2 research outputs found
Adversarial Synthesis of Human Pose from Text
This work introduces the novel task of human pose synthesis from text. In
order to solve this task, we propose a model that is based on a conditional
generative adversarial network. It is designed to generate 2D human poses
conditioned on human-written text descriptions. The model is trained and
evaluated using the COCO dataset, which consists of images capturing complex
everyday scenes. We show through qualitative and quantitative results that the
model is capable of synthesizing plausible poses matching the given text,
indicating it is possible to generate poses that are consistent with the given
semantic features, especially for actions with distinctive poses. We also show
that the model outperforms a vanilla GAN
Towards Better Adversarial Synthesis of Human Images from Text
This paper proposes an approach that generates multiple 3D human meshes from
text. The human shapes are represented by 3D meshes based on the SMPL model.
The model's performance is evaluated on the COCO dataset, which contains
challenging human shapes and intricate interactions between individuals. The
model is able to capture the dynamics of the scene and the interactions between
individuals based on text. We further show how using such a shape as input to
image synthesis frameworks helps to constrain the network to synthesize humans
with realistic human shapes