In this episode of the Talking Papers Podcast, I hosted Yuliang Xiu to chat about his paper "ICON: Implicit Clothed humans Obtained from Normals”, published in CVPR 2022. SMPL(-X) body model to infer clothed humans (conditioned on the normals). Additionally, they propose an inference-time feedback loop that alternates between refining the body's normals and the shape.
"ICON: Implicit Clothed humans Obtained from Normals" https://bit.ly/3uXe6Yw
Yuliang Xiu, Jinlong Yang, Dimitrios Tzionas, Michael J. Black
Current methods for learning realistic and animatable 3D clothed avatars need either posed 3D scans or 2D images with carefully controlled user poses. In contrast, our goal is to learn an avatar from only 2D images of people in unconstrained poses. Given a set of images, our method estimates a detailed 3D surface from each image and then combines these into an animatable avatar. Implicit functions are well suited to the first task, as they can capture details like hair and clothes. Current methods, however, are not robust to varied human poses and often produce 3D surfaces with broken or disembodied limbs, missing details, or non-human shapes. The problem is that these methods use global feature encoders that are sensitive to global pose. To address this, we propose ICON ("Implicit Clothed humans Obtained from Normals"), which, instead, uses local features. ICON has two main modules, both of which exploit the SMPL(-X) body model. First, ICON infers detailed clothed-human normals (front/back) conditioned on the SMPL(-X) normals. Second, a visibility-aware implicit surface regressor produces an iso-surface of a human occupancy field. Importantly, at inference time, a feedback loop alternates between refining the SMPL(-X) mesh using the inferred clothed normals and then refining the normals. Given multiple reconstructed frames of a subject in varied poses, we use SCANimate to produce an animatable avatar from them. Evaluation on the AGORA and CAPE datasets shows that ICON outperforms the state of the art in reconstruction, even with heavily limited training data. Additionally, it is much more robust to out-of-distribution samples, e.g., in-the-wild poses/images and out-of-frame cropping. ICON takes a step towards robust 3D clothed human reconstruction from in-the-wild images. This enables creating avatars directly from video with personalized and natural pose-dependent cloth deformation.
📚 Monocular Real-Time Volumetric Performance Capture https://bit.ly/3L2S4JF
📚 PIFu https://bit.ly/3rBsrYN
📚 PIFuHD https://bit.ly/3rymDiE
LINKS AND RESOURCES
💻 Project Page https://icon.is.tue.mpg.de/
💻 Code https://github.com/yuliangxiu/ICON
To stay up to date with Yulian'gs latest research, follow him on:
👨🏻🎓 Yuliang's personal page: https://bit.ly/3jQb16n
🎓 Google Scholar: https://bit.ly/3JW25ae
🐦 Twitter: https://twitter.com/yuliangxiu
Recorded on March11th 2022.
If you would like to be a guest, sponsor or just share your thoughts, feel free to reach out via email: email@example.com
SUBSCRIBE AND FOLLOW
🎧Subscribe on your favourite podcast app: https://talking.papers.podcast.itzikb...
📧Subscribe to our mailing list: http://eepurl.com/hRznqb
🐦Follow us on Twitter: https://twitter.com/talking_papers
🎥YouTube Channel: https://bit.ly/3eQOgwP
This episode was recorded on February 11 2022.
#talkingpapers #CVPR2022 #ICON #ImplicitHumans #3DVision #ComputerVision #AI #DeepLearning #MachineLearning #deeplearning #AI #neuralnetworks #research #artificialintelligence