Analyzing and Improving the Skin Tone Consistency and Bias in Implicit 3D Relightable Face Generators

• URL: http://arxiv.org/abs/2411.12002v1
• Date: Mon, 18 Nov 2024 19:38:34 GMT
• Title: Analyzing and Improving the Skin Tone Consistency and Bias in Implicit 3D Relightable Face Generators
• Authors: Libing Zeng, Nima Khademi Kalantari,
• Abstract summary: We propose a strategy to mitigate the problem of biased albedo images with lighter skin tones. We also propose a scaling strategy to match the distribution of illumination magnitude in the generated images with the training data.
• Score: 6.175211259659976
• License:
• Abstract: With the advances in generative adversarial networks (GANs) and neural rendering, 3D relightable face generation has received significant attention. Among the existing methods, a particularly successful technique uses an implicit lighting representation and generates relit images through the product of synthesized albedo and light-dependent shading images. While this approach produces high-quality results with intricate shading details, it often has difficulty producing relit images with consistent skin tones, particularly when the lighting condition is extracted from images of individuals with dark skin. Additionally, this technique is biased towards producing albedo images with lighter skin tones. Our main observation is that this problem is rooted in the biased spherical harmonics (SH) coefficients, used during training. Following this observation, we conduct an analysis and demonstrate that the bias appears not only in band 0 (DC term), but also in the other bands of the estimated SH coefficients. We then propose a simple, but effective, strategy to mitigate the problem. Specifically, we normalize the SH coefficients by their DC term to eliminate the inherent magnitude bias, while statistically align the coefficients in the other bands to alleviate the directional bias. We also propose a scaling strategy to match the distribution of illumination magnitude in the generated images with the training data. Through extensive experiments, we demonstrate the effectiveness of our solution in increasing the skin tone consistency and mitigating bias.

Related papers

• Flash Cache: Reducing Bias in Radiance Cache Based Inverse Rendering [62.92985004295714]
  We present a method that avoids approximations that introduce bias into the renderings and, more importantly, the gradients used for optimization. We show that by removing these biases our approach improves the generality of radiance cache based inverse rendering, as well as increasing quality in the presence of challenging light transport effects such as specular reflections.
  arXiv  Detail & Related papers  (2024-09-09T17:59:57Z)
• 3D Gaussian Splatting with Deferred Reflection [25.254842246219585]
  We present a deferred shading method to render specular reflection with Gaussian splatting. Our method significantly outperforms state-of-the-art techniques and concurrent work in synthesizing high-quality specular reflection effects.
  arXiv  Detail & Related papers  (2024-04-29T06:24:32Z)
• Gadolinium dose reduction for brain MRI using conditional deep learning [66.99830668082234]
  Two main challenges for these approaches are the accurate prediction of contrast enhancement and the synthesis of realistic images. We address both challenges by utilizing the contrast signal encoded in the subtraction images of pre-contrast and post-contrast image pairs. We demonstrate the effectiveness of our approach on synthetic and real datasets using various scanners, field strengths, and contrast agents.
  arXiv  Detail & Related papers  (2024-03-06T08:35:29Z)
• Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement [16.35696535606822]
  BDCE exploits the learning of the distribution of the curve parameters instead of the normal-light image itself. It achieves state-of-the-art qualitative and quantitative performance.
  arXiv  Detail & Related papers  (2023-09-26T07:04:47Z)
• Gradpaint: Gradient-Guided Inpainting with Diffusion Models [71.47496445507862]
  Denoising Diffusion Probabilistic Models (DDPMs) have recently achieved remarkable results in conditional and unconditional image generation. We present GradPaint, which steers the generation towards a globally coherent image. We generalizes well to diffusion models trained on various datasets, improving upon current state-of-the-art supervised and unsupervised methods.
  arXiv  Detail & Related papers  (2023-09-18T09:36:24Z)
• ExposureDiffusion: Learning to Expose for Low-light Image Enhancement [87.08496758469835]
  This work addresses the issue by seamlessly integrating a diffusion model with a physics-based exposure model. Our method obtains significantly improved performance and reduced inference time compared with vanilla diffusion models. The proposed framework can work with both real-paired datasets, SOTA noise models, and different backbone networks.
  arXiv  Detail & Related papers  (2023-07-15T04:48:35Z)
• Deep Uncalibrated Photometric Stereo via Inter-Intra Image Feature Fusion [17.686973510425172]
  This paper presents a new method for deep uncalibrated photometric stereo. It efficiently utilizes the inter-image representation to guide the normal estimation. Our method produces significantly better results than the state-of-the-art methods on both synthetic and real data.
  arXiv  Detail & Related papers  (2022-08-06T03:59:54Z)
• Low-Light Image Enhancement with Normalizing Flow [92.52290821418778]
  In this paper, we investigate to model this one-to-many relationship via a proposed normalizing flow model. An invertible network that takes the low-light images/features as the condition and learns to map the distribution of normally exposed images into a Gaussian distribution. The experimental results on the existing benchmark datasets show our method achieves better quantitative and qualitative results, obtaining better-exposed illumination, less noise and artifact, and richer colors.
  arXiv  Detail & Related papers  (2021-09-13T12:45:08Z)
• Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
  Blind image restoration is a common yet challenging problem in computer vision. We propose a novel blind image restoration method, aiming to integrate both the advantages of them. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
  arXiv  Detail & Related papers  (2020-08-25T03:30:53Z)
• Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser [7.7288480250888]
  We develop a robust and general methodology for making use of implicit priors in deep neural networks. A CNN trained to perform blind (i.e., with unknown noise level) least-squares denoising is presented. A generalization of this algorithm to constrained sampling provides a method for using the implicit prior to solve any linear inverse problem.
  arXiv  Detail & Related papers  (2020-07-27T15:40:46Z)

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.