49 citations · 60 across the 9 of their papers we have counts for
7 papers · 1 filter
X-Adapter: Adding Universal Compatibility of Plugins for Upgraded Diffusion Model
Lingmin Ran, Xiaodong Cun, Jia-Wei Liu +5
We introduce X-Adapter, a universal upgrader to enable the pretrained plug-and-play modules (e.g., ControlNet, LoRA) to work directly with the upgraded text-to-image diffusion mode…
Instant3D: Instant Text-to-3D Generation
Ming Li, Pan Zhou, Jia-Wei Liu +4
Text-to-3D generation has attracted much attention from the computer vision community. Existing methods mainly optimize a neural field from scratch for each text prompt, relying on…
MotionDirector: Motion Customization of Text-to-Video Diffusion Models
Rui Zhao, Yuchao Gu, Jay Zhangjie Wu +5
Large-scale pre-trained diffusion models have exhibited remarkable capabilities in diverse video generations. Given a set of video clips of the same motion concept, the task of Mot…
DynVideo-E: Harnessing Dynamic NeRF for Large-Scale Motion- and View-Change Human-Centric Video Editing
Jia-Wei Liu, Yan-Pei Cao, Jay Zhangjie Wu +6
Despite recent progress in diffusion-based video editing, existing methods are limited to short-length videos due to the contradiction between long-range consistency and frame-wise…
CroCoDai: A Stablecoin for Cross-Chain Commerce
Daniël Reijsbergen, Bretislav Hajek, Tien Tuan Anh Dinh +3
Decentralized Finance (DeFi), in which digital assets are exchanged without trusted intermediaries, has grown rapidly in value in recent years. The global DeFi ecosystem is fragmen…
HOSNeRF: Dynamic Human-Object-Scene Neural Radiance Fields from a Single Video
Jia-Wei Liu, Yan-Pei Cao, Tianyuan Yang +5
We introduce HOSNeRF, a novel 360° free-viewpoint rendering method that reconstructs neural radiance fields for dynamic human-object-scene from a single monocular in-the-wild video…
STPrivacy: Spatio-Temporal Privacy-Preserving Action Recognition
Ming Li, Xiangyu Xu, Hehe Fan +7
Existing methods of privacy-preserving action recognition (PPAR) mainly focus on frame-level (spatial) privacy removal through 2D CNNs. Unfortunately, they have two major drawbacks…