Computer Vision News - November 2022

9 Garvita Tiwari the same idea to a very high dimension and use the same formulation for a complicated data distribution and a complicated space . We learn a prior using that approach. ” The work has several real-world applications, including in the entertainment sphere. In animation, for example, animating a character dancing to a song would usually require expensive animators working long hours. However, this research direction and these models make it possible to achieve the same result much faster and semi-automatically. “ This is the first time anyone has done this, ” Garvita tells us. “ I guess that was appreciated by the program and area 3D human modeling is about modeling how a human behaves, moves, and looks in the real world, which is closely related to seeing a person in a virtual or augmented reality environment. In this work, Garvita explores how they move. It introduces a human pose prior model, which is essential for modeling realistic human poses and avoiding results such as the unrealistic rotation of joints. In contrast to previous work on human pose priors, which tackled it from a very specific approach, this work merges two different fields. “ Neural implicit fields are a recent advancement and important for modeling 3D shapes, ” Garvita explains. “ We extend RABLE T I O N ARD CCV