15 citations · 45 across the 12 of their papers we have counts for
11 papers · 1 filter
GNM Head: A Generative aNthropometric Model of the human head
Stylianos Ploumpis, Jan Bednarik, Gaspard Zoss +29
Parametric models of the human head are essential tools traditionally used in computer vision and graphics for animation, rendering, and reconstruction. More recently, they serve a…
RelightAnyone: A Generalized Relightable 3D Gaussian Head Model
Yingyan Xu, Pramod Rao, Sebastian Weiss +5
3D Gaussian Splatting (3DGS) has become a standard approach to reconstruct and render photorealistic 3D head avatars. A major challenge is to relight the avatars to match any scene…
Joint Learning of Depth and Appearance for Portrait Image Animation
Xinya Ji, Gaspard Zoss, Prashanth Chandran +4
2D portrait animation has experienced significant advancements in recent years. Much research has utilized the prior knowledge embedded in large generative diffusion models to enha…
Monocular Facial Appearance Capture in the Wild
Yingyan Xu, Kate Gadola, Prashanth Chandran +4
We present a new method for reconstructing the appearance properties of human faces from a lightweight capture procedure in an unconstrained environment. Our method recovers the su…
Multimodal Conditional 3D Face Geometry Generation
Christopher Otto, Prashanth Chandran, Sebastian Weiss +3
We present a new method for multimodal conditional 3D face geometry generation that allows user-friendly control over the output identity and expression via a number of different c…
Infinite 3D Landmarks: Improving Continuous 2D Facial Landmark Detection
Prashanth Chandran, Gaspard Zoss, Paulo Gotardo +1
In this paper, we examine 3 important issues in the practical use of state-of-the-art facial landmark detectors and show how a combination of specific architectural modifications c…
Learning a Generalized Physical Face Model From Data
Lingchen Yang, Gaspard Zoss, Prashanth Chandran +4
Physically-based simulation is a powerful approach for 3D facial animation as the resulting deformations are governed by physical constraints, allowing to easily resolve self-colli…
An Implicit Physical Face Model Driven by Expression and Style
Lingchen Yang, Gaspard Zoss, Prashanth Chandran +5
3D facial animation is often produced by manipulating facial deformation models (or rigs), that are traditionally parameterized by expression controls. A key component that is usua…
Implicit Neural Representation for Physics-driven Actuated Soft Bodies
Lingchen Yang, Byungsoo Kim, Gaspard Zoss +3
Active soft bodies can affect their shape through an internal actuation mechanism that induces a deformation. Similar to recent work, this paper utilizes a differentiable, quasi-st…
Artist-Friendly Relightable and Animatable Neural Heads
Yingyan Xu, Prashanth Chandran, Sebastian Weiss +3
An increasingly common approach for creating photo-realistic digital avatars is through the use of volumetric neural fields. The original neural radiance field (NeRF) allowed for i…
A Perceptual Shape Loss for Monocular 3D Face Reconstruction
Christopher Otto, Prashanth Chandran, Gaspard Zoss +3
Monocular 3D face reconstruction is a wide-spread topic, and existing approaches tackle the problem either through fast neural network inference or offline iterative reconstruction…