Learning Topology Uniformed Face Mesh by Volume Rendering for Multi-view Reconstruction

• URL: http://arxiv.org/abs/2404.05606v1
• Date: Mon, 8 Apr 2024 15:25:50 GMT
• Title: Learning Topology Uniformed Face Mesh by Volume Rendering for Multi-view Reconstruction
• Authors: Yating Wang, Ran Yi, Ke Fan, Jinkun Hao, Jiangbo Lu, Lizhuang Ma,
• Abstract summary: Face meshes in consistent topology serve as the foundation for many face-related applications. We propose a mesh volume rendering method that enables directly optimizing mesh geometry while preserving topology. Key innovation lies in spreading sparse mesh features into the surrounding space to simulate radiance field required for volume rendering.
• Score: 40.45683488053611
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face meshes in consistent topology serve as the foundation for many face-related applications, such as 3DMM constrained face reconstruction and expression retargeting. Traditional methods commonly acquire topology uniformed face meshes by two separate steps: multi-view stereo (MVS) to reconstruct shapes followed by non-rigid registration to align topology, but struggles with handling noise and non-lambertian surfaces. Recently neural volume rendering techniques have been rapidly evolved and shown great advantages in 3D reconstruction or novel view synthesis. Our goal is to leverage the superiority of neural volume rendering into multi-view reconstruction of face mesh with consistent topology. We propose a mesh volume rendering method that enables directly optimizing mesh geometry while preserving topology, and learning implicit features to model complex facial appearance from multi-view images. The key innovation lies in spreading sparse mesh features into the surrounding space to simulate radiance field required for volume rendering, which facilitates backpropagation of gradients from images to mesh geometry and implicit appearance features. Our proposed feature spreading module exhibits deformation invariance, enabling photorealistic rendering seamlessly after mesh editing. We conduct experiments on multi-view face image dataset to evaluate the reconstruction and implement an application for photorealistic rendering of animated face mesh.

Related papers

• GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement [51.97726804507328]
  We propose a novel approach for 3D mesh reconstruction from multi-view images. Our method takes inspiration from large reconstruction models that use a transformer-based triplane generator and a Neural Radiance Field (NeRF) model trained on multi-view images.
  arXiv  Detail & Related papers  (2024-06-09T05:19:24Z)
• 3D Facial Expressions through Analysis-by-Neural-Synthesis [30.2749903946587]
  SMIRK (Spatial Modeling for Image-based Reconstruction of Kinesics) faithfully reconstructs expressive 3D faces from images. We identify two key limitations in existing methods: shortcomings in their self-supervised training formulation, and a lack of expression diversity in the training images. Our qualitative, quantitative and particularly our perceptual evaluations demonstrate that SMIRK achieves the new state-of-the art performance on accurate expression reconstruction.
  arXiv  Detail & Related papers  (2024-04-05T14:00:07Z)
• Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement [78.48648360358193]
  We present a novel framework that generates textured surface meshes from images. Our approach begins by efficiently initializing the geometry and view-dependency appearance with a NeRF. We jointly refine the appearance with geometry and bake it into texture images for real-time rendering.
  arXiv  Detail & Related papers  (2023-03-03T17:14:44Z)
• MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction [72.05649682685197]
  State-of-the-art neural implicit methods allow for high-quality reconstructions of simple scenes from many input views. This is caused primarily by the inherent ambiguity in the RGB reconstruction loss that does not provide enough constraints. Motivated by recent advances in the area of monocular geometry prediction, we explore the utility these cues provide for improving neural implicit surface reconstruction.
  arXiv  Detail & Related papers  (2022-06-01T17:58:15Z)
• 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)
• Implicit Neural Deformation for Multi-View Face Reconstruction [43.88676778013593]
  We present a new method for 3D face reconstruction from multi-view RGB images. Unlike previous methods which are built upon 3D morphable models, our method leverages an implicit representation to encode rich geometric features. Our experimental results on several benchmark datasets demonstrate that our approach outperforms alternative baselines and achieves superior face reconstruction results compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-05T07:02:53Z)
• Topologically Consistent Multi-View Face Inference Using Volumetric Sampling [25.001398662643986]
  ToFu is a geometry inference framework that can produce topologically consistent meshes across identities and expressions. A novel progressive mesh generation network embeds the topological structure of the face in a feature volume. These high-quality assets are readily usable by production studios for avatar creation, animation and physically-based skin rendering.
  arXiv  Detail & Related papers  (2021-10-06T17:55:08Z)
• Inverting Generative Adversarial Renderer for Face Reconstruction [58.45125455811038]
  In this work, we introduce a novel Generative Adversa Renderer (GAR) GAR learns to model the complicated real-world image, instead of relying on the graphics rules, it is capable of producing realistic images. Our method achieves state-of-the-art performances on multiple face reconstruction.
  arXiv  Detail & Related papers  (2021-05-06T04:16:06Z)

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.