Xu et al., 2019 - Google Patents
Predicting animation skeletons for 3d articulated models via volumetric netsXu et al., 2019
View PDF- Document ID
- 15903717760560857462
- Author
- Xu Z
- Zhou Y
- Kalogerakis E
- Singh K
- Publication year
- Publication venue
- 2019 international conference on 3D vision (3DV)
External Links
Snippet
We present a learning method for predicting animation skeletons for input 3D models of articulated characters. In contrast to previous approaches that fit pre-defined skeleton templates or predict fixed sets of joints, our method produces an animation skeleton tailored …
- 210000002356 Skeleton 0 title abstract description 103
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Xu et al. | Predicting animation skeletons for 3d articulated models via volumetric nets | |
Xu et al. | Rignet: Neural rigging for articulated characters | |
Ge et al. | 3d hand shape and pose estimation from a single rgb image | |
Zhu et al. | Detailed human shape estimation from a single image by hierarchical mesh deformation | |
US10679046B1 (en) | Machine learning systems and methods of estimating body shape from images | |
Tung et al. | Self-supervised learning of motion capture | |
Varol et al. | Bodynet: Volumetric inference of 3d human body shapes | |
Tekin et al. | Learning to fuse 2d and 3d image cues for monocular body pose estimation | |
Moreno-Noguer | 3d human pose estimation from a single image via distance matrix regression | |
Oberweger et al. | Hands deep in deep learning for hand pose estimation | |
Bogo et al. | Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image | |
Lifshitz et al. | Human pose estimation using deep consensus voting | |
Zhou et al. | Sparseness meets deepness: 3d human pose estimation from monocular video | |
EP3579198A1 (en) | Image processing method, system and device | |
Xu et al. | Autoscanning for coupled scene reconstruction and proactive object analysis | |
Petit et al. | Tracking elastic deformable objects with an RGB-D sensor for a pizza chef robot | |
Martínez-González et al. | Efficient convolutional neural networks for depth-based multi-person pose estimation | |
Rogez et al. | Image-based synthesis for deep 3D human pose estimation | |
Alldieck et al. | Optical flow-based 3d human motion estimation from monocular video | |
Tan et al. | Deep multi-task and task-specific feature learning network for robust shape preserved organ segmentation | |
Chatzitofis et al. | DeMoCap: Low-cost marker-based motion capture | |
Michel et al. | Tracking the articulated motion of the human body with two RGBD cameras | |
Laga | A survey on deep learning architectures for image-based depth reconstruction | |
Lei et al. | What's the Situation With Intelligent Mesh Generation: A Survey and Perspectives | |
Verma et al. | Two-stage multi-view deep network for 3D human pose reconstruction using images and its 2D joint heatmaps through enhanced stack-hourglass approach |