{"id":15,"date":"2022-04-27T03:42:55","date_gmt":"2022-04-27T03:42:55","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2022team10\/?page_id=15"},"modified":"2022-12-03T20:34:10","modified_gmt":"2022-12-04T01:34:10","slug":"overview","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2022team10\/","title":{"rendered":"Overview"},"content":{"rendered":"\n

Motivation<\/h1>\n\n\n\n

Our project is called Dynamic Implicit Neural Representation for Avatar Animation. We would like to build a photorealistic human avatar for which the facial expression and head pose can be explicitly controlled. Some of the social motivations include the use of a human avatar as a digital twin the AR\/VR space. It can also have applications in the entertainment industry for overlaying another  language on top for seamless dubbing. In a more technical fashion this is useful to generate synthetic data thus increasing the data space size.<\/p>\n\n\n\n

Research Objective<\/h1>\n\n\n\n

Our main research objective is to build a model that can dynamically change expressions through the use of Neural Radiance Fields (NeRF) and Generative Adversarial Networks (GAN). <\/p>\n\n\n\n

Approach<\/h1>\n\n\n\n

We will be using MoFaNeRF: Morphable Facial Neural Radiance Fields as the basis of our experiments. Our project is split into two primary categories. <\/p>\n\n\n\n