3 papers
cs.CV2026
Riemannian Motion Generation: A Unified Framework for Human Motion Representation and Generation via Riemannian Flow Matching
Fangran Miao, Jian Huang, Ting Li
Human motion generation is often learned in Euclidean spaces, although valid motions follow structured non-Euclidean geometry. We present Riemannian Motion Generation (RMG), a unif…
cs.LG2024
Bayesian Power Steering: An Effective Approach for Domain Adaptation of Diffusion Models
Ding Huang, Ting Li, Jian Huang
We propose a Bayesian framework for fine-tuning large diffusion models with a novel network structure called Bayesian Power Steering (BPS). We clarify the meaning behind adaptation…
stat.ML2023
Conditional Stochastic Interpolation for Generative Learning
Ding Huang, Jian Huang, Ting Li +1
We propose a conditional stochastic interpolation (CSI) method for learning conditional distributions. CSI is based on estimating probability flow equations or stochastic different…