QPoser: Quantized Explicit Pose Prior Modeling for Controllable Pose
  Generation
Yumeng Li Yaoxiang Ding Zhong Ren Kun Zhou
Explicit pose prior models compress human poses into latent representations
for using in pose-related downstream tasks. A desirable explicit pose prior
model should satisfy three desirable abilities: 1) correctness, i.e. ensuring
to generate physically possible poses; 2) expressiveness, i.e. ensuring to
preserve details in generation; 3) controllability, meaning that generation
from reference poses and explicit instructions should be convenient. Existing
explicit pose prior models fail to achieve all of three properties, in special
controllability. To break this situation, we propose QPoser, a highly
controllable explicit pose prior model which guarantees correctness and
expressiveness. In QPoser, a multi-head vector quantized autoencoder (MS-VQVAE)
is proposed for obtaining expressive and distributed pose representations.
Furthermore, a global-local feature integration mechanism (GLIF-AE) is utilized
to disentangle the latent representation and integrate full-body information
into local-joint features. Experimental results show that QPoser significantly
outperforms state-of-the-art approaches in representing expressive and correct
poses, meanwhile is easily to be used for detailed conditional generation from
reference poses and prompting instructions.