Dynamic human body free viewpoint video generation method and device based on neural network

A neural network and human body technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as slow reconstruction sequences, complex equipment, and slow speed

Active Publication Date: 2021-07-09
杭州新畅元科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, high-quality, high-fidelity human body free-viewpoint video acquisition usually relies on expensive laser scanners or multi-camera array systems to model the human body. Although the effect is more realistic, there are also some obvious shortcomings: first, T

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  • Dynamic human body free viewpoint video generation method and device based on neural network
  • Dynamic human body free viewpoint video generation method and device based on neural network

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Embodiment Construction

[0053] Embodiments of the present application are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary, and are intended to explain the present application, and should not be construed as limiting the present application.

[0054] The method and device for generating a dynamic human body free-viewpoint video based on a neural network according to an embodiment of the present application will be described below with reference to the accompanying drawings.

[0055] figure 1 It is a schematic flowchart of a method for generating a dynamic human body free-viewpoint video based on a neural network provided by an embodiment of the present application.

[0056] Specifically, this application proposes a neural network-based method for generating free-...

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Abstract

The invention provides a method and device for generating a dynamic human body free viewpoint video based on a neural network, and relates to the technical field of computer vision and computer graphics, and the method comprises the steps: reconstructing a pre-scanning model of a single human body; shooting the single human body through a plurality of RGB cameras to obtain an RGB image sequence; the pre-scanning model being deformed, so that the deformed pre-scanning model is matched with each frame of RGB image; sampling the pre-scanning model, defining a hidden code at each sampling point, and jointly optimizing the hidden codes and network parameters based on a neural network; and obtaining any rendering view angle, and generating a free viewpoint video based on the any rendering view angle. Therefore, the RGB image sequence is captured based on the plurality of RGB cameras, and the free viewpoint video with continuous and dynamic time domain is generated according to the sequence, so that a more real and dynamic rendering result is generated.

Description

technical field [0001] The present application relates to the technical fields of computer vision and computer graphics, and in particular to a method and device for generating a dynamic human body free-viewpoint video based on a neural network. Background technique [0002] Free-viewpoint video generation of dynamic human body is an important problem in the field of computer graphics and computer vision. High-quality human body free-viewpoint video has broad application prospects and important application value in the fields of film and television entertainment and human body digitization. However, high-quality, high-fidelity human body free-viewpoint video acquisition usually relies on expensive laser scanners or multi-camera array systems to model the human body. Although the effect is more realistic, there are also some obvious shortcomings: first, The equipment is complicated, and these methods often require the construction of multi-camera arrays; second, the speed is...

Claims

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Application Information

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IPC IPC(8): G06T7/13G06T7/181G06T7/90G06N3/04G06N3/08
CPCG06T7/13G06T7/181G06T7/90G06N3/04G06N3/08G06T2207/10016G06T2207/20081G06T2207/30196
Inventor 刘烨斌李哲于涛
Owner 杭州新畅元科技有限公司
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