FaceLift: Single Image to 3D Head with View Generation and GS-LRM

• URL: http://arxiv.org/abs/2412.17812v1
• Date: Mon, 23 Dec 2024 18:59:49 GMT
• Title: FaceLift: Single Image to 3D Head with View Generation and GS-LRM
• Authors: Weijie Lyu, Yi Zhou, Ming-Hsuan Yang, Zhixin Shu,
• Abstract summary: FaceLift is a feed-forward approach for rapid, high-quality, 360-degree head reconstruction from a single image. We show that FaceLift outperforms state-of-the-art methods in 3D head reconstruction, highlighting its practical applicability and robust performance on real-world images.
• Score: 54.24070918942727
• License:
• Abstract: We present FaceLift, a feed-forward approach for rapid, high-quality, 360-degree head reconstruction from a single image. Our pipeline begins by employing a multi-view latent diffusion model that generates consistent side and back views of the head from a single facial input. These generated views then serve as input to a GS-LRM reconstructor, which produces a comprehensive 3D representation using Gaussian splats. To train our system, we develop a dataset of multi-view renderings using synthetic 3D human head as-sets. The diffusion-based multi-view generator is trained exclusively on synthetic head images, while the GS-LRM reconstructor undergoes initial training on Objaverse followed by fine-tuning on synthetic head data. FaceLift excels at preserving identity and maintaining view consistency across views. Despite being trained solely on synthetic data, FaceLift demonstrates remarkable generalization to real-world images. Through extensive qualitative and quantitative evaluations, we show that FaceLift outperforms state-of-the-art methods in 3D head reconstruction, highlighting its practical applicability and robust performance on real-world images. In addition to single image reconstruction, FaceLift supports video inputs for 4D novel view synthesis and seamlessly integrates with 2D reanimation techniques to enable 3D facial animation. Project page: https://weijielyu.github.io/FaceLift.

Related papers

• Single Image, Any Face: Generalisable 3D Face Generation [59.9369171926757]
  We propose a novel model, Gen3D-Face, which generates 3D human faces with unconstrained single image input. To the best of our knowledge, this is the first attempt and benchmark for creating photorealistic 3D human face avatars from single images.
  arXiv  Detail & Related papers  (2024-09-25T14:56:37Z)
• G3FA: Geometry-guided GAN for Face Animation [14.488117084637631]
  We introduce Geometry-guided GAN for Face Animation (G3FA) to tackle this limitation. Our novel approach empowers the face animation model to incorporate 3D information using only 2D images. In our face reenactment model, we leverage 2D motion warping to capture motion dynamics.
  arXiv  Detail & Related papers  (2024-08-23T13:13:24Z)
• FitDiff: Robust monocular 3D facial shape and reflectance estimation using Diffusion Models [79.65289816077629]
  We present FitDiff, a diffusion-based 3D facial avatar generative model. Our model accurately generates relightable facial avatars, utilizing an identity embedding extracted from an "in-the-wild" 2D facial image. Being the first 3D LDM conditioned on face recognition embeddings, FitDiff reconstructs relightable human avatars, that can be used as-is in common rendering engines.
  arXiv  Detail & Related papers  (2023-12-07T17:35:49Z)
• SiTH: Single-view Textured Human Reconstruction with Image-Conditioned Diffusion [35.73448283467723]
  SiTH is a novel pipeline that integrates an image-conditioned diffusion model into a 3D mesh reconstruction workflow. We employ a powerful generative diffusion model to hallucinate unseen back-view appearance based on the input images. For the latter, we leverage skinned body meshes as guidance to recover full-body texture meshes from the input and back-view images.
  arXiv  Detail & Related papers  (2023-11-27T14:22:07Z)
• PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360$^{\circ}$ [17.355141949293852]
  Existing 3D generative adversarial networks (GANs) for 3D human head synthesis are either limited to near-frontal views or hard to preserve 3D consistency in large view angles. We propose PanoHead, the first 3D-aware generative model that enables high-quality view-consistent image synthesis of full heads in $360circ$ with diverse appearance and detailed geometry.
  arXiv  Detail & Related papers  (2023-03-23T06:54:34Z)
• 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)
• Image-to-Video Generation via 3D Facial Dynamics [78.01476554323179]
  We present a versatile model, FaceAnime, for various video generation tasks from still images. Our model is versatile for various AR/VR and entertainment applications, such as face video and face video prediction.
  arXiv  Detail & Related papers  (2021-05-31T02:30:11Z)
• Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction [76.1612334630256]
  We harness the power of Generative Adversarial Networks (GANs) and Deep Convolutional Neural Networks (DCNNs) to reconstruct the facial texture and shape from single images. We demonstrate excellent results in photorealistic and identity preserving 3D face reconstructions and achieve for the first time, facial texture reconstruction with high-frequency details.
  arXiv  Detail & Related papers  (2021-05-16T16:35:44Z)

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.