activity
20182024
most citedJoint-task Self-supervised Learning for Temporal Correspondence

53 citations · 97 across the 8 of their papers we have counts for

collaborators

10 papers

cs.CV20237 cited

Generalizable One-shot Neural Head Avatar

Xueting Li, Shalini De Mello, Sifei Liu +3

We present a method that reconstructs and animates a 3D head avatar from a single-view portrait image. Existing methods either involve time-consuming optimization for a specific pe…

cs.CV2022

Autoregressive 3D Shape Generation via Canonical Mapping

An-Chieh Cheng, Xueting Li, Sifei Liu +2

With the capacity of modeling long-range dependencies in sequential data, transformers have shown remarkable performances in a variety of generative tasks such as image, audio, and…

cs.CV202114 cited

Learning 3D Dense Correspondence via Canonical Point Autoencoder

An-Chieh Cheng, Xueting Li, Min Sun +2

We propose a canonical point autoencoder (CPAE) that predicts dense correspondences between 3D shapes of the same category. The autoencoder performs two key functions: (a) encoding…

cs.CV202019 cited

Online Adaptation for Consistent Mesh Reconstruction in the Wild

Xueting Li, Sifei Liu, Shalini De Mello +4

This paper presents an algorithm to reconstruct temporally consistent 3D meshes of deformable object instances from videos in the wild. Without requiring annotations of 3D mesh, 2D…

cs.CV2020

Self-supervised Single-view 3D Reconstruction via Semantic Consistency

Xueting Li, Sifei Liu, Kihwan Kim +4

We learn a self-supervised, single-view 3D reconstruction model that predicts the 3D mesh shape, texture and camera pose of a target object with a collection of 2D images and silho…

cs.CV201953 cited

Joint-task Self-supervised Learning for Temporal Correspondence

Xueting Li, Sifei Liu, Shalini De Mello +3

This paper proposes to learn reliable dense correspondence from videos in a self-supervised manner. Our learning process integrates two highly related tasks: tracking large image r…