Recent advances in generative adversarial networks. (GANs) have provided potential solutions for photo- realistic human image synthesis.

This paper proposes a multi-factor conditioned method dubbed BodyGAN. Specifically, given a source image, our Body-GAN aims at capturing the characteristics of ...

The proposed BodyGAN can achieve the fine-grained dis-entanglement of pose, body shape, and appearance, and enable the explicit and effective control of ...

This paper proposes a multi-factor conditioned method dubbed BodyGAN. Specifically, given a source image, our Body-GAN aims at capturing the characteristics of ...

The BodyGAN is composed of three major components: the pose encoding branch and the appearance encoding branch are responsible for generating the condition map-.

Transfer-based methods transfers a source appearance (or garment [14,52]) into a new pose [2,33,39,47,52,56], motion [4] or scene [57].

PyTorch implementation for controllable person image synthesis. Controllable Person Image Synthesis with Attribute-Decomposed GAN.

Oct 5, 2022 · Bibliographic details on BodyGAN: General-purpose Controllable Neural Human Body Generation.

The proposed BodyGAN consists of a pose encoding branch, an appearance encoding branch, and a generator. The pose encoding branch and the appearance ...

Application of convolutional neural network on early human embryo ... Bodygan: General-purpose controllable neural human body generation. C Yang, H ...