Depth-Aware Generative Adversarial Network for Talking Head Video Generation -Supplementary Material- Fa-Ting Hong1 Longhao Zhang2 Li Shen2 Dan Xu1* 1Department of Computer Science and Engineering, HKUST 2Alibaba Cloud fhongac@cse.ust.hk, longhao.zlh@alibaba-inc.com, lshen.lsh@g...
All these contributions compose a novel depth-aware generative adversarial network (DaGAN) for talking head generation. Extensive experiments conducted demonstrate that our proposed method can generate highly realistic faces, and achieve significant results on the unseen human faces. 展开 ...
Motivated by this, we develop a depth-aware haze generation network, namely HazeGAN, by incorporating the Generative Adversarial Network (GAN), depth estimation network, and physical atmospheric scattering to progressively synthesize hazy images. Specifically, a separate depth estimation network is ...
📖 Depth-Aware Generative Adversarial Network for Talking Head Video Generation (CVPR 2022) 🔥 If DaGAN is helpful in your photos/projects, please help to ⭐ it or recommend it to your friends. Thanks🔥 [Paper] [Project Page] [Demo] [Poster Video] ...
IMG2DSM [9] leverages the generative adversarial network for monocular height estimation on the base of Unet. Son [10] proposes a deep monocular depth network for single aerial imagery height estimation. It is especially efficient for 3D reconstruction in urban areas when the building suffers from...
In this work, based on the structure of conventional generative adversarial network, we propose a depth aware method to estimate the depth maps and provide the depth features for dehazing within one joint framework. By fusing the depth feature to the dehazing network, the dehazing model is able...
Spatial correspondence with generative adversarial network: Learning depth from monocular videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea, 27 October–2 November 2019; pp. 7494–7504. [Google Scholar] Wimbauer, F.; Yang, N.; Von ...