  Recent advancements in 4D scene reconstruction using neural radiance fields
(NeRF) have demonstrated the ability to represent dynamic scenes from
multi-view videos. However, they fail to reconstruct the dynamic scenes and
struggle to fit even the training views in unsynchronized settings. It happens
because they employ a single latent embedding for a frame while the multi-view
images at the same frame were actually captured at different moments. To
address this limitation, we introduce time offsets for individual
unsynchronized videos and jointly optimize the offsets with NeRF. By design,
our method is applicable for various baselines and improves them with large
margins. Furthermore, finding the offsets naturally works as synchronizing the
videos without manual effort. Experiments are conducted on the common Plenoptic
Video Dataset and a newly built Unsynchronized Dynamic Blender Dataset to
verify the performance of our method. Project page:
https://seoha-kim.github.io/sync-nerf
