7

Fig. 6. Illustration of our texture inference process. The texture network
ﬁrst recovers the color on the whole surface as well as an alpha channel
which is used to blend the predicted color with the observation. In this
way, the input image is maximally utilized and more texture details are
recovered (Please zoom in to compare the texture on the shirt).

Fig. 7. Comparison between the single-image result and the multi-image
result. By adding four more frames (without calibration and synchro-
nization infomation) as input, our method is able to recover the surface
details on the back. In contrast, the back area is over-smoothed in the
single-image setting. Zoom in for better view.

of sparse/dense keypoint detection (although they can be
used as additional constraints).
PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction