Deep generative models have revolutionized visual computing, achieving unprecedented realism in synthesizing images, videos, and 3D scenes. However, as the quality of generation matures, the critical challenge shifts towards achieving precise, user-driven control and practical, efficient deployment, especially in high-stakes domains like medical imaging. This dissertation addresses two fundamental obstacles hindering this transition. The first is the lack of controlled generation capabilities in emerging generative architectures, such as discrete diffusion and next-scale visual autoregressive models, whose non-invertible sampling mechanisms prevent the recovery of latent codes necessary for high-fidelity editing. The second is the "practicality gap" in clinical applications, where a confluence of data scarcity (few-shot learning), data heterogeneity (non-IID distributions), stringent privacy constraints (requiring Federated Learning), and communication bottlenecks impede the collaborative development of robust AI models. This dissertation presents a cohesive body of work that bridges these gaps, progressing from foundational algorithmic innovations to their application in real-world medical imaging. The primary contributions are fourfold: (1) DICE, a pioneering inversion framework that enables, for the first time, controllable editing for discrete diffusion and masked generative models; (2) VARIN, the first noise inversion-based editing technique for next-scale visual autoregressive models, which introduces a novel pseudo-inverse for the argmax operator; (3) DMCVR, a morphology-guided diffusion model that solves the clinical problem of 3D cardiac volume reconstruction from sparse MRI by leveraging explicit anatomical conditioning; and (4) a comprehensive suite of Federated Learning frameworks that integrate few-shot learning, dual knowledge distillation, and parameter-efficient fine-tuning to enable robust, private, and communication-efficient medical image analysis on decentralized data. Collectively, this research provides a validated blueprint for building the next generation of visual computing systems that are not only powerful but also controllable, efficient, and trustworthy.
Xiaoxiao He (Thu,) studied this question.