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Found 13 papers, 8 papers with code
Deep Geometry Post-Processing for Decompressed Point Clouds
Point cloud compression plays a crucial role in reducing the huge cost of data storage and transmission.
Neural Texture Extraction and Distribution for Controllable Person Image Synthesis
Our model is trained to predict human images in arbitrary poses, which encourages it to extract disentangled and expressive neural textures representing the appearance of different semantic entities.
PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering
The proposed model can generate photo-realistic portrait images with accurate movements according to intuitive modifications.
Combining Attention with Flow for Person Image Synthesis
Pose-guided person image synthesis aims to synthesize person images by transforming reference images into target poses.
Structure-Transformed Texture-Enhanced Network for Person Image Synthesis
Pose-guided virtual try-on task aims to modify the fashion item based on pose transfer task.
Deep Spatial Transformation for Pose-Guided Person Image Generation and Animation
We show that our framework can spatially transform the inputs in an efficient manner.
Deep Image Spatial Transformation for Person Image Generation
Finally, we warp the source features using a content-aware sampling method with the obtained local attention coefficients.
StructureFlow: Image Inpainting via Structure-aware Appearance Flow
Image inpainting techniques have shown significant improvements by using deep neural networks recently.
Deep AutoEncoder-based Lossy Geometry Compression for Point Clouds
Point cloud is a fundamental 3D representation which is widely used in real world applications such as autonomous driving.
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection
Remarkably, we obtain the frame-level AUC score of 82. 12% on UCF-Crime.
Step-by-step Erasion, One-by-one Collection: A Weakly Supervised Temporal Action Detector
Weakly supervised temporal action detection is a Herculean task in understanding untrimmed videos, since no supervisory signal except the video-level category label is available on training data.
Exploiting the Value of the Center-dark Channel Prior for Salient Object Detection
In this paper, we propose a novel salient object detection algorithm for RGB-D images using center-dark channel priors.
PDNet: Prior-model Guided Depth-enhanced Network for Salient Object Detection
One is the lack of tremendous amount of annotated data to train a network.
RGB-D Salient Object Detection
RGB Salient Object Detection
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