Hallucinated Humans: Learning Latent Factors to Model 3d Environments
نویسنده
چکیده
منابع مشابه
Conditional Models for 3d Human Pose Estimation
OF THE DISSERTATION Conditional Models for 3D Human Pose Estimation by ATUL KANAUJIA Dissertation Director: Dimitris Metaxas Human 3d pose estimation from monocular sequence is a challenging problem, owing to highly articulated structure of human body, varied anthropometry, self occlusion, depth ambiguities and large variability in the appearance and background in which humans may appear. Conve...
متن کاملLearning to Generate 3D Stylized Character Expressions from Humans
We present ExprGen, a system to automatically generate 3D stylized character expressions from humans in a perceptually valid and geometrically consistent manner. Our multi-stage deep learning system utilizes the latent variables of human and character expression recognition convolutional neural networks to control a 3D animated character rig. This end-to-end system takes images of human faces a...
متن کاملLearning a Hierarchical Latent-Variable Model of Voxelized 3D Shapes
We propose the Variational Shape Learner (VSL), a hierarchical latent-variable model for 3D shape learning. VSL employs an unsupervised approach to learning and inferring the underlying structure of voxelized 3D shapes. Through the use of skip-connections, our model can successfully learn a latent, hierarchical representation of objects. Furthermore, realistic 3D objects can be easily generated...
متن کاملشناسایی و اعتباریابی عوامل سازمانی تسهیلکننده انتقال آموزش
This research aims to identify the facilitating factors of learning transfer in NIGC, using a mixed research method. In the qualitative phase, 16 experts of NIGC and university faculty members were selected as the research sample. In the qualitative phase, survey research method was applied and a sample of 376 subjects was selected based on Cochran formula. In order to analyze the qualitative d...
متن کاملWorld Models
We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task. W...
متن کامل