Diffusion models create data from noise by inverting the forward paths of data towards noise
and have emerged as a powerful generative modeling technique for high-dimensional …

The diffusion model has long been plagued by scalability and quadratic complexity issues,
especially within transformer-based structures. In this study, we aim to leverage the long …

Diffusion Models are popular generative modeling methods in various vision tasks, attracting
significant attention. They can be considered a unique instance of self-supervised learning …

Diffusion models, and their generalization, flow matching, have had a remarkable impact on
the field of media generation. Here, the conventional approach is to learn the complex …

Modern 3D generation methods can rapidly create shapes from sparse or single views, but
their outputs often lack geometric detail due to computational constraints. We present …

With the rapid advancement of diffusion-based generative models, portrait image animation
has achieved remarkable results. However, it still faces challenges in temporally consistent …