activity
20182022
most citedOne-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing

22 citations · 52 across the 7 of their papers we have counts for

collaborators

13 papers

cs.CV2022

SPACE: Speech-driven Portrait Animation with Controllable Expression

Siddharth Gururani, Arun Mallya, Ting-Chun Wang +2

Animating portraits using speech has received growing attention in recent years, with various creative and practical use cases. An ideal generated video should have good lip sync w…

cs.CV20228 cited

Implicit Warping for Animation with Image Sets

Arun Mallya, Ting-Chun Wang, Ming-Yu Liu

We present a new implicit warping framework for image animation using sets of source images through the transfer of the motion of a driving video. A single cross- modal attention l…

cs.CV202022 cited

One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing

Ting-Chun Wang, Arun Mallya, Ming-Yu Liu

We propose a neural talking-head video synthesis model and demonstrate its application to video conferencing. Our model learns to synthesize a talking-head video using a source ima…

cs.CV2020

Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications

Ming-Yu Liu, Xun Huang, Jiahui Yu +2

The generative adversarial network (GAN) framework has emerged as a powerful tool for various image and video synthesis tasks, allowing the synthesis of visual content in an uncond…

cs.CV20203 cited

World-Consistent Video-to-Video Synthesis

Arun Mallya, Ting-Chun Wang, Karan Sapra +1

Video-to-video synthesis (vid2vid) aims for converting high-level semantic inputs to photorealistic videos. While existing vid2vid methods can achieve short-term temporal consisten…

cs.CV20206 cited

Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution Filter

Guilin Liu, Rohan Taori, Ting-Chun Wang +6

Conventional CNNs for texture synthesis consist of a sequence of (de)-convolution and up/down-sampling layers, where each layer operates locally and lacks the ability to capture th…