Current video diffusion models can generate recognizable camera-effect appearances but do not reliably follow specified physical parameters such as field of view, aperture, and readout time, partly because real dynamic videos with diverse and accurate camera-effect annotations are scarce. Physics-based simulation provides exact labels but suffers from a sim-to-real gap. Reconstructing real dynamic scenes offers an alternative, but Dynamic NeRF methods render too slowly, whereas rasterization-based Dynamic Gaussian Splatting cannot model the light transport required by these effects. We propose 4D Gaussian Ray Tracing (4D-GRT), a two-stage data-generation pipeline that reconstructs dynamic scenes as 4D Gaussians from multi-view pinhole videos and re-renders them with physically based camera models under specified parameters. On CamVFX-4D, our controlled benchmark of eight dynamic indoor scenes under four camera effects, 4D-GRT achieves up to 3.4 dB higher PSNR and 6 times faster rendering than MSTH. Finally, we demonstrate two downstream applications: fine-tuning a video generation model on our data to improve camera-effect control and extending our pipeline to monocular input. Project page: https://shigon255.github.io/4DGRT-project-page/.

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