3DMM-RF: Convolutional Radiance Fields for 3D Face Modeling

• URL: http://arxiv.org/abs/2209.07366v1
• Date: Thu, 15 Sep 2022 15:28:45 GMT
• Title: 3DMM-RF: Convolutional Radiance Fields for 3D Face Modeling
• Authors: Stathis Galanakis, Baris Gecer, Alexandros Lattas, Stefanos Zafeiriou
• Abstract summary: 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.
• Score: 111.98096975078158
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Facial 3D Morphable Models are a main computer vision subject with countless applications and have been highly optimized in the last two decades. The tremendous improvements of deep generative networks have created various possibilities for improving such models and have attracted wide interest. Moreover, the recent advances in neural radiance fields, are revolutionising novel-view synthesis of known scenes. In this work, we present a facial 3D Morphable Model, which exploits both of the above, and can accurately model a subject's identity, pose and expression and render it in arbitrary illumination. This is achieved by utilizing a powerful deep style-based generator to overcome two main weaknesses of neural radiance fields, their rigidity and rendering speed. 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 create a vast labelled synthetic dataset of facial renders, and train the network on these data, so that it can accurately model and generalize on facial identity, pose and appearance. Finally, 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.

Related papers

• Cafca: High-quality Novel View Synthesis of Expressive Faces from Casual Few-shot Captures [33.463245327698]
  We present a novel volumetric prior on human faces that allows for high-fidelity expressive face modeling. We leverage a 3D Morphable Face Model to synthesize a large training set, rendering each identity with different expressions. We then train a conditional Neural Radiance Field prior on this synthetic dataset and, at inference time, fine-tune the model on a very sparse set of real images of a single subject.
  arXiv  Detail & Related papers  (2024-10-01T12:24:50Z)
• FitDiff: Robust monocular 3D facial shape and reflectance estimation using Diffusion Models [79.65289816077629]
  We present FitDiff, a diffusion-based 3D facial avatar generative model. Our model accurately generates relightable facial avatars, utilizing an identity embedding extracted from an "in-the-wild" 2D facial image. Being the first 3D LDM conditioned on face recognition embeddings, FitDiff reconstructs relightable human avatars, that can be used as-is in common rendering engines.
  arXiv  Detail & Related papers  (2023-12-07T17:35:49Z)
• 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)
• 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)
• 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)
• MoFaNeRF: Morphable Facial Neural Radiance Field [12.443638713719357]
  MoFaNeRF is a parametric model that maps free-view images into a vector space coded facial shape, expression and appearance. By introducing identity-specific modulation and encoder texture, our model synthesizes accurate photometric details. Our model shows strong ability on multiple applications including image-based fitting, random generation, face rigging, face editing, and novel view.
  arXiv  Detail & Related papers  (2021-12-04T11:25:28Z)
• Normalized Avatar Synthesis Using StyleGAN and Perceptual Refinement [11.422683083130577]
  We introduce a highly robust GAN-based framework for digitizing a normalized 3D avatar of a person from a single unconstrained photo. While the input image can be of a smiling person or taken in extreme lighting conditions, our method can reliably produce a high-quality textured model of a person's face.
  arXiv  Detail & Related papers  (2021-06-21T21:57:16Z)
• OSTeC: One-Shot Texture Completion [86.23018402732748]
  We propose an unsupervised approach for one-shot 3D facial texture completion. The proposed approach rotates an input image in 3D and fill-in the unseen regions by reconstructing the rotated image in a 2D face generator. We frontalize the target image by projecting the completed texture into the generator.
  arXiv  Detail & Related papers  (2020-12-30T23:53:26Z)

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.