around. Finally, for in-the-wild videos, we rely on manual
intervention to correct segmentation errors. These limita-
tions point to a range of interesting avenues for future work.
Conclusion: We have presented HumanNeRF, produc-
ing state-of-the-art results for free-viewpoint renderings of
moving people from monocular video. We demonstrate
high fidelity results for this challenging scenario by care-
fully modeling body pose and motion as well as regular-
izing the optimization process. We hope the result points

in a promising direction toward modeling humans in mo-
tion and, eventually, achieving fully photorealistic, free-
viewpoint rendering of people from casual captures.

Acknowledgement: We thank Marquese Scott for gen-
erously allowing us to feature his inspiring videos in this
work. Special thanks to dear Lulu Chu for her enduring
support. This work was funded by the UW Reality Lab,
Meta, Google, Futurewei, and Amazon.

Supplementary Material
A. Derivation of Motion Bases
HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video