Talking Papers Podcast
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Are you ready to explore the fascinating world of cutting-edge research in computer vision, machine learning, artificial intelligence, graphics, and beyond? Join us on this podcast by researchers, for researchers, as we venture into the heart of groundbreaking academic papers.
At Talking Papers, we've reimagined the way research is shared. In each episode, we engage in insightful discussions with the main authors of academic papers, offering you a unique opportunity to dive deep into the minds behind the innovation.
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Just like a well-structured research paper, each episode takes you on a journey through the academic landscape. We provide a concise TL;DR (abstract) to set the stage, followed by a thorough exploration of related work, approach, results, conclusions, and a peek into future work.
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Talking Papers Podcast
Guy Gafni - NerFACE
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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