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

• URL: http://arxiv.org/abs/2312.04028v3
• Date: Fri, 11 Oct 2024 07:45:46 GMT
• Title: ImFace++: A Sophisticated Nonlinear 3D Morphable Face Model with Implicit Neural Representations
• Authors: Mingwu Zheng, Haiyu Zhang, Hongyu Yang, Liming Chen, Di Huang,
• Abstract summary: 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.
• Score: 25.016000421755162
• License:
• Abstract: Accurate representations of 3D faces are of paramount importance in various computer vision and graphics applications. However, the challenges persist due to the limitations imposed by data discretization and model linearity, which hinder the precise capture of identity and expression clues in current studies. 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, respectively, which simultaneously facilitate automatic learning of point-to-point correspondences across diverse facial shapes. To capture more sophisticated facial details, a refinement displacement field within the template space is further incorporated, enabling fine-grained learning of individual-specific facial details. Furthermore, a Neural Blend-Field is designed to reinforce the representation capabilities through adaptive blending of an array of local fields. In addition to ImFace++, we devise an improved learning strategy to extend expression embeddings, allowing for a broader range of expression variations. Comprehensive qualitative and quantitative evaluation demonstrates that ImFace++ significantly advances the state-of-the-art in terms of both face reconstruction fidelity and correspondence accuracy.

Related papers

• 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)
• 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)
• ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations [21.389170615787368]
  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.
  arXiv  Detail & Related papers  (2022-03-28T05:37:59Z)
• Facial Geometric Detail Recovery via Implicit Representation [147.07961322377685]
  We present a robust texture-guided geometric detail recovery approach using only a single in-the-wild facial image. Our method combines high-quality texture completion with the powerful expressiveness of implicit surfaces. Our method not only recovers accurate facial details but also decomposes normals, albedos, and shading parts in a self-supervised way.
  arXiv  Detail & Related papers  (2022-03-18T01:42:59Z)
• FaceTuneGAN: Face Autoencoder for Convolutional Expression Transfer Using Neural Generative Adversarial Networks [0.7043489166804575]
  We present FaceTuneGAN, a new 3D face model representation decomposing and encoding separately facial identity and facial expression. We propose a first adaptation of image-to-image translation networks, that have successfully been used in the 2D domain, to 3D face geometry.
  arXiv  Detail & Related papers  (2021-12-01T14:42:03Z)
• 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)
• Learning Complete 3D Morphable Face Models from Images and Videos [88.34033810328201]
  We present the first approach to learn complete 3D models of face identity geometry, albedo and expression just from images and videos. We show that our learned models better generalize and lead to higher quality image-based reconstructions than existing approaches.
  arXiv  Detail & Related papers  (2020-10-04T20:51:23Z)
• 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.