This paper addresses the problem of 3D human pose estimation in the wild. A significant challenge is the lack of training data, i.e., 2D images of humans annotated with 3D poses. Such data is necessary to train state-of-the-art CNN architectures. Here, we propose a solution to generate a large set of photorealistic synthetic images of humans with 3D pose annotations. We introduce an image-based synthesis engine that artificially augments a dataset of real images with 2D human pose annotations using 3D Motion Capture (MoCap) data.

This work was completed while Xin Y uan was a PhD student at the University of Chicago.

CLIP struggles with representing [ 17 ].

The augmentations include: Color: adjusts the brightness, saturation, and contrast of images. Flip: flips the images horizontally with a probability of 0.5. It happens at a probability of 0.5. The first part is the update of the synthetic images. The second part is the update of the network.

Photoswap is able to maintain the same pose with a decent style preservation.

The problem of extracting auseful interpretation from an image may be simplified byimagesegmentation, i.e., by partitioning such image into regions associated to separate objects.