
Sign up to save your podcasts
Or


The paper presents a method for building high-fidelity relightable head avatars that can be animated to generate novel expressions, using a geometry model based on 3D Gaussians and a relightable appearance model based on learnable radiance transfer. The method achieves real-time relighting with spatially all-frequency reflections and improves the fidelity of eye reflections.
https://arxiv.org/abs//2312.03704
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
By Igor Melnyk5
33 ratings
The paper presents a method for building high-fidelity relightable head avatars that can be animated to generate novel expressions, using a geometry model based on 3D Gaussians and a relightable appearance model based on learnable radiance transfer. The method achieves real-time relighting with spatially all-frequency reflections and improves the fidelity of eye reflections.
https://arxiv.org/abs//2312.03704
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

954 Listeners

1,971 Listeners

438 Listeners

112,664 Listeners

10,051 Listeners

5,531 Listeners

214 Listeners

51 Listeners

93 Listeners

473 Listeners