Chen Cao

Orcid: 0000-0001-7992-2814

Affiliations:
  • Meta Codec Avatars Lab, Reality Labs Research, Pittsburgh, USA
  • ETH Zurich, Swiss


According to our database1, Chen Cao authored at least 16 papers between 2020 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

Online presence:

On csauthors.net:

Bibliography

2025
3DGH: 3D Head Generation with Composable Hair and Face.
ACM Trans. Graph., August, 2025

Vid2Avatar-Pro: Authentic Avatar from Videos in the Wild via Universal Prior.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

LUCAS: Layered Universal Codec Avatars.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

2024
Universal Facial Encoding of Codec Avatars from VR Headsets.
ACM Trans. Graph., July, 2024

Universal Facial Encoding of Codec Avatars from VR Headsets.
CoRR, 2024

StyleAvatar: Stylizing Animatable Head Avatars.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

URAvatar: Universal Relightable Gaussian Codec Avatars.
Proceedings of the SIGGRAPH Asia 2024 Conference Papers, 2024

Bridging the Gap: Studio-Like Avatar Creation from a Monocular Phone Capture.
Proceedings of the Computer Vision - ECCV 2024, 2024


A Local Appearance Model for Volumetric Capture of Diverse Hairstyles.
Proceedings of the International Conference on 3D Vision, 2024

2023
A Local Appearance Model for Volumetric Capture of Diverse Hairstyle.
CoRR, 2023

NeuWigs: A Neural Dynamic Model for Volumetric Hair Capture and Animation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Authentic volumetric avatars from a phone scan.
ACM Trans. Graph., 2022

2021
Real-time 3D neural facial animation from binocular video.
ACM Trans. Graph., 2021

High-Fidelity Face Tracking for AR/VR via Deep Lighting Adaptation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020


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