Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …

This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a
text-to-image synthesis model (ie, Stable Diffusion). ModelScopeT2V incorporates spatio …

Score-based generative models (SGMs) are a powerful class of generative models that
exhibit remarkable empirical performance. Score-based generative modelling (SGM) …

The design of novel protein structures remains a challenge in protein engineering for
applications across biomedicine and chemistry. In this line of work, a diffusion model over …

Text-to-image diffusion models have demonstrated remarkable capabilities in transforming
text prompts into coherent images yet the computational cost of the multi-step inference …

Multi-objective optimization problems are ubiquitous in robotics, eg, the optimization of a
robot manipulation task requires a joint consideration of grasp pose configurations …

Video synthesis has recently made remarkable strides benefiting from the rapid
development of diffusion models. However, it still encounters challenges in terms of …

We propose Riemannian Flow Matching (RFM), a simple yet powerful framework for training
continuous normalizing flows on manifolds. Existing methods for generative modeling on …

Modern successes of diffusion models in learning complex, high-dimensional data
distributions are attributed, in part, to their capability to construct diffusion processes with …