6 citations · 26 across the 8 of their papers we have counts for
11 papers
Disentangled3D: Learning a 3D Generative Model with Disentangled Geometry and Appearance from Monocular Images
Ayush Tewari, Mallikarjun B R, Xingang Pan +3
Learning 3D generative models from a dataset of monocular images enables self-supervised 3D reasoning and controllable synthesis. State-of-the-art 3D generative models are GANs whi…
Generative Occupancy Fields for 3D Surface-Aware Image Synthesis
Xudong Xu, Xingang Pan, Dahua Lin +1
The advent of generative radiance fields has significantly promoted the development of 3D-aware image synthesis. The cumulative rendering process in radiance fields makes training…
Talk-to-Edit: Fine-Grained Facial Editing via Dialog
Yuming Jiang, Ziqi Huang, Xingang Pan +2
Facial editing is an important task in vision and graphics with numerous applications. However, existing works are incapable to deliver a continuous and fine-grained editing mode (…
Do 2D GANs Know 3D Shape? Unsupervised 3D shape reconstruction from 2D Image GANs
Xingang Pan, Bo Dai, Ziwei Liu +2
Natural images are projections of 3D objects on a 2D image plane. While state-of-the-art 2D generative models like GANs show unprecedented quality in modeling the natural image man…
Self-Supervised Scene De-occlusion
Xiaohang Zhan, Xingang Pan, Bo Dai +3
Natural scene understanding is a challenging task, particularly when encountering images of multiple objects that are partially occluded. This obstacle is given rise by varying obj…
Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation
Xingang Pan, Xiaohang Zhan, Bo Dai +3
Learning a good image prior is a long-term goal for image restoration and manipulation. While existing methods like deep image prior (DIP) capture low-level image statistics, there…