  Recovering photorealistic and drivable full-body avatars is crucial for
numerous applications, including virtual reality, 3D games, and tele-presence.
Most methods, whether reconstruction or generation, require large numbers of
human motion sequences and corresponding textured meshes. To easily learn a
drivable avatar, a reasonable parametric body model with unified topology is
paramount. However, existing human body datasets either have images or textured
models and lack parametric models which fit clothes well. We propose a new
parametric model SMPLX-Lite-D, which can fit detailed geometry of the scanned
mesh while maintaining stable geometry in the face, hand and foot regions. We
present SMPLX-Lite dataset, the most comprehensive clothing avatar dataset with
multi-view RGB sequences, keypoints annotations, textured scanned meshes, and
textured SMPLX-Lite-D models. With the SMPLX-Lite dataset, we train a
conditional variational autoencoder model that takes human pose and facial
keypoints as input, and generates a photorealistic drivable human avatar.
