4 citations · 14 across the 17 of their papers we have counts for
5 papers · 1 filter
FADE: Frame-Aware Diffusion-Transformer-based Multi-Concept Erasure for Video Unlearning
Yuchen Li, Kaiyuan Deng, Chaoran Feng +2
Text-to-video (T2V) diffusion models can reproduce copyrighted, violent, or explicit content, which motivates concept erasure: removing designated concepts from a pretrained model…
UniWorld-View: Large-Baseline View Synthesis via Video Diffusion Models
Haiyang Zhou, Wangbo Yu, Chaoran Feng +3
The abundance of casually captured monocular videos and images on social media provides a valuable source for immersive content creation, where generating novel views from such spa…
MSVS-VAE: Multi-Scale Anchored VecSet for High-Fidelity 3D Reconstruction
Dehao Hao, Kaiyi Zhang, Tanghui Jia +10
High-fidelity 3D generative modeling increasingly relies on the latent diffusion paradigm, where the reconstruction quality of the underlying 3D VAE becomes a primary bottleneck. E…
DeblurNVS: Geometric Latent Diffusion for Novel View Synthesis from Sparse Motion-Blurred Images
Changyue Shi, Wangbo Yu, Chaoran Feng +1
Novel view synthesis (NVS) is a fundamental problem in computer vision and graphics. Recent advances in neural radiance fields (NeRF), 3D Gaussian Splatting (3DGS), and generative…
AnyAct: Towards Human Reenactment of Character Motion From Video
Liuhan Chen, Lei Zhong, Jiawei Wang +5
We study the problem of directly deriving an initial human reenactment from a monocular video of a non-human character. Our goal is not to reconstruct the source character itself b…