We train our model on a large dataset of 2D images using
adversarial training, leveraging a combination of multiple
discriminators and an Eikonal loss.
Image Discriminator: The ﬁrst discriminator Dimage com-
pares full images generated by our method to real images.

CanonicalGeneratorSRCanonical  Shape Color NormalDeformer + Volume RendererPretrained NormalPredictor(a) View Control

(b) Pose Control

(c) Interpolation

Figure 3: Qualitative Results: 3D Human Generation. We generate 3D human appearance and shape, and render the
resulting 3D representations using different body poses and from different viewpoints. In addition, we show virtual people
generated by interpolating between latent codes. Overall, our synthesized humans exhibit reasonable appearance and geo-
metric quality, remain consistent across different poses and views, and smoothly interpolate when varying the latent code
z.
AG3D- Learning to Generate 3D Avatars from 2D Image Collections