Articulated 3D Head Avatar Generation using Text-to-Image Diffusion Models

• URL: http://arxiv.org/abs/2307.04859v1
• Date: Mon, 10 Jul 2023 19:15:32 GMT
• Title: Articulated 3D Head Avatar Generation using Text-to-Image Diffusion Models
• Authors: Alexander W. Bergman, Wang Yifan, Gordon Wetzstein
• Abstract summary: The ability to generate diverse 3D articulated head avatars is vital to a plethora of applications, including augmented reality, cinematography, and education. Recent work on text-guided 3D object generation has shown great promise in addressing these needs. We show that our diffusion-based articulated head avatars outperform state-of-the-art approaches for this task.
• Score: 107.84324544272481
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability to generate diverse 3D articulated head avatars is vital to a plethora of applications, including augmented reality, cinematography, and education. Recent work on text-guided 3D object generation has shown great promise in addressing these needs. These methods directly leverage pre-trained 2D text-to-image diffusion models to generate 3D-multi-view-consistent radiance fields of generic objects. However, due to the lack of geometry and texture priors, these methods have limited control over the generated 3D objects, making it difficult to operate inside a specific domain, e.g., human heads. In this work, we develop a new approach to text-guided 3D head avatar generation to address this limitation. Our framework directly operates on the geometry and texture of an articulable 3D morphable model (3DMM) of a head, and introduces novel optimization procedures to update the geometry and texture while keeping the 2D and 3D facial features aligned. The result is a 3D head avatar that is consistent with the text description and can be readily articulated using the deformation model of the 3DMM. We show that our diffusion-based articulated head avatars outperform state-of-the-art approaches for this task. The latter are typically based on CLIP, which is known to provide limited diversity of generation and accuracy for 3D object generation.

Related papers

• Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior [57.986512832738704]
  We present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model. Specifically, we demonstrate that high-quality and diverse 3D geometry can be guaranteed by keypoints supervision through a sparse ray sampling approach. These two decoupled designs effectively harness 3D information from reference objects to generate 3D objects while preserving the generation quality of the 2D diffusion model.
  arXiv  Detail & Related papers  (2024-03-14T07:39:59Z)
• En3D: An Enhanced Generative Model for Sculpting 3D Humans from 2D Synthetic Data [36.51674664590734]
  We present En3D, an enhanced izable scheme for high-qualityd 3D human avatars. Unlike previous works that rely on scarce 3D datasets or limited 2D collections with imbalance viewing angles and pose priors, our approach aims to develop a zero-shot 3D capable of producing 3D humans.
  arXiv  Detail & Related papers  (2024-01-02T12:06:31Z)
• HeadSculpt: Crafting 3D Head Avatars with Text [143.14548696613886]
  We introduce a versatile pipeline dubbed HeadSculpt for crafting 3D head avatars from textual prompts. We first equip the diffusion model with 3D awareness by leveraging landmark-based control and a learned textual embedding. We propose a novel identity-aware editing score distillation strategy to optimize a textured mesh with a high-resolution differentiable rendering technique.
  arXiv  Detail & Related papers  (2023-06-05T16:53:58Z)
• AG3D: Learning to Generate 3D Avatars from 2D Image Collections [96.28021214088746]
  We propose a new adversarial generative model of realistic 3D people from 2D images. Our method captures shape and deformation of the body and loose clothing by adopting a holistic 3D generator. We experimentally find that our method outperforms previous 3D- and articulation-aware methods in terms of geometry and appearance.
  arXiv  Detail & Related papers  (2023-05-03T17:56:24Z)
• DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Models [55.71306021041785]
  We present DreamAvatar, a text-and-shape guided framework for generating high-quality 3D human avatars. We leverage the SMPL model to provide shape and pose guidance for the generation. We also jointly optimize the losses computed from the full body and from the zoomed-in 3D head to alleviate the common multi-face ''Janus'' problem.
  arXiv  Detail & Related papers  (2023-04-03T12:11:51Z)
• 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)
• DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars [92.37436369781692]
  We present DRaCoN, a framework for learning full-body volumetric avatars. It exploits the advantages of both the 2D and 3D neural rendering techniques. Experiments on the challenging ZJU-MoCap and Human3.6M datasets indicate that DRaCoN outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T17:59:15Z)

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.