Nothing Special   »   [go: up one dir, main page]

Li et al., 2020 - Google Patents

MannequinChallenge: Learning the depths of moving people by watching frozen people

Li et al., 2020

Document ID
7997294999462875699
Author
Li Z
Dekel T
Cole F
Tucker R
Snavely N
Liu C
Freeman W
Publication year
Publication venue
IEEE Transactions on Pattern Analysis and Machine Intelligence

External Links

Snippet

We present a method for predicting dense depth in scenarios where both a monocular camera and people in the scene are freely moving (right). Existing methods for recovering depth for dynamic, non-rigid objects from monocular video impose strong assumptions on …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • 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
    • G06T2207/10016—Video; Image sequence
    • 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
    • G06T2207/10024—Color image
    • 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
    • G06T2207/30196—Human being; Person
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • 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
    • 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
    • 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
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • 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
    • G06K9/00362—Recognising human body or animal bodies, e.g. vehicle occupant, pedestrian; Recognising body parts, e.g. hand
    • G06K9/00369—Recognition of whole body, e.g. static pedestrian or occupant recognition
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • 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

Similar Documents

Publication Publication Date Title
Li et al. Learning the depths of moving people by watching frozen people
CN109636831B (en) Method for estimating three-dimensional human body posture and hand information
Elhayek et al. Marconi—convnet-based marker-less motion capture in outdoor and indoor scenes
Li et al. MannequinChallenge: Learning the depths of moving people by watching frozen people
Panteleris et al. Back to rgb: 3d tracking of hands and hand-object interactions based on short-baseline stereo
CN113706699B (en) Data processing method and device, electronic equipment and computer readable storage medium
Sun et al. Sc-depthv3: Robust self-supervised monocular depth estimation for dynamic scenes
Hu et al. 3DBodyNet: fast reconstruction of 3D animatable human body shape from a single commodity depth camera
Kemelmacher-Shlizerman et al. Being john malkovich
Ren et al. Lidar-aid inertial poser: Large-scale human motion capture by sparse inertial and lidar sensors
Riegler et al. Connecting the dots: Learning representations for active monocular depth estimation
Ye et al. Free-viewpoint video of human actors using multiple handheld kinects
Gu et al. Exploring severe occlusion: Multi-person 3d pose estimation with gated convolution
Elhayek et al. Fully automatic multi-person human motion capture for vr applications
Khan et al. An efficient encoder–decoder model for portrait depth estimation from single images trained on pixel-accurate synthetic data
Xie et al. Recent advances in conventional and deep learning-based depth completion: A survey
Huang et al. A review of 3D human body pose estimation and mesh recovery
Liang et al. Hybridcap: Inertia-aid monocular capture of challenging human motions
Zhou et al. Constant velocity constraints for self-supervised monocular depth estimation
Vo et al. Spatiotemporal bundle adjustment for dynamic 3d human reconstruction in the wild
Lin et al. Overview of 3d human pose estimation
Chen et al. Prior-knowledge-based self-attention network for 3D human pose estimation
Li et al. Three-dimensional motion estimation via matrix completion
Khan et al. Towards monocular neural facial depth estimation: Past, present, and future
Park et al. Learning to produce 3D media from a captured 2D video