  Monte Carlo rendering algorithms often utilize correlations between pixels to
improve efficiency and enhance image quality. For real-time applications in
particular, repeated reservoir resampling offers a powerful framework to reuse
samples both spatially in an image and temporally across multiple frames. While
such techniques achieve equal-error up to 100 times faster for real-time direct
lighting and global illumination, they are still far from optimal. For
instance, unchecked spatiotemporal resampling often introduces noticeable
correlation artifacts, while reservoirs holding more than one sample suffer
from impoverishment in the form of duplicate samples. We demonstrate how
interleaving Markov Chain Monte Carlo (MCMC) mutations with reservoir
resampling helps alleviate these issues, especially in scenes with glossy
materials and difficult-to-sample lighting. Moreover, our approach does not
introduce any bias, and in practice we find considerable improvement in image
quality with just a single mutation per reservoir sample in each frame.
