IDE-3D: Interactive Disentangled Editing for High-Resolution 3D-aware Portrait Synthesis

• URL: http://arxiv.org/abs/2205.15517v1
• Date: Tue, 31 May 2022 03:35:44 GMT
• Title: IDE-3D: Interactive Disentangled Editing for High-Resolution 3D-aware Portrait Synthesis
• Authors: Jingxiang Sun, Xuan Wang, Yichun Shi, Lizhen Wang, Jue Wang, Yebin Liu
• Abstract summary: Our system consists of three major components: (1) a 3D-semantics-aware generative model that produces view-consistent, disentangled face images and semantic masks; (2) a hybrid GAN inversion approach that initializes the latent codes from the semantic and texture encoder, and further optimized them for faithful reconstruction; and (3) a canonical editor that enables efficient manipulation of semantic masks in canonical view and product high-quality editing results.
• Score: 38.517819699560945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing 3D-aware facial generation methods face a dilemma in quality versus editability: they either generate editable results in low resolution or high-quality ones with no editing flexibility. In this work, we propose a new approach that brings the best of both worlds together. Our system consists of three major components: (1) a 3D-semantics-aware generative model that produces view-consistent, disentangled face images and semantic masks; (2) a hybrid GAN inversion approach that initialize the latent codes from the semantic and texture encoder, and further optimized them for faithful reconstruction; and (3) a canonical editor that enables efficient manipulation of semantic masks in canonical view and product high-quality editing results. Our approach is competent for many applications, e.g. free-view face drawing, editing, and style control. Both quantitative and qualitative results show that our method reaches the state-of-the-art in terms of photorealism, faithfulness, and efficiency.

Related papers

• Reference-Based 3D-Aware Image Editing with Triplanes [15.222454412573455]
  Generative Adversarial Networks (GANs) have emerged as powerful tools for high-quality image generation and real image editing by manipulating their latent spaces. Recent advancements in GANs include 3D-aware models such as EG3D, which feature efficient triplane-based architectures capable of reconstructing 3D geometry from single images. This study addresses this gap by exploring and demonstrating the effectiveness of the triplane space for advanced reference-based edits.
  arXiv  Detail & Related papers  (2024-04-04T17:53:33Z)
• Diffusion Models are Geometry Critics: Single Image 3D Editing Using Pre-Trained Diffusion Priors [24.478875248825563]
  We propose a novel image editing technique that enables 3D manipulations on single images. Our method directly leverages powerful image diffusion models trained on a broad spectrum of text-image pairs. Our method can generate high-quality 3D-aware image edits with large viewpoint transformations and high appearance and shape consistency with the input image.
  arXiv  Detail & Related papers  (2024-03-18T06:18:59Z)
• Guide3D: Create 3D Avatars from Text and Image Guidance [55.71306021041785]
  Guide3D is a text-and-image-guided generative model for 3D avatar generation based on diffusion models. Our framework produces topologically and structurally correct geometry and high-resolution textures.
  arXiv  Detail & Related papers  (2023-08-18T17:55:47Z)
• In-N-Out: Faithful 3D GAN Inversion with Volumetric Decomposition for Face Editing [28.790900756506833]
  3D-aware GANs offer new capabilities for view synthesis while preserving the editing functionalities of their 2D counterparts. GAN inversion is a crucial step that seeks the latent code to reconstruct input images or videos, subsequently enabling diverse editing tasks through manipulation of this latent code. We address this issue by explicitly modeling OOD objects from the input in 3D-aware GANs.
  arXiv  Detail & Related papers  (2023-02-09T18:59:56Z)
• High-fidelity 3D GAN Inversion by Pseudo-multi-view Optimization [51.878078860524795]
  We present a high-fidelity 3D generative adversarial network (GAN) inversion framework that can synthesize photo-realistic novel views. Our approach enables high-fidelity 3D rendering from a single image, which is promising for various applications of AI-generated 3D content.
  arXiv  Detail & Related papers  (2022-11-28T18:59:52Z)
• 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)
• 3D-FM GAN: Towards 3D-Controllable Face Manipulation [43.99393180444706]
  3D-FM GAN is a novel conditional GAN framework designed specifically for 3D-controllable face manipulation. By carefully encoding both the input face image and a physically-based rendering of 3D edits into a StyleGAN's latent spaces, our image generator provides high-quality, identity-preserved, 3D-controllable face manipulation. We show that our method outperforms the prior arts on various tasks, with better editability, stronger identity preservation, and higher photo-realism.
  arXiv  Detail & Related papers  (2022-08-24T01:33:13Z)
• Efficient Geometry-aware 3D Generative Adversarial Networks [50.68436093869381]
  Existing 3D GANs are either compute-intensive or make approximations that are not 3D-consistent. In this work, we improve the computational efficiency and image quality of 3D GANs without overly relying on these approximations. We introduce an expressive hybrid explicit-implicit network architecture that synthesizes not only high-resolution multi-view-consistent images in real time but also produces high-quality 3D geometry.
  arXiv  Detail & Related papers  (2021-12-15T08:01:43Z)
• Towards Realistic 3D Embedding via View Alignment [53.89445873577063]
  This paper presents an innovative View Alignment GAN (VA-GAN) that composes new images by embedding 3D models into 2D background images realistically and automatically. VA-GAN consists of a texture generator and a differential discriminator that are inter-connected and end-to-end trainable.
  arXiv  Detail & Related papers  (2020-07-14T14:45:00Z)

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.