activity
20232026
most citedLearning a Generalized Physical Face Model From Data

10 citations · 24 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2026★ 1 cited

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…

cs.CV2024

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…

cs.CV2024★ 6 cited

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…

cs.CV2024★ 10 cited

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…

cs.CV2024★ 7 cited

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…

cs.CV2023

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…

cs.CV2023

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…