64 citations · 268 across the 26 of their papers we have counts for
8 papers · 1 filter
Towards Accurate Guided Diffusion Sampling through Symplectic Adjoint Method
Jiachun Pan, Hanshu Yan, Jun Hao Liew +2
Training-free guided sampling in diffusion models leverages off-the-shelf pre-trained networks, such as an aesthetic evaluation model, to guide the generation process. Current trai…
MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model
Zhongcong Xu, Jianfeng Zhang, Jun Hao Liew +5
This paper studies the human image animation task, which aims to generate a video of a certain reference identity following a particular motion sequence. Existing animation works t…
MagicProp: Diffusion-based Video Editing via Motion-aware Appearance Propagation
Hanshu Yan, Jun Hao Liew, Long Mai +2
This paper addresses the issue of modifying the visual appearance of videos while preserving their motion. A novel framework, named MagicProp, is proposed, which disentangles the v…
MagicEdit: High-Fidelity and Temporally Coherent Video Editing
Jun Hao Liew, Hanshu Yan, Jianfeng Zhang +2
In this report, we present MagicEdit, a surprisingly simple yet effective solution to the text-guided video editing task. We found that high-fidelity and temporally coherent video-…
MagicAvatar: Multimodal Avatar Generation and Animation
Jianfeng Zhang, Hanshu Yan, Zhongcong Xu +2
This report presents MagicAvatar, a framework for multimodal video generation and animation of human avatars. Unlike most existing methods that generate avatar-centric videos direc…
AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models
Jiachun Pan, Jun Hao Liew, Vincent Y. F. Tan +2
Existing customization methods require access to multiple reference examples to align pre-trained diffusion probabilistic models (DPMs) with user-provided concepts. This paper aims…
DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing
Yujun Shi, Chuhui Xue, Jun Hao Liew +5
Accurate and controllable image editing is a challenging task that has attracted significant attention recently. Notably, DragGAN is an interactive point-based image editing framew…
Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach
Xiang Lan, Hanshu Yan, Shenda Hong +1
Not all positive pairs are beneficial to time series contrastive learning. In this paper, we study two types of bad positive pairs that can impair the quality of time series repres…