reconstruction losses

(cid:17)
(cid:125)

(cid:16)Lmatch + L2D-cyc
(cid:124)

(cid:123)(cid:122)

feature registration losses

(cid:17)
(cid:125)

Reconstruction losses are similar to those in existing dif-
ferentiable rendering pipelines [29, 67]. Besides color loss
Lrgb and silhouette loss Lsil, we further compute flow recon-
struction losses LOF by comparing the rendered F defined
in Eq. 7 with the observed 2D optical flow ˆF computed by
an off-the-shelf flow network:
Lrgb =

(cid:13)(cid:13)c(xt) − ˆc(xt)(cid:13)(cid:13)2
(cid:13)(cid:13)(cid:13)F(cid:0)xt, t → t′(cid:1) − ˆF(cid:0)xt, t → t′(cid:1)(cid:13)(cid:13)(cid:13)2
(cid:88)

(cid:13)(cid:13)o(xt) − ˆs(xt)(cid:13)(cid:13)2

, Lsil =

(cid:88)

(cid:88)

LOF =

(16)

xt

xt

,

,

xt,(t,t′)

where ˆc and ˆs are observed color and silhouette. Addi-
tionally, we define feature matching losses to enforce 3D
points predicted via canonical embedding ˆX∗(xt) (Eq. 15)
to match the prediction from backward warping (Eq. 5):
BANMo- Building Animatable 3D Neural Models from Many Casual Videos