Search Results for author:
Found 35 papers, 26 papers with code
Review of Statistical Shape Spaces for 3D Data with Comparative Analysis for Human Faces
Due to the wide availability of databases of high-quality data, we use the human face as the specific shape we wish to extract from corrupted data.
Multilinear Wavelets: A Statistical Shape Space for Human Faces
We show that in comparison to a global multilinear model, our model better preserves fine detail and is computationally faster, while in comparison to a localized PCA model, our model better handles variation in expression, is faster, and allows us to fix identity parameters for a given subject.
Review of Statistical Shape Spaces for 3D Data with ComparativeAnalysis for Human Faces
Due to the wide avail-ability of databases of high-quality data, we use the human face as the specific shape we wish to extract from corrupted data.
3D faces in motion: Fully automatic registration and statistical analysis
The resulting statistical analysis is applied to automatically generate realistic facial animations and to recognize dynamic facial expressions.
A statistical shape space model of the palate surface trained on 3D MRI scans of the vocal tract
The palate model is then tested using 3D MRI from another corpus and evaluated using a high-resolution optical scan.
A Groupwise Multilinear Correspondence Optimization for 3D Faces
To compute a high-quality multilinear face model, the quality of the registration of the database of 3D face scans used for training is essential.
Fitting a 3D Morphable Model to Edges: A Comparison Between Hard and Soft Correspondences
We propose a fully automatic method for fitting a 3D morphable model to single face images in arbitrary pose and lighting.
A Robust Multilinear Model Learning Framework for 3D Faces
Multilinear models are widely used to represent the statistical variations of 3D human faces as they decouple shape changes due to identity and expression.
Learning a model of facial shape and expression from 4D scans
FLAME is low-dimensional but more expressive than the FaceWarehouse model and the Basel Face Model.
Ranked #3 on
Face Alignment
on FaceScape
Generating 3D faces using Convolutional Mesh Autoencoders
To address this, we introduce a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface.
Ranked #4 on
Face Alignment
on FaceScape
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image
We use the new method, SMPLify-X, to fit SMPL-X to both controlled images and images in the wild.
Ranked #1 on
3D Human Reconstruction
on Expressive hands and faces dataset (EHF)
(TR V2V (mm), left hand metric)
Capture, Learning, and Synthesis of 3D Speaking Styles
To address this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio from 12 speakers.
Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
The estimation of 3D face shape from a single image must be robust to variations in lighting, head pose, expression, facial hair, makeup, and occlusions.
3D Morphable Face Models -- Past, Present and Future
In this paper, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed.
STAR: Sparse Trained Articulated Human Body Regressor
The SMPL body model is widely used for the estimation, synthesis, and analysis of 3D human pose and shape.
Monocular Expressive Body Regression through Body-Driven Attention
To understand how people look, interact, or perform tasks, we need to quickly and accurately capture their 3D body, face, and hands together from an RGB image.
GIF: Generative Interpretable Faces
Specifically, we condition StyleGAN2 on FLAME, a generative 3D face model.
Learning an Animatable Detailed 3D Face Model from In-The-Wild Images
Some methods produce faces that cannot be realistically animated because they do not model how wrinkles vary with expression.
Collaborative Regression of Expressive Bodies using Moderation
Second, human shape is highly correlated with gender, but existing work ignores this.
Topologically Consistent Multi-View Face Inference Using Volumetric Sampling
We propose ToFu, Topologically consistent Face from multi-view, a geometry inference framework that can produce topologically consistent meshes across facial identities and expressions using a volumetric representation instead of an explicit underlying 3DMM.
Learning Realistic Human Reposing using Cyclic Self-Supervision with 3D Shape, Pose, and Appearance Consistency
Synthesizing images of a person in novel poses from a single image is a highly ambiguous task.
Towards Metrical Reconstruction of Human Faces
To this end, we take advantage of a face recognition network pretrained on a large-scale 2D image dataset, which provides distinct features for different faces and is robust to expression, illumination, and camera changes.
Ranked #2 on
3D Face Reconstruction
on NoW Benchmark
EMOCA: Emotion Driven Monocular Face Capture and Animation
While EMOCA achieves 3D reconstruction errors that are on par with the current best methods, it significantly outperforms them in terms of the quality of the reconstructed expression and the perceived emotional content.
Ranked #13 on
3D Face Reconstruction
on REALY (side-view)
Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation
Our experimental results show significant improvement compared to state-of-the-art methods on albedo estimation, both in terms of accuracy and fairness.
Capturing and Animation of Body and Clothing from Monocular Video
Building on this insight, we propose SCARF (Segmented Clothed Avatar Radiance Field), a hybrid model combining a mesh-based body with a neural radiance field.
Human Body Measurement Estimation with Adversarial Augmentation
Training of BMnet is performed on data from real human subjects, and augmented with a novel adversarial body simulator (ABS) that finds and synthesizes challenging body shapes.
SUPR: A Sparse Unified Part-Based Human Representation
Using novel 4D scans of feet, we train a model with an extended kinematic tree that captures the range of motion of the toes.
Instant Volumetric Head Avatars
In addition, it allows for the interactive rendering of novel poses and expressions.
Generating Holistic 3D Human Motion from Speech
This work addresses the problem of generating 3D holistic body motions from human speech.
Ranked #2 on
Gesture Generation
on BEAT2
Instant Multi-View Head Capture through Learnable Registration
We use raw MVS scans as supervision during training, but, once trained, TEMPEH directly predicts 3D heads in dense correspondence without requiring scans.
Emotional Speech-Driven Animation with Content-Emotion Disentanglement
While the best recent methods generate 3D animations that are synchronized with the input audio, they largely ignore the impact of emotions on facial expressions.
SCULPT: Shape-Conditioned Unpaired Learning of Pose-dependent Clothed and Textured Human Meshes
We present SCULPT, a novel 3D generative model for clothed and textured 3D meshes of humans.
Learning Disentangled Avatars with Hybrid 3D Representations
Towards this end, both explicit and implicit 3D representations are heavily studied for a holistic modeling and capture of the whole human (e. g., body, clothing, face and hair), but neither representation is an optimal choice in terms of representation efficacy since different parts of the human avatar have different modeling desiderata.
Emotional Speech-driven 3D Body Animation via Disentangled Latent Diffusion
Once trained, AMUSE synthesizes 3D human gestures directly from speech with control over the expressed emotions and style by combining the content from the driving speech with the emotion and style of another speech sequence.
3D Facial Expressions through Analysis-by-Neural-Synthesis
Instead, SMIRK replaces the differentiable rendering with a neural rendering module that, given the rendered predicted mesh geometry, and sparsely sampled pixels of the input image, generates a face image.
