ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations

• URL: http://arxiv.org/abs/2203.14510v1
• Date: Mon, 28 Mar 2022 05:37:59 GMT
• Title: ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations
• Authors: Mingwu Zheng, Hongyu Yang, Di Huang, Liming Chen
• Abstract summary: This paper presents a novel 3D morphable face model, namely ImFace, to learn a nonlinear and continuous space with implicit neural representations. It builds two explicitly disentangled deformation fields to model complex shapes associated with identities and expressions, respectively, and designs an improved learning strategy to extend embeddings of expressions. In addition to ImFace, an effective preprocessing pipeline is proposed to address the issue of watertight input requirement in implicit representations.
• Score: 21.389170615787368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precise representations of 3D faces are beneficial to various computer vision and graphics applications. Due to the data discretization and model linearity, however, it remains challenging to capture accurate identity and expression clues in current studies. This paper presents a novel 3D morphable face model, namely ImFace, to learn a nonlinear and continuous space with implicit neural representations. It builds two explicitly disentangled deformation fields to model complex shapes associated with identities and expressions, respectively, and designs an improved learning strategy to extend embeddings of expressions to allow more diverse changes. We further introduce a Neural Blend-Field to learn sophisticated details by adaptively blending a series of local fields. In addition to ImFace, an effective preprocessing pipeline is proposed to address the issue of watertight input requirement in implicit representations, enabling them to work with common facial surfaces for the first time. Extensive experiments are performed to demonstrate the superiority of ImFace.

Related papers

• ImFace++: A Sophisticated Nonlinear 3D Morphable Face Model with Implicit Neural Representations [25.016000421755162]
  This paper presents a novel 3D morphable face model, named ImFace++, to learn a sophisticated and continuous space with implicit neural representations. ImFace++ first constructs two explicitly disentangled deformation fields to model complex shapes associated with identities and expressions. A refinement displacement field within the template space is further incorporated, enabling fine-grained learning of individual-specific facial details.
  arXiv  Detail & Related papers  (2023-12-07T03:53:53Z)
• GaFET: Learning Geometry-aware Facial Expression Translation from In-The-Wild Images [55.431697263581626]
  We introduce a novel Geometry-aware Facial Expression Translation framework, which is based on parametric 3D facial representations and can stably decoupled expression. We achieve higher-quality and more accurate facial expression transfer results compared to state-of-the-art methods, and demonstrate applicability of various poses and complex textures.
  arXiv  Detail & Related papers  (2023-08-07T09:03:35Z)
• BlendFields: Few-Shot Example-Driven Facial Modeling [35.86727715239676]
  We introduce a method that bridges the gap by drawing inspiration from traditional computer graphics techniques. Unseen expressions are modeled by blending appearance from a sparse set of extreme poses. We show that our method generalizes to unseen expressions, adding fine-grained effects on top of smooth volumetric deformations of a face, and demonstrate how it generalizes beyond faces.
  arXiv  Detail & Related papers  (2023-05-12T14:30:07Z)
• Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization [91.52882218901627]
  We propose a novel method for constructing implicit 3D morphable face models that are both generalizable and intuitive for editing. Our method improves upon photo-realism, geometry, and expression accuracy compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-04T17:58:40Z)
• One-Shot High-Fidelity Talking-Head Synthesis with Deformable Neural Radiance Field [81.07651217942679]
  Talking head generation aims to generate faces that maintain the identity information of the source image and imitate the motion of the driving image. We propose HiDe-NeRF, which achieves high-fidelity and free-view talking-head synthesis.
  arXiv  Detail & Related papers  (2023-04-11T09:47:35Z)
• 3DMM-RF: Convolutional Radiance Fields for 3D Face Modeling [111.98096975078158]
  We introduce a style-based generative network that synthesizes in one pass all and only the required rendering samples of a neural radiance field. We show that this model can accurately be fit to "in-the-wild" facial images of arbitrary pose and illumination, extract the facial characteristics, and be used to re-render the face in controllable conditions.
  arXiv  Detail & Related papers  (2022-09-15T15:28:45Z)
• Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance [63.13801759915835]
  3D face modeling has been an active area of research in computer vision and computer graphics. This paper proposes a new 3D face generative model that can decouple identity and expression.
  arXiv  Detail & Related papers  (2022-08-30T13:40:48Z)
• Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection [65.92058628082322]
  Non-parametric face modeling aims to reconstruct 3D face only from images without shape assumptions. This paper presents a novel Learning to Aggregate and Personalize framework for unsupervised robust 3D face modeling.
  arXiv  Detail & Related papers  (2021-06-15T03:10:17Z)
• Personalized Face Modeling for Improved Face Reconstruction and Motion Retargeting [22.24046752858929]
  We propose an end-to-end framework that jointly learns a personalized face model per user and per-frame facial motion parameters. Specifically, we learn user-specific expression blendshapes and dynamic (expression-specific) albedo maps by predicting personalized corrections. Experimental results show that our personalization accurately captures fine-grained facial dynamics in a wide range of conditions.
  arXiv  Detail & Related papers  (2020-07-14T01:30:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.