Animatable Neural Radiance Fields For Human Body Modeling Deepai Some recent works have proposed to decompose a non rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation space points to the canonical space, thereby enabling them to learn the dynamic scene from images. View a pdf of the paper titled animatable neural radiance fields for modeling dynamic human bodies, by sida peng and 6 other authors.
Animatable Neural Radiance Fields For Human Body Modeling Deepai We introduce a novel representation called neural blend weight field, which can be combined with nerf and 3d human skeletons to recover animatable human models from multi view videos. We present a learned model of human body shape and pose dependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. Some recent works have proposed to decompose a non rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation space points to the canonical space, thereby enabling them to learn the dynamic scene from images. Some recent works have proposed to decompose a dynamic scene into a canonical neural radiance field and a set of deformation fields that map observation space points to the canonical space, thereby enabling them to learn the dynamic scene from images.
Animatable Neural Radiance Fields For Human Body Modeling Deepai Some recent works have proposed to decompose a non rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation space points to the canonical space, thereby enabling them to learn the dynamic scene from images. Some recent works have proposed to decompose a dynamic scene into a canonical neural radiance field and a set of deformation fields that map observation space points to the canonical space, thereby enabling them to learn the dynamic scene from images. This paper addresses the challenge of reconstructing an animatable human model from a multi view video by introducing neural blend weight fields to produce the deformation fields and shows that this approach significantly outperforms recent human synthesis methods. Given a multi view video of a performer, our task is to reconstruct an animatable human model that can be used to synthesize free viewpoint videos of the performer under novel human poses. 10 28 2021 to make the comparison with animatable nerf easier on the human3.6m dataset, we save the quantitative results at here, which also contains the results of other methods, including neural body, d nerf, multi view neural human rendering, and deferred neural human rendering. 10 28 2021 to make the comparison with animatable nerf easier on the human3.6m dataset, we save the quantitative results at here, which also contains the results of other methods, including neural body, d nerf, multi view neural human rendering, and deferred neural human rendering.
Comments are closed.