5.3. Qualitative evaluation
Dynamic scenes dataset. We provide qualitative compar-
isons between our approach and three prior state-of-the-art
methods [19, 35, 50] on the test views from two datasets in
Fig. 6 and Fig.7. Prior dynamic-NeRF methods have dif-
ﬁculty rendering details of moving objects, as seen in the
excessively blurred dynamic content including the texture
of balloons, human faces, and clothing. In contrast, our ap-
proach synthesizes photo-realistic novel views of both static
and dynamic scene content and which are closest to the
ground truth images.
In-the-wild videos. We show qualitative comparisons on
in-the-wild footage of complex dynamic scenes. We show
comparisons with Dynamic-NeRF based methods in Fig. 8,
and Fig. 9 shows comparisons with point cloud rendering
using depths [80] . Our approach synthesizes photo-realistic
DynIBaR-NeuralDynamicImage-BasedRendering