Abstract: A method for adding animation to a video includes: detecting whether the hand of a user in the current video frame, which is currently captured, holds an object; if so, determining a target animation corresponding to the object; and determining an animation addition area, which corresponds to the target animation, on the current video frame, and adding the target animation to the animation addition area. Therefore, a corresponding animation special effect is added for an object held by a user in a video, such that the interaction between the user and the handheld object is visually reflected, thereby improving the interestingness of video filming, and providing technical support for improving the viscosity of the user with respect to a video product.

Abstract: A method for adding animation to a video includes: detecting whether the hand of a user in the current video frame, which is currently captured, holds an object; if so, determining a target animation corresponding to the object; and determining an animation addition area, which corresponds to the target animation, on the current video frame, and adding the target animation to the animation addition area. Therefore, a corresponding animation special effect is added for an object held by a user in a video, such that the interaction between the user and the handheld object is visually reflected, thereby improving the interestingness of video filming, and providing technical support for improving the viscosity of the user with respect to a video product.

Abstract: The disclosure provides an approach for learning latent representations of data using factorized variational autoencoders (FVAEs). The FVAE framework builds a hierarchical Bayesian matrix factorization model on top of a variational autoencoder (VAE) by learning a VAE that has a factorized representation so as to compress the embedding space and enhance generalization and interpretability. In one embodiment, an FVAE application takes as input training data comprising observations of objects, and the FVAE application learns a latent representation of such data. In order to learn the latent representation, the FVAE application is configured to use a probabilistic VAE to jointly learn a latent representation of each of the objects and a corresponding factorization across time and identity.

Abstract: The disclosure provides an approach for learning latent representations of data using factorized variational autoencoders (FVAEs). The FVAE framework builds a hierarchical Bayesian matrix factorization model on top of a variational autoencoder (VAE) by learning a VAE that has a factorized representation so as to compress the embedding space and enhance generalization and interpretability. In one embodiment, an FVAE application takes as input training data comprising observations of objects, and the FVAE application learns a latent representation of such data. In order to learn the latent representation, the FVAE application is configured to use a probabilistic VAE to jointly learn a latent representation of each of the objects and a corresponding factorization across time and identity.

Abstract: An infrared ray gas burner comprises an ignition valve, a nozzle, an ejector, a furnace chamber, a porous combustion radiant panel and an ignition needle. An ignition nozzle is placed in the furnace chamber, a sundries baffle is provided in the furnace chamber below the porous combustion radiant panel and above the ignition nozzle and the ignition needle is set above the porous combustion radiant panel, so the gas for ignition ejected from the ignition nozzle quickly mixes with the air inside the furnace chamber, and the gas-air mixture escapes from fire holes of the porous combustion radiant panel above the ignition nozzle. The gas concentration of the mixed gas is relatively high and suitable for igniting, the main burner can be quickly ignited, there is no black smoke, obviously incomplete combustion, and it brings a high ignition rate.