Paper Reading AI Learner

DGGAN: Depth-image Guided Generative Adversarial Networks for Disentangling RGB and Depth Images in 3D Hand Pose Estimation

2020-12-06 07:23:21
Liangjian Chen, Shih-Yao Lin, Yusheng Xie, Yen-Yu Lin, Wei Fan, Xiaohui Xie

Abstract

Estimating3D hand poses from RGB images is essentialto a wide range of potential applications, but is challengingowing to substantial ambiguity in the inference of depth in-formation from RGB images. State-of-the-art estimators ad-dress this problem by regularizing3D hand pose estimationmodels during training to enforce the consistency betweenthe predicted3D poses and the ground-truth depth maps.However, these estimators rely on both RGB images and thepaired depth maps during training. In this study, we proposea conditional generative adversarial network (GAN) model,called Depth-image Guided GAN (DGGAN), to generate re-alistic depth maps conditioned on the input RGB image, anduse the synthesized depth maps to regularize the3D handpose estimation model, therefore eliminating the need forground-truth depth maps. Experimental results on multiplebenchmark datasets show that the synthesized depth mapsproduced by DGGAN are quite effective in regularizing thepose estimation model, yielding new state-of-the-art resultsin estimation accuracy, notably reducing the mean3D end-point errors (EPE) by4.7%,16.5%, and6.8%on the RHD,STB and MHP datasets, respectively.

Abstract (translated)

URL

https://arxiv.org/abs/2012.03197

PDF

https://arxiv.org/pdf/2012.03197.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot