activity
20162022
most citedLearning to Infer Implicit Surfaces without 3D Supervision

88 citations · 143 across the 7 of their papers we have counts for

collaborators

12 papers

cs.CV2022

COAP: Compositional Articulated Occupancy of People

Marko Mihajlovic, Shunsuke Saito, Aayush Bansal +2

We present a novel neural implicit representation for articulated human bodies. Compared to explicit template meshes, neural implicit body representations provide an efficient mech…

cs.CV2022

AutoAvatar: Autoregressive Neural Fields for Dynamic Avatar Modeling

Ziqian Bai, Timur Bagautdinov, Javier Romero +3

Neural fields such as implicit surfaces have recently enabled avatar modeling from raw scans without explicit temporal correspondences. In this work, we exploit autoregressive mode…

cs.CV2021

SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements

Qianli Ma, Shunsuke Saito, Jinlong Yang +2

Learning to model and reconstruct humans in clothing is challenging due to articulation, non-rigid deformation, and varying clothing types and topologies. To enable learning, the c…

cs.CV20215 cited

SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks

Shunsuke Saito, Jinlong Yang, Qianli Ma +1

We present SCANimate, an end-to-end trainable framework that takes raw 3D scans of a clothed human and turns them into an animatable avatar. These avatars are driven by pose parame…

cs.CV202122 cited

PVA: Pixel-aligned Volumetric Avatars

Amit Raj, Michael Zollhoefer, Tomas Simon +4

Acquisition and rendering of photo-realistic human heads is a highly challenging research problem of particular importance for virtual telepresence. Currently, the highest quality…

cs.CV20204 cited

Monocular Real-Time Volumetric Performance Capture

Ruilong Li, Yuliang Xiu, Shunsuke Saito +3

We present the first approach to volumetric performance capture and novel-view rendering at real-time speed from monocular video, eliminating the need for expensive multi-view syst…