# Novel-View Human Action Synthesis

arXiv: https://arxiv.org/abs/2007.02808 · HTML: https://mlsanity.com/paper/2007.02808/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [Novel View Video Prediction Using a Dual Representation](https://mlsanity.com/paper/2106.03956/) (2021)
- [Pose-guided Generative Adversarial Net for Novel View Action Synthesis](https://mlsanity.com/paper/2110.07993/) (2021)

## Abstract (arXiv)

Novel-View Human Action Synthesis aims to synthesize the movement of a body from a virtual viewpoint, given a video from a real viewpoint. We present a novel 3D reasoning to synthesize the target viewpoint. We first estimate the 3D mesh of the target body and transfer the rough textures from the 2D images to the mesh. As this transfer may generate sparse textures on the mesh due to frame resolution or occlusions. We produce a semi-dense textured mesh by propagating the transferred textures both locally, within local geodesic neighborhoods, and globally, across symmetric semantic parts. Next, we introduce a context-based generator to learn how to correct and complete the residual appearance information. This allows the network to independently focus on learning the foreground and background synthesis tasks. We validate the proposed solution on the public NTU RGB+D dataset. The code and resources are available at https://bit.ly/36u3h4K.