can be synthesised via a 2D grid deformation. Despite their compactness, point clouds are not popular for realistic and high-quality 3D geometry generation due to their lack of an underlying smooth structure. **Image-based** methods have also been proposed, such as multi-view [@arsalan2017synthesizing] and flat domain mappings such as UV maps [@moschoglou20193dfacegan; @ben2018multi], however they are computationally demanding, require pre- and post-processing steps and usually produce undesirable artefacts. It is also worth mentioning the recently introduced **implicit-surface** based approaches [@mescheder2018occupancy; @chen2018learning; @park2019deepsdf], that can yield accurate results, though with the disadvantage of slow inference (dense sampling of the 3D space followed by marching cubes).\
**Morphable models:** In the case of deformable shapes, such as faces, bodies, hands , where a fixed topology can be obtained by establishing dense correspondences with a template, the most popular methods are still statistical models given their simplicity. For **Faces**, the baseline is the PCA-based 3D Morphable Model (3DMM) [@vetterMM]. The Large Scale Face Model (LSFM) [@booth2018large] was proposed for facial identity and made publicly available, [@cao2014facewarehouse; @FLAME:SiggraphAsia2017] were proposed for facial expression, while for the entire head a large scale model was proposed in [@ploumpis2019combining]. For **Body & Hand**, the most well known models are 