OmniAvatar: Geometry-Guided Controllable 3D Head Synthesis

• URL: http://arxiv.org/abs/2303.15539v1
• Date: Mon, 27 Mar 2023 18:36:53 GMT
• Title: OmniAvatar: Geometry-Guided Controllable 3D Head Synthesis
• Authors: Hongyi Xu, Guoxian Song, Zihang Jiang, Jianfeng Zhang, Yichun Shi, Jing Liu, Wanchun Ma, Jiashi Feng, Linjie Luo
• Abstract summary: We present OmniAvatar, a novel geometry-guided 3D head synthesis model trained from in-the-wild unstructured images. Our model can synthesize more preferable identity-preserved 3D heads with compelling dynamic details compared to the state-of-the-art methods.
• Score: 81.70922087960271
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present OmniAvatar, a novel geometry-guided 3D head synthesis model trained from in-the-wild unstructured images that is capable of synthesizing diverse identity-preserved 3D heads with compelling dynamic details under full disentangled control over camera poses, facial expressions, head shapes, articulated neck and jaw poses. To achieve such high level of disentangled control, we first explicitly define a novel semantic signed distance function (SDF) around a head geometry (FLAME) conditioned on the control parameters. This semantic SDF allows us to build a differentiable volumetric correspondence map from the observation space to a disentangled canonical space from all the control parameters. We then leverage the 3D-aware GAN framework (EG3D) to synthesize detailed shape and appearance of 3D full heads in the canonical space, followed by a volume rendering step guided by the volumetric correspondence map to output into the observation space. To ensure the control accuracy on the synthesized head shapes and expressions, we introduce a geometry prior loss to conform to head SDF and a control loss to conform to the expression code. Further, we enhance the temporal realism with dynamic details conditioned upon varying expressions and joint poses. Our model can synthesize more preferable identity-preserved 3D heads with compelling dynamic details compared to the state-of-the-art methods both qualitatively and quantitatively. We also provide an ablation study to justify many of our system design choices.

Related papers

• Dynamic Scene Understanding through Object-Centric Voxelization and Neural Rendering [57.895846642868904]
  We present a 3D generative model named DynaVol-S for dynamic scenes that enables object-centric learning. voxelization infers per-object occupancy probabilities at individual spatial locations. Our approach integrates 2D semantic features to create 3D semantic grids, representing the scene through multiple disentangled voxel grids.
  arXiv  Detail & Related papers  (2024-07-30T15:33:58Z)
• ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling [96.87575334960258]
  ID-to-3D is a method to generate identity- and text-guided 3D human heads with disentangled expressions. Results achieve an unprecedented level of identity-consistent and high-quality texture and geometry generation.
  arXiv  Detail & Related papers  (2024-05-26T13:36:45Z)
• Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos [47.94545609011594]
  We propose a method to learn a high-quality implicit 3D head avatar from a monocular RGB video captured in the wild. Our hybrid pipeline combines the geometry prior and dynamic tracking of a 3DMM with a neural radiance field to achieve fine-grained control and photorealism.
  arXiv  Detail & Related papers  (2023-04-04T01:10:04Z)
• Structured 3D Features for Reconstructing Controllable Avatars [43.36074729431982]
  We introduce Structured 3D Features, a model based on a novel implicit 3D representation that pools pixel-aligned image features onto dense 3D points sampled from a parametric, statistical human mesh surface. We show that our S3F model surpasses the previous state-of-the-art on various tasks, including monocular 3D reconstruction, as well as albedo and shading estimation.
  arXiv  Detail & Related papers  (2022-12-13T18:57:33Z)
• CGOF++: Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields [52.14985242487535]
  We propose a new conditional 3D face synthesis framework, which enables 3D controllability over generated face images. At its core is a conditional Generative Occupancy Field (cGOF++) that effectively enforces the shape of the generated face to conform to a given 3D Morphable Model (3DMM) mesh. Experiments validate the effectiveness of the proposed method and show more precise 3D controllability than state-of-the-art 2D-based controllable face synthesis methods.
  arXiv  Detail & Related papers  (2022-11-23T19:02:50Z)
• Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars [36.4402388864691]
  3D-aware generative adversarial networks (GANs) synthesize high-fidelity and multi-view-consistent facial images using only collections of single-view 2D imagery. Recent efforts incorporate 3D Morphable Face Model (3DMM) to describe deformation in generative radiance fields either explicitly or implicitly. We propose a novel 3D GAN framework for unsupervised learning of generative, high-quality and 3D-consistent facial avatars from unstructured 2D images.
  arXiv  Detail & Related papers  (2022-11-21T06:40:46Z)
• 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)
• Free-HeadGAN: Neural Talking Head Synthesis with Explicit Gaze Control [54.079327030892244]
  Free-HeadGAN is a person-generic neural talking head synthesis system. We show that modeling faces with sparse 3D facial landmarks are sufficient for achieving state-of-the-art generative performance.
  arXiv  Detail & Related papers  (2022-08-03T16:46:08Z)

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.