In this paper, we present ControlVideo, a novel method for text-driven v...
Score distillation sampling (SDS) has shown great promise in text-to-3D
...
Large-scale diffusion models like Stable Diffusion are powerful and find...
This paper proposes a unified diffusion framework (dubbed UniDiffuser) t...
We propose a three-stage training strategy called dual pseudo training (...
A large-scale deep model pre-trained on massive labeled or unlabeled dat...
Extensive empirical evidence demonstrates that conditional generative mo...
Diffusion probabilistic models (DPMs) have achieved impressive success i...
Inverse molecular design is critical in material science and drug discov...
Vision transformers (ViT) have shown promise in various vision tasks
inc...
Deep generative models (DGMs) are data-eager. Essentially, it is because...
Score-based diffusion generative models (SDGMs) have achieved the SOTA F...
Score-based generative models have excellent performance in terms of
gen...
Diffusion probabilistic models (DPMs) are a class of powerful deep gener...
Diffusion probabilistic models (DPMs) are emerging powerful generative
m...
Diffusion probabilistic models (DPMs) represent a class of powerful
gene...
Recently, the (gradient-based) bilevel programming framework is widely u...
The learning and evaluation of energy-based latent variable models (EBLV...
Score matching (SM) provides a compelling approach to learn energy-based...
Deep generative models are effective methods of modeling data. However, ...
Sometimes it is not enough for a DNN to produce an outcome. For example,...
Connectivity and layout of underlying networks largely determine the beh...