Talking Papers Podcast
Talking Papers Podcast: deep dives into research papers in computer vision, 3D, machine learning, and AI, with the authors who wrote them. Where research meets conversation. By researchers, for researchers.
Each episode is structured like the paper itself: a TL;DR / abstract to set the stage, then related work, approach, results, conclusions, and future work. We close with a bonus segment called "What did Reviewer 2 say?", where the authors share the candid peer-review story behind the publication.
Hosted by Itzik Ben-Shabat. Guests are PhD students, postdocs, and faculty from leading labs across academia and industry. Aimed at fellow researchers and graduate students who want the candid version of the work, not a polished press release.
Talking Papers Podcast
Guy Gafni - NerFACE
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PAPER TITLE:
Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction
AUTHORS:
Guy Gafni Justus Thies Michael Zollhöfer Matthias Nießner
Project page: https://gafniguy.github.io/4D-Facial-Avatars/
CODE:
💻https://github.com/gafniguy/4D-Facial-Avatars
ABSTRACT:
We present dynamic neural radiance fields for modeling the appearance and dynamics of a human face. Digitally modeling and reconstructing a talking human is a key building-block for a variety of applications. Especially, for telepresence applications in AR or VR, a faithful reproduction of the appearance including novel viewpoint or head-poses is required. In contrast to state-of-the-art approaches that model the geometry and material properties explicitly, or are purely image-based, we introduce an implicit representation of the head based on scene representation networks. To handle the dynamics of the face, we combine our scene representation network with a low-dimensional morphable model which provides explicit control over pose and expressions. We use volumetric rendering to generate images from this hybrid representation and demonstrate that such a dynamic neural scene representation can be learned from monocular input data only, without the need of a specialized capture setup. In our experiments, we show that this learned volumetric representation allows for photo-realistic image generation that surpasses the quality of state-of-the-art video-based reenactment methods.
RELATED PAPERS:
📚Representing Scenes as Neural Radiance Fields for View Synthesis
📚Deep Video Portraits
📚Nerfies: Deformable Neural Radiance Fields
📚AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis
CONTACT:
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If you would like to be a guest, sponsor or just share your thoughts, feel free to reach out via email: talking.papers.podcast@gmail.com
TIME STAMPS
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00:00
00:07 Intro
00:27 Authors
01:16 Abstract / TLDR
02:54 Motivation
12:24 Related Work
13:20 Approach
17:10 Results
27:05 Conclusions and future work
32:12 Outro
#talkingpapers #CVPR2021 #NeRF
#machinelearning #deeplearning #AI #neuralnetworks #research #computervision #artificialintelligence #FacialAvatars
Recorded on April, 2nd 2021.
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