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Found 19 papers, 10 papers with code
ShapeFusion: A 3D diffusion model for localized shape editing
In the realm of 3D computer vision, parametric models have emerged as a ground-breaking methodology for the creation of realistic and expressive 3D avatars.
AnimateMe: 4D Facial Expressions via Diffusion Models
Recent advances in diffusion models have notably enhanced the capabilities of generative models in 2D animation.
Locally Adaptive Neural 3D Morphable Models
We present the Locally Adaptive Morphable Model (LAMM), a highly flexible Auto-Encoder (AE) framework for learning to generate and manipulate 3D meshes.
FitMe: Deep Photorealistic 3D Morphable Model Avatars
In this paper, we introduce FitMe, a facial reflectance model and a differentiable rendering optimization pipeline, that can be used to acquire high-fidelity renderable human avatars from single or multiple images.
Handy: Towards a High Fidelity 3D Hand Shape and Appearance Model
Currently, most of the state-of-the-art reconstruction and pose estimation methods rely on the low polygon MANO model.
Dynamic Neural Portraits
We present Dynamic Neural Portraits, a novel approach to the problem of full-head reenactment.
Neural Mesh Simplification
Then, we train a sparse attention network to propose candidate triangles based on the edge connectivity of the sampled vertices.
AvatarMe++: Facial Shape and BRDF Inference with Photorealistic Rendering-Aware GANs
Nevertheless, to the best of our knowledge, there is no method which can produce render-ready high-resolution 3D faces from "in-the-wild" images and this can be attributed to the: (a) scarcity of available data for training, and (b) lack of robust methodologies that can successfully be applied on very high-resolution data.
3D human tongue reconstruction from single "in-the-wild" images
3D face reconstruction from a single image is a task that has garnered increased interest in the Computer Vision community, especially due to its broad use in a number of applications such as realistic 3D avatar creation, pose invariant face recognition and face hallucination.
Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction
In this paper, we take a radically different approach and harness the power of Generative Adversarial Networks (GANs) and DCNNs in order to reconstruct the facial texture and shape from single images.
Learning to Generate Customized Dynamic 3D Facial Expressions
To this end, in this study we employ a deep mesh encoder-decoder like architecture to synthesize realistic high resolution facial expressions by using a single neutral frame along with an expression identification.
AvatarMe: Realistically Renderable 3D Facial Reconstruction "in-the-wild"
Over the last years, with the advent of Generative Adversarial Networks (GANs), many face analysis tasks have accomplished astounding performance, with applications including, but not limited to, face generation and 3D face reconstruction from a single "in-the-wild" image.
Towards a complete 3D morphable model of the human head
Eye and eye region models are incorporated into the head model, along with basic models of the teeth, tongue and inner mouth cavity.
Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks
In this paper, we present the first methodology that generates high-quality texture, shape, and normals jointly, which can be used for photo-realistic synthesis.
Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation
Generative models for 3D geometric data arise in many important applications in 3D computer vision and graphics.
3DFaceGAN: Adversarial Nets for 3D Face Representation, Generation, and Translation
As a result, linear methods such as Principal Component Analysis (PCA) have been mainly utilized towards 3D shape analysis, despite being unable to capture non-linearities and high frequency details of the 3D face - such as eyelid and lip variations.
Combining 3D Morphable Models: A Large scale Face-and-Head Model
Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for representing the 3D surfaces of an object class.
GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
In this paper, we take a radically different approach and harness the power of Generative Adversarial Networks (GANs) and DCNNs in order to reconstruct the facial texture and shape from single images.
Ranked #1 on
3D Face Reconstruction
on Florence
(Average 3D Error metric)
3D Face Morphable Models "In-the-Wild"
In this paper, we propose the first, to the best of our knowledge, "in-the-wild" 3DMM by combining a powerful statistical model of facial shape, which describes both identity and expression, with an "in-the-wild" texture model.
Ranked #3 on
3D Face Reconstruction
on Florence
(Average 3D Error metric)
