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

The ability toleverage large-scale hardware parallelism has been one of the key1 enablers of the accelerated recent progress in machine learning.

The ability toleverage large-scale hardware parallelism has been one ofthe key enablers of the accelerated recent progress in machine learning.

More insights about the predictive GP posterior used for the contrastive expectation integrals are inthefirst section. Our coding idea is that the Python user only specifies a dictionary of models as input:models = {model1, model2, ..., modelK}.

This lets us identify several changes to both the sampling and training processes, as well as preconditioning of the score networks.