Code will be madepublicuponpublication. Imagefeatureextractor. RC d, from the previously constructed time-augmented skeletal featuress 1:C,t RL C d. Inspired by The SparseConvNet consists in 3D sparse convolutions toprocess the input volume, diffusing the skeletal features into the nearby 3D space. The overview of the cross-attention between the sampled time-augmented skeletal features and time-specific pixel-aligned features is illustratedinFig.2. Wediscuss the additional details about the datasets used, including the train/test splits and license information. C.1 ZJU-MoCap We use the512 512 videos for the training and testing following the original Neural Body [7].

Experiments onthe ZJU-MoCap andAISTdatasets showthatourmethod significantly outperforms recent generalizable NeRF methods on unseen identities and poses.