High-Fidelity Facial Albedo Estimation via Texture Quantization

• URL: http://arxiv.org/abs/2406.13149v1
• Date: Wed, 19 Jun 2024 01:53:30 GMT
• Title: High-Fidelity Facial Albedo Estimation via Texture Quantization
• Authors: Zimin Ran, Xingyu Ren, Xiang An, Kaicheng Yang, Xiangzi Dai, Ziyong Feng, Jia Guo, Linchao Zhu, Jiankang Deng,
• Abstract summary: We present HiFiAlbedo, which recovers the albedo map directly from a single image without the need for captured albedo data. Our method exhibits excellent generalizability and is capable of achieving high-fidelity results for in-the-wild facial albedo recovery.
• Score: 59.100759403614695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent 3D face reconstruction methods have made significant progress in shape estimation, but high-fidelity facial albedo reconstruction remains challenging. Existing methods depend on expensive light-stage captured data to learn facial albedo maps. However, a lack of diversity in subjects limits their ability to recover high-fidelity results. In this paper, we present a novel facial albedo reconstruction model, HiFiAlbedo, which recovers the albedo map directly from a single image without the need for captured albedo data. Our key insight is that the albedo map is the illumination invariant texture map, which enables us to use inexpensive texture data to derive an albedo estimation by eliminating illumination. To achieve this, we first collect large-scale ultra-high-resolution facial images and train a high-fidelity facial texture codebook. By using the FFHQ dataset and limited UV textures, we then fine-tune the encoder for texture reconstruction from the input image with adversarial supervision in both image and UV space. Finally, we train a cross-attention module and utilize group identity loss to learn the adaptation from facial texture to the albedo domain. Extensive experimentation has demonstrated that our method exhibits excellent generalizability and is capable of achieving high-fidelity results for in-the-wild facial albedo recovery. Our code, pre-trained weights, and training data will be made publicly available at https://hifialbedo.github.io/.

Related papers

• Monocular Identity-Conditioned Facial Reflectance Reconstruction [71.90507628715388]
  Existing methods rely on a large amount of light-stage captured data to learn facial reflectance models. We learn the reflectance prior in image space rather than UV space and present a framework named ID2Reflectance. Our framework can directly estimate the reflectance maps of a single image while using limited reflectance data for training.
  arXiv  Detail & Related papers  (2024-03-30T09:43:40Z)
• CLR-Face: Conditional Latent Refinement for Blind Face Restoration Using Score-Based Diffusion Models [57.9771859175664]
  Recent generative-prior-based methods have shown promising blind face restoration performance. Generating fine-grained facial details faithful to inputs remains a challenging problem. We introduce a diffusion-based-prior inside a VQGAN architecture that focuses on learning the distribution over uncorrupted latent embeddings.
  arXiv  Detail & Related papers  (2024-02-08T23:51:49Z)
• TIFace: Improving Facial Reconstruction through Tensorial Radiance Fields and Implicit Surfaces [34.090466325032686]
  This report describes the solution that secured the first place in the "View Synthesis Challenge for Human Heads" Given the sparse view images of human heads, the objective of this challenge is to synthesize images from novel viewpoints. We propose TI-Face, which improves facial reconstruction through tensorial radiance fields (T-Face) and implicit surfaces (I-Face)
  arXiv  Detail & Related papers  (2023-12-15T04:23:20Z)
• 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)
• Self-supervised High-fidelity and Re-renderable 3D Facial Reconstruction from a Single Image [19.0074836183624]
  We propose a novel self-supervised learning framework for reconstructing high-quality 3D faces from single-view images in-the-wild. Our framework substantially outperforms state-of-the-art approaches in both qualitative and quantitative comparisons.
  arXiv  Detail & Related papers  (2021-11-16T08:10:24Z)
• Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction [48.952656891182826]
  High-fidelity 3D face texture generation has yet to be studied. Model consists of a UV sampler and a UV generator. Training is based on pseudo ground truth blended by the 3DMM texture and the input face texture.
  arXiv  Detail & Related papers  (2021-06-14T12:34:35Z)
• 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)
• OSTeC: One-Shot Texture Completion [86.23018402732748]
  We propose an unsupervised approach for one-shot 3D facial texture completion. The proposed approach rotates an input image in 3D and fill-in the unseen regions by reconstructing the rotated image in a 2D face generator. We frontalize the target image by projecting the completed texture into the generator.
  arXiv  Detail & Related papers  (2020-12-30T23:53:26Z)
• Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks [32.859340851346786]
  We introduce a method to reconstruct 3D facial shapes with high-fidelity textures from single-view images in-the-wild. Our method can generate high-quality results and outperforms state-of-the-art methods in both qualitative and quantitative comparisons.
  arXiv  Detail & Related papers  (2020-03-12T08:06:04Z)

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.