Abstract We present Neural Articulated Radiance Field (NARF), a novel deformable 3D
representation for articulated objects learned from images. While recent advances in 3D …

Deep person generation has attracted extensive research attention due to its wide
applications in virtual agents, video conferencing, online shopping, and art/movie …

This paper presents a simple method for" do as I do" motion transfer: given a source video of
a person dancing, we can transfer that performance to a novel (amateur) target after only a …

Abstract We present Multi-Garment Network (MGN), a method to predict body shape and
clothing, layered on top of the SMPL model from a few frames (1-8) of a video. Several …

In this paper, we present TailorNet, a neural model which predicts clothing deformation in
3D as a function of three factors: pose, shape and style (garment geometry), while retaining …

This paper proposes a new generative adversarial network to the problem of pose transfer,
ie, transferring the pose of a given person to a target one. The generator of the network …

Person re-identification (reID) is an important task that requires to retrieve a person's images
from an image dataset, given one image of the person of interest. For learning robust person …

We tackle the human motion imitation, appearance transfer, and novel view synthesis within
a unified framework, which means that the model once being trained can be used to handle …

This paper introduces the Attribute-Decomposed GAN, a novel generative model for
controllable person image synthesis, which can produce realistic person images with …

The generative adversarial network (GAN) framework has emerged as a powerful tool for
various image and video synthesis tasks, allowing the synthesis of visual content in an …