Search Results for author:
Found 6 papers, 1 papers with code
Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model
A particular challenge is that only a sparse tracking signal is available from standalone HMDs (Head Mounted Devices), often limited to tracking the user's head and wrists.
Real-time Virtual-Try-On from a Single Example Image through Deep Inverse Graphics and Learned Differentiable Renderers
In this paper we propose a novel framework based on deep learning to build a real-time inverse graphics encoder that learns to map a single example image into the parameter space of a given augmented reality rendering engine.
Hair Color Digitization through Imaging and Deep Inverse Graphics
Since rendering realistic hair images requires path-tracing rendering, the conventional inverse graphics approach based on differentiable rendering is untractable.
Deep Graphics Encoder for Real-Time Video Makeup Synthesis from Example
While makeup virtual-try-on is now widespread, parametrizing a computer graphics rendering engine for synthesizing images of a given cosmetics product remains a challenging task.
Learning Long-Term Style-Preserving Blind Video Temporal Consistency
When trying to independently apply image-trained algorithms to successive frames in videos, noxious flickering tends to appear.
CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transfer
While existing makeup style transfer models perform an image synthesis whose results cannot be explicitly controlled, the ability to modify makeup color continuously is a desirable property for virtual try-on applications.
