Yaohui Wang

TL;DR: A novel conditional GAN architecture, namely ImaGINator, which given a single image, a condition (label of a facial expression or action) and noise, decomposes appearance and motion in both latent and high level feature spaces, generating realistic videos.

TL;DR: Li et al. as discussed by the authors proposed a mesh-based view generator to generate novel views of a person and proposed a view-invariant loss to facilitate contrastive learning between original and generated views.

TL;DR: The incorporation of simple temporal self-attentions, coupled with rotary positional encoding, adequately captures the temporal correlations inherent in video data and validate that the process of joint image-video fine-tuning plays a pivotal role in producing high-quality and creative outcomes.

TL;DR: This paper incorporates a Generative Adversarial Network and a contrastive learning module into one joint training framework, and proposes a mesh-based view generator that significantly outperforms state-of-the-art methods under both, fully unsupervised and unsuper supervised domain adaptive settings on several large scale ReID dat-sets.