DynaDog+T: A Parametric Animal Model for Synthetic Canine Image Generation

07/15/2021
by   Jake Deane, et al.
13

Synthetic data is becoming increasingly common for training computer vision models for a variety of tasks. Notably, such data has been applied in tasks related to humans such as 3D pose estimation where data is either difficult to create or obtain in realistic settings. Comparatively, there has been less work into synthetic animal data and it's uses for training models. Consequently, we introduce a parametric canine model, DynaDog+T, for generating synthetic canine images and data which we use for a common computer vision task, binary segmentation, which would otherwise be difficult due to the lack of available data.

READ FULL TEXT

page 2

page 4

page 9

page 12

page 13

page 14

page 15

research
01/03/2023

Procedural Humans for Computer Vision

Recent work has shown the benefits of synthetic data for use in computer...
research
07/09/2021

Unity Perception: Generate Synthetic Data for Computer Vision

We introduce the Unity Perception package which aims to simplify and acc...
research
06/08/2023

Does Image Anonymization Impact Computer Vision Training?

Image anonymization is widely adapted in practice to comply with privacy...
research
05/31/2022

Hands-Up: Leveraging Synthetic Data for Hands-On-Wheel Detection

Over the past few years there has been major progress in the field of sy...
research
05/05/2022

OCR Synthetic Benchmark Dataset for Indic Languages

We present the largest publicly available synthetic OCR benchmark datase...
research
11/22/2022

Synthetic Data for Semantic Image Segmentation of Imagery of Unmanned Spacecraft

Images of spacecraft photographed from other spacecraft operating in out...
research
11/02/2021

3-D PET Image Generation with tumour masks using TGAN

Training computer-vision related algorithms on medical images for diseas...

Please sign up or login with your details

Forgot password? Click here to reset