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Found 17 papers, 8 papers with code
Adversarial Identity Injection for Semantic Face Image Synthesis
Among all the explored techniques, Semantic Image Synthesis (SIS) methods, whose goal is to generate an image conditioned on a semantic segmentation mask, are the most promising, even though preserving the perceived identity of the input subject is not their main concern.
EmoVOCA: Speech-Driven Emotional 3D Talking Heads
The domain of 3D talking head generation has witnessed significant progress in recent years.
Towards Controllable Face Generation with Semantic Latent Diffusion Models
To address that, in this paper we propose a SIS framework based on a novel Latent Diffusion Model architecture for human face generation and editing that is both able to reproduce and manipulate a real reference image and generate diversity-driven results.
ScanTalk: 3D Talking Heads from Unregistered Scans
Speech-driven 3D talking heads generation has emerged as a significant area of interest among researchers, presenting numerous challenges.
Semantic Image Synthesis via Class-Adaptive Cross-Attention
Thus, they tend to overlook global image statistics, ultimately leading to unconvincing local style editing and causing global inconsistencies such as color or illumination distribution shifts.
Automatic Generation of Semantic Parts for Face Image Synthesis
Also, we show our model can be put before a SIS generator, opening the way to a fully automatic generation control of both shape and texture.
Learning Landmarks Motion from Speech for Speaker-Agnostic 3D Talking Heads Generation
This paper presents a novel approach for generating 3D talking heads from raw audio inputs.
Generating Multiple 4D Expression Transitions by Learning Face Landmark Trajectories
We thus propose a new model that generates transitions between different expressions, and synthesizes long and composed 4D expressions.
What makes you, you? Analyzing Recognition by Swapping Face Parts
Deep learning advanced face recognition to an unprecedented accuracy.
Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound
State-of-the-art neural network verifiers are fundamentally based on one of two paradigms: either encoding the whole verification problem via tight multi-neuron convex relaxations or applying a Branch-and-Bound (BaB) procedure leveraging imprecise but fast bounding methods on a large number of easier subproblems.
Instance-wise algorithm configuration with graph neural networks
We pose this task as a supervised learning problem: First, we compile a large dataset of the solver performance for various configurations and all provided MILP instances.
Sparse to Dense Dynamic 3D Facial Expression Generation
This allows us to learn how the motion of a sparse set of landmarks influences the deformation of the overall face surface, independently from the identity.
Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema
Second, using the segmentation map and the original image, we are able to predict if a patient has MF or Eczema.
Inner Eye Canthus Localization for Human Body Temperature Screening
As additional contribution, we enrich the original dataset by using the annotated landmarks to deform and project the 3DMM onto the images.
A Sparse and Locally Coherent Morphable Face Model for Dense Semantic Correspondence Across Heterogeneous 3D Faces
The 3D Morphable Model (3DMM) is a powerful statistical tool for representing 3D face shapes.
Additional Baseline Metrics for the paper "Extended YouTube Faces: a Dataset for Heterogeneous Open-Set Face Identification"
In this report, we provide additional and corrected results for the paper "Extended YouTube Faces: a Dataset for Heterogeneous Open-Set Face Identification".
Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making
We draw attention to an important, yet largely overlooked aspect of evaluating fairness for automated decision making systems---namely risk and welfare considerations.
