136 citations · 170 across the 5 of their papers we have counts for
6 papers
LUCAS: Layered Universal Codec Avatars
Di Liu, Teng Deng, Giljoo Nam +6
Photorealistic 3D head avatar reconstruction faces critical challenges in modeling dynamic face-hair interactions and achieving cross-identity generalization, particularly during e…
Rasterized Edge Gradients: Handling Discontinuities Differentiably
Stanislav Pidhorskyi, Tomas Simon, Gabriel Schwartz +3
Computing the gradients of a rendering process is paramount for diverse applications in computer vision and graphics. However, accurate computation of these gradients is challengin…
LiP-Flow: Learning Inference-time Priors for Codec Avatars via Normalizing Flows in Latent Space
Emre Aksan, Shugao Ma, Akin Caliskan +5
Neural face avatars that are trained from multi-view data captured in camera domes can produce photo-realistic 3D reconstructions. However, at inference time, they must be driven b…
Adversarial Latent Autoencoders
Stanislav Pidhorskyi, Donald Adjeroh, Gianfranco Doretto
Autoencoder networks are unsupervised approaches aiming at combining generative and representational properties by learning simultaneously an encoder-generator map. Although studie…
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Stanislav Pidhorskyi, Ranya Almohsen, Donald A Adjeroh +1
Novelty detection is the problem of identifying whether a new data point is considered to be an inlier or an outlier. We assume that training data is available to describe only the…
syGlass: Interactive Exploration of Multidimensional Images Using Virtual Reality Head-mounted Displays
Stanislav Pidhorskyi, Michael Morehead, Quinn Jones +2
The quest for deeper understanding of biological systems has driven the acquisition of increasingly larger multidimensional image datasets. Inspecting and manipulating data of this…