Easy access to audio-visual content on social media, combined with the availability of
modern tools such as Tensorflow or Keras, and open-source trained models, along with …

Gait recognition is an appealing biometric modality which aims to identify individuals based
on the way they walk. Deep learning has reshaped the research landscape in this area …

Animating a still image offers an engaging visual experience. Traditional image animation
techniques mainly focus on animating natural scenes with stochastic dynamics (eg clouds …

Conditional image-to-video (cI2V) generation aims to synthesize a new plausible video
starting from an image (eg, a person's face) and a condition (eg, an action class label like …

This work aims to learn a high-quality text-to-video (T2V) generative model by leveraging a
pre-trained text-to-image (T2I) model as a basis. It is a highly desirable yet challenging task …

We introduce Motion-I2V, a novel framework for consistent and controllable text-guided
image-to-video generation (I2V). In contrast to previous methods that directly learn the …

Recent self-supervised contrastive learning provides an effective approach for unsupervised
person re-identification (ReID) by learning invariance from different views (transformed …

Due to the remarkable progress of deep generative models, animating images has become
increasingly efficient, whereas associated results have become increasingly realistic …

Generative deep learning algorithms have progressed to a point where it is difficult to tell the
difference between what is real and what is fake. In 2018, it was discovered how easy it is to …

We propose a novel Latent Diffusion Transformer, namely Latte, for video generation. Latte
first extracts spatio-temporal tokens from input videos and then adopts a series of …