4 accepted papers
Vision Transformers (ViTs) have achieved remarkable progress in visual and multimodal tasks, yet their deployment remains costly. Token-adaptive methods reduce FLOPs through dynamic depth computation, but they face two limitations: (1) Global attention overemphasizes highly similar foreground region
"Controllable person image synthesis task enables a wide range of applications through explicit control over body pose and appearance. In this paper, we propose a cross attention based style distribution module that computes between the source semantic styles and target pose for pose transfer. The m…
View synthesis is usually done by an autoencoder, in which the encoder maps a source view image into a latent content code, and the decoder transforms it into a target view image according to the condition. However, the source contents are often not well kept in this setting, which leads to unnecess…
Novel view synthesis often requires to have the paired data from both the source and target views. This paper proposes a view translation model within cVAE-GAN framework for the purpose of unpaired training. We design a conditional deformable module (CDM) which uses the source (or target) view condi…