High-resolution depth map generator for 3D video applications using time-of-flight camerasdoi:10.1109/tce.2017.015096Yunseok SongYo-Sung HoIEEE
The procedures are performed multiple times, and with each iteration, the generator and discriminator improve their performance in their respective roles. These networks compete to evaluate and reconstruct the variations within a dataset. In some cases, GAN can be used as supervised learning for ...
2)temporal asynchronization augmentation mechanism,时间异步采样策略,通过获取不完全匹配的rgb和depth clip,在特征层做数据增强,提升模型鲁棒性。 网络结构图: 整体由two stream构成:RGB stream (main visual info), depth stream (supplementary scene info); depth map的获取方式:Monodepth2 module作为depth estimator;...
Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generator - - May, 2023 DiffSynth: Latent In-Iteration Deflickering for Realistic Video Synthesis - - Aug, 2023 Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style Transfer - May, 2023 Instruct-Video2Avatar: ...
Kinghorn P, Zhang L, Shao L (2018) A region-based image caption generator with refined descriptions. Neurocomputing 272:416–424. https://doi.org/10.1016/j.neucom.2017.07.014 Article Google Scholar Zhang A, Lipton ZC, Li M, Smola AJ (2021) Dive into deep learning. https://doi.org/...
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这里用上面得到的class prototype作为condition来限制生成的数据分布。具体的网络结构在框架图中可以看到,给定一个noise和prototype vector先经过一个generator得到生成的feature,后面的decoder,discriminator以及分类产生的结果在训练阶段都将用于计算loss以优化网络。
Figure 1. Outline of the generator part in our proposed network. 3.1. TecoGAN The network is based on the GAN. The generator G is divided into two parts. The first part is the optical flow network F, which obtains the motion compensation 𝑣𝑡vt from two adjacent low-resolution input...
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CAP_OBSENSOR_DEPTH_MAP用于深度值(以毫米为单位) CAP_OBSENSOR_BGR_IMAGE用于从BGR流生成器中获取的数据, CAP_OBSENSOR_IR_IMAGE用于从IR流生成器中获取的数据(CV_16UC1)。 VideoCaptureOBSensorGenerators OBSensor视频捕获的生成器。 CAP_OBSENSOR_DEPTH_GENERATOR用于深度生成器 ...