Our improvements are particularly pronounced when
considering side views. As shown in Fig. 5b, our method
generates sharp and meaningful images also from the side
where EVA3D’s image quality signiﬁcantly degrades. This
is a consequence of EVA3D’s pose-guided sampling strat-
egy. As discussed in their paper, EVA3D had to increase
the dataset’s frontal bias during training to achieve reason-
able geometry and face quality. We hypothesize that this
requirement is due to the limited capacity of the lightweight
part models. As a consequence, EVA3D overﬁts more to

(a) Overall Quality

(b) Novel Views

(c) Loose Clothing

Figure 5: Qualitative Comparison to EVA3D. We show random samples of our method and the SotA method EVA3D [23].
Our method achieves better image and shape quality, degrades more gracefully at side views, and better models loose clothing.

FID ↓
Method
10.93
Ours
w/o normal GAN 11.15
w/o face GAN
11.71

FIDnormal ↓

20.38
32.17
23.96

FIDface ↓
14.79
14.35
20.88
AG3D- Learning to Generate 3D Avatars from 2D Image Collections