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Found 10 papers, 2 papers with code
Learning a Generalized Physical Face Model From Data
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-collisions, respond to external forces and perform realistic anatomy edits.
An Implicit Physical Face Model Driven by Expression and Style
At the core, we present a framework for learning implicit physics-based actuations for multiple subjects simultaneously, trained on a few arbitrary performance capture sequences from a small set of identities.
Artist-Friendly Relightable and Animatable Neural Heads
An increasingly common approach for creating photo-realistic digital avatars is through the use of volumetric neural fields.
A Perceptual Shape Loss for Monocular 3D Face Reconstruction
In this work we propose a new loss function for monocular face capture, inspired by how humans would perceive the quality of a 3D face reconstruction given a particular image.
Ranked #7 on
3D Face Reconstruction
on REALY (side-view)
CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
While conditional diffusion models are known to have good coverage of the data distribution, they still face limitations in output diversity, particularly when sampled with a high classifier-free guidance scale for optimal image quality or when trained on small datasets.
Ranked #3 on
Image Generation
on ImageNet 256x256
Continuous Landmark Detection With 3D Queries
Neural networks for facial landmark detection are notoriously limited to a fixed set of landmarks in a dedicated layout, which must be specified at training time.
ReNeRF: Relightable Neural Radiance Fields with Nearfield Lighting
Recent work on radiance fields and volumetric inverse rendering (e. g., NeRFs) has provided excellent results in building data-driven models of real scenes for novel view synthesis with high photorealism.
Adaptive Convolutions for Structure-Aware Style Transfer
Style transfer between images is an artistic application of CNNs, where the 'style' of one image is transferred onto another image while preserving the latter's content.
ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation
We show that our dataset can significantly improve the robustness of gaze estimation methods across different head poses and gaze angles.
Ranked #1 on
Gaze Estimation
on ETH-XGaze
(using extra training data)
FaceDirector: Continuous Control of Facial Performance in Video
We present a method to continuously blend between multiple facial performances of an actor, which can contain different facial expressions or emotional states.
