Smooth-Swap: A Simple Enhancement for Face-Swapping with Smoothness

Face-swapping models have been drawing attention for their compelling generation quality, but their complex architectures and loss functions often require careful tuning for successful training. We propose a new face-swapping model called `Smooth-Swap', which excludes complex handcrafted designs and allows fast and stable training. The main idea of Smooth-Swap is to build smooth identity embedding that can provide stable gradients for identity change. Unlike the one used in previous models trained for a purely discriminative task, the proposed embedding is trained with a supervised contrastive loss promoting a smoother space. With improved smoothness, Smooth-Swap suffices to be composed of a generic U-Net-based generator and three basic loss functions, a far simpler design compared with the previous models. Extensive experiments on face-swapping benchmarks (FFHQ, FaceForensics++) and face images in the wild show that our model is also quantitatively and qualitatively comparable or even superior to the existing methods.Comment: CVPR 2022 (Oral

Variational Distribution Learning for Unsupervised Text-to-Image Generation

We propose a text-to-image generation algorithm based on deep neural networks when text captions for images are unavailable during training. In this work, instead of simply generating pseudo-ground-truth sentences of training images using existing image captioning methods, we employ a pretrained CLIP model, which is capable of properly aligning embeddings of images and corresponding texts in a joint space and, consequently, works well on zero-shot recognition tasks. We optimize a text-to-image generation model by maximizing the data log-likelihood conditioned on pairs of image-text CLIP embeddings. To better align data in the two domains, we employ a principled way based on a variational inference, which efficiently estimates an approximate posterior of the hidden text embedding given an image and its CLIP feature. Experimental results validate that the proposed framework outperforms existing approaches by large margins under unsupervised and semi-supervised text-to-image generation settings.Comment: Accepted at CVPR202

FLAME: Free-Form Language-Based Motion Synthesis & Editing

Text-based motion generation models are drawing a surge of interest for their potential for automating the motion-making process in the game, animation, or robot industries. In this paper, we propose a diffusion-based motion synthesis and editing model named FLAME. Inspired by the recent successes in diffusion models, we integrate diffusion-based generative models into the motion domain. FLAME can generate high-fidelity motions well aligned with the given text. Also, it can edit the parts of the motion, both frame-wise and joint-wise, without any fine-tuning. FLAME involves a new transformer-based architecture we devise to better handle motion data, which is found to be crucial to manage variable-length motions and well attend to free-form text. In experiments, we show that FLAME achieves state-of-the-art generation performances on three text-motion datasets: HumanML3D, BABEL, and KIT. We also demonstrate that FLAME’s editing capability can be extended to other tasks such as motion prediction or motion in-betweening, which have been previously covered by dedicated models

Mortgage defaults and mortgage modification policies.

Thesis (Ph. D.)--University of Rochester. Department of Economics, 2013.In this dissertation, I study the fallout from the 2007 US housing market crisis and analyze how effective government mortgage-related policies can be in mitigating mortgage defaults. In my introductory chapter, I analyze the heterogeneity in the financial characteristics of households that filed for bankruptcy or defaulted on their mortgage, using the 2007-09 Panel Survey of Consumer Finances. Since different households hold different amounts and varieties of debts, households considering default choose to default on different debts depending on their financial conditions. In chapter 2, I analyze the effectiveness of government-driven mortgage modification programs in reducing mortgage defaults. I compare an economy without the possibility of modification to one with fairly cheap modifications, and evaluate the impact of these loan modifications on the mortgage default rate. Through loan modification, mortgage servicers can mitigate their losses and households can improve their financial positions without having to walk away from their homes. I calibrate the cost of modifying loan contracts based on government spending on modification programs in 2011. My quantitative exercises show that current government efforts to promote mortgage modification are not very successful in reducing mortgage defaults. However, the US government can potentially decrease mortgage defaults by increasing subsidies for such programs. In chapter 3, I analyze households optimal mortgage and unsecured loan borrowing and default decisions in the recent recession. I model households as able to default on mortgage debt to walk away from negative home equity, at the price of foreclosure. A household can also default on unsecured debt (file for bankruptcy) to maintain its home, in exchange for a longer exclusion from credit markets following default. Depending on the costs of each alternative, financially constrained households exhibit heterogeneity in optimal default decisions within the model that parallels the data. Next, I analyze how mortgage loan modification policy affects household choices in the mortgage and unsecured loan markets. My quantitative exercise shows that while mortgage modification policy can be an effective way of reducing mortgage defaults, it can potentially increase the unsecured loan default rate, especially unsecured loan charge-off rates

Modeling delay discounting using Gaussian process with active learning

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Electrospinning/Electrospray of Ferrocene Containing Copolymers to Fabricate ROS-Responsive Particles and Fibers

We demonstrate an electrospray/electrospinning process to fabricate stimuli-responsive nanofibers or particles that can be utilized as stimuli-responsive drug-loaded materials. A series of random copolymers consisting of hydrophobic ferrocene monomers and hydrophilic carboxyl groups, namely poly(ferrocenylmethyl methacrylate-r-methacrylic acid) [poly(FMMA-r-MA)] with varied composition, was synthesized with free radical copolymerization. The morphologies of the resulting objects created by electrospray/electrospinning of the poly(FMMA-r-MA) solutions were effectively varied from particulate to fibrous structures by control of the composition, suggesting that the morphology of electrosprayed/electrospun copolymer objects was governed by its composition and hence, interaction with the solvent, highlighting the significance of the balance of hydrophilicity/hydrophobicity of the copolymer chain to the assembled structure. Resulting particles and nanofibers exhibited largely preserved responsiveness to reactive oxygen species (ROS) during the deposition process, opening up the potential to fabricate ROS-sensitive material with various desirable structures toward different applications

Modeling Delay Discounting using Gaussian Process with Active Learning

We explore a nonparametric approach to cognitive modeling.Traditionally, models in cognitive science have been paramet-ric. As such, the model relies on the assumption that the datadistribution can be defined by a finite set of parameters. How-ever, there is no guarantee that such an assumption will hold,and it may introduce undesirable biases. For these reasons, anonparametric approach to model building is appealing. Wepropose a novel framework that combines Gaussian Processeswith active learning (GPAL), and evaluate it in the context ofdelay discounting (DD), a well-studied task in decision mak-ing. We evaluate GPAL in a simulation and a behavioral exper-iment, and compare it against a traditional parametric model.The results show that GPAL is a suitable modeling frameworkthat is robust, reliable, and efficient, exhibiting high sensitivityto individual differences