to focus on this local region and thus achieves a more real-
istic generation as shown in Fig. 7.
Deformer: To test the importance of our Fast-SNARF
based deformation module, we compare our model to a
SMPL nearest-neighbor-based deformer (denoted by Ours
w/ SMPL), where points are deformed based on the skinning
weights of their K nearest SMPL vertices in posed space.
As shown in Fig. 8, only our method can deform the skirts
without splitting them. This is due to our articulation mod-
ule being able to derive meaningful deformations for points
far away from the SMPL surface. In contrast, our ablated
baselines, with different choices of K, suffer from discon-
tinuity artifacts as they only provide meaningful deforma-
tion at regions close to the SMPL surface. Similar artifacts
can be observed in EVA3D’s results, which we hypothesize
stem from their part-based model.

4.4. Limitations
AG3D- Learning to Generate 3D Avatars from 2D Image Collections