Recent deep generative models allow real-time generation of hair images from sketch inputs. Existing solutions often require a user-provided binary mask to specify a target hair shape. This not only costs users extra labor but also fails to capture c
omplicated hair boundaries. Those solutions usually encode hair structures via orientation maps, which, however, are not very effective to encode complex structures. We observe that colored hair sketches already implicitly define target hair shapes as well as hair appearance and are more flexible to depict hair structures than orientation maps. Based on these observations, we present SketchHairSalon, a two-stage framework for generating realistic hair images directly from freehand sketches depicting desired hair structure and appearance. At the first stage, we train a network to predict a hair matte from an input hair sketch, with an optional set of non-hair strokes. At the second stage, another network is trained to synthesize the structure and appearance of hair images from the input sketch and the generated matte. To make the networks in the two stages aware of long-term dependency of strokes, we apply self-attention modules to them. To train these networks, we present a new dataset containing thousands of annotated hair sketch-image pairs and corresponding hair mattes. Two efficient methods for sketch completion are proposed to automatically complete repetitive braided parts and hair strokes, respectively, thus reducing the workload of users. Based on the trained networks and the two sketch completion strategies, we build an intuitive interface to allow even novice users to design visually pleasing hair images exhibiting various hair structures and appearance via freehand sketches. The qualitative and quantitative evaluations show the advantages of the proposed system over the existing or alternative solutions.
We propose a multivariate functional responses low rank regression model with possible high dimensional functional responses and scalar covariates. By expanding the slope functions on a set of sieve basis, we reconstruct the basis coefficients as a m
atrix. To estimate these coefficients, we propose an efficient procedure using nuclear norm regularization. We also derive error bounds for our estimates and evaluate our method using simulations. We further apply our method to the Human Connectome Project neuroimaging data to predict cortical surface motor task-evoked functional magnetic resonance imaging signals using various clinical covariates to illustrate the usefulness of our results.
Alzheimers disease is a progressive form of dementia that results in problems with memory, thinking and behavior. It often starts with abnormal aggregation and deposition of beta-amyloid and tau, followed by neuronal damage such as atrophy of the hip
pocampi, and finally leads to behavioral deficits. Despite significant progress in finding biomarkers associated with behavioral deficits, the underlying causal mechanism remains largely unknown. Here we investigate whether and how hippocampal atrophy contributes to behavioral deficits based on a large-scale observational study conducted by the Alzheimers Disease Neuroimaging Initiative (ADNI). As a key novelty, we use 2D representations of the hippocampi, which allows us to better understand atrophy associated with different subregions. It, however, introduces methodological challenges as existing causal inference methods are not well suited for exploiting structural information embedded in the 2D exposures. Moreover, our data contain more than 6 million clinical and genetic covariates, necessitating appropriate confounder selection methods. We hence develop a novel two-step causal inference approach tailored for our ADNI data application. Analysis results suggest that atrophy of CA1 and subiculum subregions may cause more severe behavioral deficits compared to CA2 and CA3 subregions. We further evaluate our method using simulations and provide theoretical guarantees.
