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.
We propose a novel procedure which adds "content-addressability" to any given unconditional implicit model e. g., a generative adversarial network (GAN).
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.
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)
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.
In this paper, we investigate deep image synthesis guided by sketch, color, and texture.
Ranked #2 on Image Reconstruction on Edge-to-Shoes
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.