Weight, Sex, and Facial Expressions: On the Manipulation of Attributes in Generative 3D Face Models

Amberg, Brian; Paysan, Pascal; Vetter, Thomas

doi:10.1007/978-3-642-10331-5_81

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…There has been significant work in modifying faces along other axes or attributes, such as gender [15], age [17,23], facial expressions [1] and attractiveness [18]. However, while these works focus on intuitive and describable attributes, our work focuses on a property that is yet not well understood.…”

Section: Related Worksupporting

confidence: 44%

Modifying the Memorability of Face Photographs

Khosla

Bainbridge

Torralba

et al. 2013

2013 IEEE International Conference on Computer Vision

100

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Contemporary life bombards us with many new images of faces every day, which poses non-trivial constraints on human memory. The vast majority of face photographs are intended to be remembered, either because of personal relevance, commercial interests or because the pictures were deliberately designed to be memorable. Can we make a portrait more memorable or more forgettable automatically? Here, we provide a method to modify the memorability of individual face photographs, while keeping the identity and other facial traits (e.g. age, attractiveness, and emotional magnitude) of the individual fixed. We show that face photographs manipulated to be more memorable (or more forgettable) are indeed more often remembered (or forgotten) in a crowd-sourcing experiment with an accuracy of 74%. Quantifying and modifying the 'memorability' of a face lends itself to many useful applications in computer vision and graphics, such as mnemonic aids for learning, photo editing applications for social networks and tools for designing memorable advertisements. Additional information, data and supplemental material is available at: http://facemem

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Section: Related Worksupporting

confidence: 44%

Modifying the Memorability of Face Photographs

Khosla

Bainbridge

Torralba

et al. 2013

2013 IEEE International Conference on Computer Vision

100

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“…Even though some facial attributes are encoded in the model parameters [1], the analysis of them has have not yet been explored. We show the limitations using the model parameters for attribute classification.…”

Section: Prior Workmentioning

confidence: 47%

Pose Normalization for Eye Gaze Estimation and Facial Attribute Description from Still Images

Egger

Schönborn

Förster

et al. 2014

Lecture Notes in Computer Science

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Abstract. Our goal is to obtain an eye gaze estimation and a face description based on attributes (e.g. glasses, beard or thick lips) from still images. An attribute-based face description reflects human vocabulary and is therefore adequate as face description. Head pose and eye gaze play an important role in human interaction and are a key element to extract interaction information from still images. Pose variation is a major challenge when analyzing them. Most current approaches for facial image analysis are not explicitly pose-invariant. To obtain a poseinvariant representation, we have to account the three dimensional nature of a face. A 3D Morphable Model (3DMM) of faces is used to obtain a dense 3D reconstruction of the face in the image. This Analysis-bySynthesis approach provides model parameters which contain an explicit face description and a dense model to image correspondence. However, the fit is restricted to the model space and cannot explain all variations. Our model only contains straight gaze directions and lacks high detail textural features. To overcome this limitations, we use the obtained correspondence in a discriminative approach. The dense correspondence is used to extract a pose-normalized version of the input image. The warped image contains all information from the original image and preserves gaze and detailed textural information. On the pose-normalized representation we train a regression function to obtain gaze estimation and attribute description. We provide results for pose-invariant gaze estimation on still images on the UUlm Head Pose and Gaze Database and attribute description on the Multi-PIE database. To the best of our knowledge, this is the first pose-invariant approach to estimate gaze from unconstrained still images.

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“…1) Static Face Generation: This task is typically tackled based on face morphable models [27], [28], [29] by editing the geometry of a reconstructed face shape. With the rise of generative modeling, a number of methods apply Variational Auto-Encoder (VAE) [30], [31], [37] and Generative Adversarial Network (GAN) [38], [40], [41], [42] to generate face images towards specific attributes.…”

Section: A Related Workmentioning

confidence: 99%

Image-to-Video Generation via 3D Facial Dynamics

Zou

Zhao³

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

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We present a versatile model, FaceAnime, for various video generation tasks from still images. Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial landmarks. However, the generated face images usually suffer from quality loss, image distortion, identity change, and expression mismatching due to the weak representation capacity of the facial landmarks. In this paper, we propose to "imagine" a face video from a single face image according to the reconstructed 3D face dynamics, aiming to generate a realistic and identity-preserving face video, with precisely predicted pose and facial expression. The 3D dynamics reveal changes of the facial expression and motion, and can serve as a strong prior knowledge for guiding highly realistic face video generation. In particular, we explore face video prediction and exploit a well-designed 3D dynamic prediction network to predict a 3D dynamic sequence for a single face image. The 3D dynamics are then further rendered by the sparse texture mapping algorithm to recover structural details and sparse textures for generating face frames. Our model is versatile for various AR/VR and entertainment applications, such as face video retargeting and face video prediction. Superior experimental results have well demonstrated its effectiveness in generating high-fidelity, identitypreserving, and visually pleasant face video clips from a single source face image.

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Weight, Sex, and Facial Expressions: On the Manipulation of Attributes in Generative 3D Face Models

Cited by 9 publications

References 12 publications

Modifying the Memorability of Face Photographs

Modifying the Memorability of Face Photographs

Pose Normalization for Eye Gaze Estimation and Facial Attribute Description from Still Images

Image-to-Video Generation via 3D Facial Dynamics

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