89 citations · 304 across the 20 of their papers we have counts for
30 papers
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…
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…
CoordGAN: Self-Supervised Dense Correspondences Emerge from GANs
Jiteng Mu, Shalini De Mello, Zhiding Yu +4
Recent advances show that Generative Adversarial Networks (GANs) can synthesize images with smooth variations along semantically meaningful latent directions, such as pose, express…
Self-Supervised Object Detection via Generative Image Synthesis
Siva Karthik Mustikovela, Shalini De Mello, Aayush Prakash +5
We present SSOD, the first end-to-end analysis-by synthesis framework with controllable GANs for the task of self-supervised object detection. We use collections of real world imag…
Video Autoencoder: self-supervised disentanglement of static 3D structure and motion
Zihang Lai, Sifei Liu, Alexei A. Efros +1
A video autoencoder is proposed for learning disentan- gled representations of 3D structure and camera pose from videos in a self-supervised manner. Relying on temporal continuity…
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…