Explicifying Neural Implicit Fields for Efficient Dynamic Human Avatar Modeling via a Neural Explicit Surface

Ruiqi Zhang, Jie Chen*, Qiang Wang

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

This paper proposes a technique for efficiently modeling dynamic humans by explicifying the implicit neural fields via a Neural Explicit Surface (NES). Implicit neural fields have advantages over traditional explicit representations in modeling dynamic 3D content from sparse observations and effectively representing complex geometries and appearances. Implicit neural fields defined in 3D space, however, are expensive to render due to the need for dense sampling during volumetric rendering. Moreover, their memory efficiency can be further optimized when modeling sparse 3D space. To overcome these issues, the paper proposes utilizing Neural Explicit Surface (NES) to explicitly represent implicit neural fields, facilitating memory and computational efficiency. To achieve this, the paper creates a fully differentiable conversion between the implicit neural fields and the explicit rendering interface of NES, leveraging the strengths of both implicit and explicit approaches. This conversion enables effective training of the hybrid representation using implicit methods and efficient rendering by integrating the explicit rendering interface with a newly proposed rasterization-based neural renderer that only incurs a texture color query once for the initial ray interaction with the explicit surface, resulting in improved inference efficiency. NES describes dynamic human geometries with pose-dependent neural implicit surface deformation fields and their dynamic neural textures both in 2D space, which is a more memory-efficient alternative to traditional 3D methods, reducing redundancy and computational load. The comprehensive experiments show that NES performs similarly to previous 3D approaches, with greatly improved rendering speed and reduced memory cost.

Original languageEnglish
Title of host publicationMM 2023 - Proceedings of the 31st ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery (ACM)
Pages1955-1963
Number of pages9
ISBN (Print)9798400701085
DOIs
Publication statusPublished - 27 Oct 2023
Event31st ACM International Conference on Multimedia, MM 2023 - Ottawa, Canada
Duration: 29 Oct 20233 Nov 2023
https://dl.acm.org/doi/proceedings/10.1145/3581783 (Conference proceedings)
https://www.acmmm2023.org/ (Conference website)

Publication series

NameProceedings of the ACM International Conference on Multimedia

Conference

Conference31st ACM International Conference on Multimedia, MM 2023
Country/TerritoryCanada
CityOttawa
Period29/10/233/11/23
Internet address

Scopus Subject Areas

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software

User-Defined Keywords

  • dynamic human modelling
  • neural fields

Fingerprint

Dive into the research topics of 'Explicifying Neural Implicit Fields for Efficient Dynamic Human Avatar Modeling via a Neural Explicit Surface'. Together they form a unique fingerprint.

Cite this