终点学习而非合成插值，保留蒙特卡洛估计的统计结构，同时支持任意步长的神经精炼。我们在一个控制性渲染基准上评估，该基准能分离传输难度与场景上下文，结果表明：该框架实现稳定图像精炼，支持任意状态间的连续停止，并可作为冻结生成采样器的物理先验。这些结果表明，蒙特卡洛采样可作为渲染与生成的共同连续时间基础，既提供物理状态，也提供学习图像传输的监督信号。
原文摘要 · Abstract (English)
Monte Carlo rendering and modern generative models both transform uncertain states into structured images, yet they are usually studied as separate processes. We introduce Monte Carlo Transport Scheduling, a framework that treats progressive path tracing as a continuous sampling-driven transport process. Our key observation is that the renderer already produces physically valid states along this process: nested Monte Carlo estimates trace a refinement trajectory whose natural time coordinate follows from sampling variance. This view leads to a continuous training framework that learns from real render endpoints rather than synthetic interpolants, preserving the statistical structure of Monte Carlo estimation while enabling arbitrary-step neural refinement. We evaluate the framework on a controlled rendering benchmark designed to separate transport difficulty from scene context, and show that it yields stable render refinement, supports continuous stopping between rendering states, and transfers as a physical prior for frozen generative samplers. These results suggest a common continuous-time substrate for rendering and generation, where Monte Carlo sampling provides both the physical states and the supervision for learning image transport.
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