1 citations · 1 across the 4 of their papers we have counts for
6 papers
RANA: Relightable Articulated Neural Avatars
Umar Iqbal, Akin Caliskan, Koki Nagano +3
We propose RANA, a relightable and articulated neural avatar for the photorealistic synthesis of humans under arbitrary viewpoints, body poses, and lighting. We only require a shor…
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…
Multi-person Implicit Reconstruction from a Single Image
Armin Mustafa, Akin Caliskan, Lourdes Agapito +1
We present a new end-to-end learning framework to obtain detailed and spatially coherent reconstructions of multiple people from a single image. Existing multi-person methods suffe…
Temporal Consistency Loss for High Resolution Textured and Clothed 3DHuman Reconstruction from Monocular Video
Akin Caliskan, Armin Mustafa, Adrian Hilton
We present a novel method to learn temporally consistent 3D reconstruction of clothed people from a monocular video. Recent methods for 3D human reconstruction from monocular video…
Multi-View Consistency Loss for Improved Single-Image 3D Reconstruction of Clothed People
Akin Caliskan, Armin Mustafa, Evren Imre +1
We present a novel method to improve the accuracy of the 3D reconstruction of clothed human shape from a single image. Recent work has introduced volumetric, implicit and model-bas…
Learning Dense Wide Baseline Stereo Matching for People
Akin Caliskan, Armin Mustafa, Evren Imre +1
Existing methods for stereo work on narrow baseline image pairs giving limited performance between wide baseline views. This paper proposes a framework to learn and estimate dense…