A conditional deep generative model of people in natural images

A conditional deep generative model of people in natural images

We propose a deep generative model of humans in natural images which keeps 2D pose separated from other latent factors of variation, such as background scene and clothing. In contrast to methods that learn generative models of low-dimensional representations, e.g., segmentation masks and 2D skeleton...

Disgrifiad llawn

Manylion Llyfryddiaeth
Prif Awduron:	De Bem, R, Ghosh, A, Boukhayma, A, Ajanthan, T, Siddharth, N, Torr, P
Fformat:	Conference item
Cyhoeddwyd:	IEEE 2019

Eitemau Tebyg

DGPose: Deep Generative Models for Human Body Analysis
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Cyhoeddwyd: (2020)

A semi-supervised deep generative model for human body analysis
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Cyhoeddwyd: (2019)

3D hand shape and pose from images in the wild
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Looking deep at people: towards understanding and generating humans in images with deep learning
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Cross-modal deep face normals with deactivable skip connections
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SNIP: single-shot network pruning based on connection sensitivity
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Learning disentangled representations with semi-supervised deep generative models
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
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Deep fully-connected part-based models for human pose estimation
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Proximal mean-field for neural network quantization
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A signal propagation perspective for pruning neural networks at initialization
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Understanding the effects of data parallelism and sparsity on neural network training
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Data parallelism in training sparse neural networks
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Similarity learning for dense label transfer
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Efficient linear programming for dense CRFs
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Comparison of Two in Pixel Source Follower Schemes for Deep Subelectron Noise CMOS Image Sensors
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Mirror Descent view for Neural Network quantization
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Higher Order Conditional Random Fields in Deep Neural Networks
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Controllable text-to-image generation
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Controllable text-to-image generation
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Deep generative models for natural language processing
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Deep generative modeling of annotated bacterial biofilm images
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Continual learning with tiny episodic memories
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Memory-driven text-to-image generation
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Efficient relaxations for dense CRFs with sparse higher-order potentials
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Model-agnostic origin attribution of generated images with few-shot examples
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Local and blobal GANs with semantic-aware upsampling for image generation
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XingGAN for person image generation
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Bipartite graph reasoning GANs for person image generation
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Deep generative modeling for image synthesis and manipulation
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Image restoration and representation with deep generative models
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SCGG: A deep structure-conditioned graph generative model.
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SCGG: A deep structure-conditioned graph generative model
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Multi-agent diverse generative adversarial networks
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Latent guard: a safety framework for text-to-image generation
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Conditional random fields meet deep neural networks for semantic segmentation: combining probabilistic graphical models with deep learning for structured prediction
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