NeuWigs: A Neural Dynamic Model for Volumetric Hair Capture and Animation

• URL: http://arxiv.org/abs/2212.00613v3
• Date: Thu, 12 Oct 2023 00:27:09 GMT
• Title: NeuWigs: A Neural Dynamic Model for Volumetric Hair Capture and Animation
• Authors: Ziyan Wang, Giljoo Nam, Tuur Stuyck, Stephen Lombardi, Chen Cao, Jason Saragih, Michael Zollhoefer, Jessica Hodgins and Christoph Lassner
• Abstract summary: The capture and animation of human hair are two of the major challenges in the creation of realistic avatars for the virtual reality. We present a two-stage approach that models hair independently from the head to address these challenges in a data-driven manner. Our model outperforms the state of the art in novel view synthesis and is capable of creating novel hair animations without having to rely on hair observations as a driving signal.
• Score: 23.625243364572867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The capture and animation of human hair are two of the major challenges in the creation of realistic avatars for the virtual reality. Both problems are highly challenging, because hair has complex geometry and appearance, as well as exhibits challenging motion. In this paper, we present a two-stage approach that models hair independently from the head to address these challenges in a data-driven manner. The first stage, state compression, learns a low-dimensional latent space of 3D hair states containing motion and appearance, via a novel autoencoder-as-a-tracker strategy. To better disentangle the hair and head in appearance learning, we employ multi-view hair segmentation masks in combination with a differentiable volumetric renderer. The second stage learns a novel hair dynamics model that performs temporal hair transfer based on the discovered latent codes. To enforce higher stability while driving our dynamics model, we employ the 3D point-cloud autoencoder from the compression stage for de-noising of the hair state. Our model outperforms the state of the art in novel view synthesis and is capable of creating novel hair animations without having to rely on hair observations as a driving signal. Project page is here https://ziyanw1.github.io/neuwigs/.

Related papers

• Head360: Learning a Parametric 3D Full-Head for Free-View Synthesis in 360° [25.86740659962933]
  We build a dataset of artist-designed high-fidelity human heads and propose to create a novel parametric head model from it. Our scheme decouples the facial motion/shape and facial appearance, which are represented by a classic parametric 3D mesh model and an attached neural texture. Experiments show that facial motions and appearances are well disentangled in the parametric space, leading to SOTA performance in rendering and animating quality.
  arXiv  Detail & Related papers  (2024-08-01T05:46:06Z)
• MonoHair: High-Fidelity Hair Modeling from a Monocular Video [40.27026803872373]
  MonoHair is a generic framework to achieve high-fidelity hair reconstruction from a monocular video. Our approach bifurcates the hair modeling process into two main stages: precise exterior reconstruction and interior structure inference. Our experiments demonstrate that our method exhibits robustness across diverse hairstyles and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-03-27T08:48:47Z)
• HAAR: Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles [85.12672855502517]
  We present HAAR, a new strand-based generative model for 3D human hairstyles. Based on textual inputs, HAAR produces 3D hairstyles that could be used as production-level assets in modern computer graphics engines.
  arXiv  Detail & Related papers  (2023-12-18T19:19:32Z)
• GaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video via Animatable 3D Gaussians [51.46168990249278]
  We present an efficient approach to creating realistic human avatars with dynamic 3D appearances from a single video. GustafAvatar is validated on both the public dataset and our collected dataset.
  arXiv  Detail & Related papers  (2023-12-04T18:55:45Z)
• Learning Disentangled Avatars with Hybrid 3D Representations [102.9632315060652]
  We present Disentangled Avatars(DELTA) which models humans with hybrid explicit-implicit 3D representations. We consider the disentanglement of the human body and clothing and in the second, we disentangle the face and hair. We show how these two applications can be easily combined to model full-body avatars.
  arXiv  Detail & Related papers  (2023-09-12T17:59:36Z)
• HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling [55.57803336895614]
  We tackle the challenging problem of learning-based single-view 3D hair modeling. We first propose a novel intermediate representation, termed as HairStep, which consists of a strand map and a depth map. It is found that HairStep not only provides sufficient information for accurate 3D hair modeling, but also is feasible to be inferred from real images.
  arXiv  Detail & Related papers  (2023-03-05T15:28:13Z)
• Controllable Radiance Fields for Dynamic Face Synthesis [125.48602100893845]
  We study how to explicitly control generative model synthesis of face dynamics exhibiting non-rigid motion. Controllable Radiance Field (CoRF) On head image/video data we show that CoRFs are 3D-aware while enabling editing of identity, viewing directions, and motion.
  arXiv  Detail & Related papers  (2022-10-11T23:17:31Z)
• HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture [11.645769995924548]
  Capturing and rendering life-like hair is particularly challenging due to its fine geometric structure, the complex physical interaction and its non-trivial visual appearance. In this paper, we use a novel, volumetric hair representation that is com-posed of thousands of primitives. Our method can not only create realistic renders of recorded multi-view sequences, but also create renderings for new hair configurations by providing new control signals.
  arXiv  Detail & Related papers  (2021-12-13T18:57:50Z)
• Real-time Deep Dynamic Characters [95.5592405831368]
  We propose a deep videorealistic 3D human character model displaying highly realistic shape, motion, and dynamic appearance. We use a novel graph convolutional network architecture to enable motion-dependent deformation learning of body and clothing. We show that our model creates motion-dependent surface deformations, physically plausible dynamic clothing deformations, as well as video-realistic surface textures at a much higher level of detail than previous state of the art approaches.
  arXiv  Detail & Related papers  (2021-05-04T23:28:55Z)

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.