Search Results for author:
Found 9 papers, 7 papers with code
StyleGAN Salon: Multi-View Latent Optimization for Pose-Invariant Hairstyle Transfer
However, there remains a challenge in controlling the hallucinations to accurately transfer hairstyle and preserve the face shape and identity of the input.
A Sketch Is Worth a Thousand Words: Image Retrieval with Text and Sketch
We empirically demonstrate that using an input sketch (even a poorly drawn one) in addition to text considerably increases retrieval recall compared to traditional text-based image retrieval.
Argoverse: 3D Tracking and Forecasting with Rich Maps
In our baseline experiments, we illustrate how detailed map information such as lane direction, driveable area, and ground height improves the accuracy of 3D object tracking and motion forecasting.
Kernel Mean Matching for Content Addressability of GANs
We propose a novel procedure which adds "content-addressability" to any given unconditional implicit model e. g., a generative adversarial network (GAN).
Informative Features for Model Comparison
Given two candidate models, and a set of target observations, we address the problem of measuring the relative goodness of fit of the two models.
SwapNet: Garment Transfer in Single View Images
Garment transfer is a challenging task that requires (i) disentangling the features of the clothing from the body pose and shape and (ii) realistic synthesis of the garment texture on the new body.
Ranked #1 on
Virtual Try-on
on FashionIQ
(using extra training data)
Let's Dance: Learning From Online Dance Videos
We present a comparison of numerous state-of-the-art techniques on our dataset using three different representations (video, optical flow and multi-person pose data) in order to analyze these approaches.
TextureGAN: Controlling Deep Image Synthesis with Texture Patches
In this paper, we investigate deep image synthesis guided by sketch, color, and texture.
Ranked #2 on
Image Reconstruction
on Edge-to-Shoes
Scribbler: Controlling Deep Image Synthesis with Sketch and Color
In this paper, we propose a deep adversarial image synthesis architecture that is conditioned on sketched boundaries and sparse color strokes to generate realistic cars, bedrooms, or faces.
