Search Results for author:
Found 22 papers, 14 papers with code
Enhancing Virtual Try-On with Synthetic Pairs and Error-Aware Noise Scheduling
We introduce a garment extraction model that generates (human, synthetic garment) pairs from a single image of a clothed individual.
P-Flow: A Fast and Data-Efficient Zero-Shot TTS through Speech Prompting
P-Flow comprises a speech-prompted text encoder for speaker adaptation and a flow matching generative decoder for high-quality and fast speech synthesis.
VANI: Very-lightweight Accent-controllable TTS for Native and Non-native speakers with Identity Preservation
We introduce VANI, a very lightweight multi-lingual accent controllable speech synthesis system.
Multilingual Multiaccented Multispeaker TTS with RADTTS
We work to create a multilingual speech synthesis system which can generate speech with the proper accent while retaining the characteristics of an individual voice.
Collecting The Puzzle Pieces: Disentangled Self-Driven Human Pose Transfer by Permuting Textures
Then we reconstruct the input image by sampling from the permuted textures for patch-level disentanglement.
Generative Modeling for Low Dimensional Speech Attributes with Neural Spline Flows
Despite recent advances in generative modeling for text-to-speech synthesis, these models do not yet have the same fine-grained adjustability of pitch-conditioned deterministic models such as FastPitch and FastSpeech2.
One TTS Alignment To Rule Them All
However, these alignments tend to be brittle and often fail to generalize to long utterances and out-of-domain text, leading to missing or repeating words.
RAD-TTS: Parallel Flow-Based TTS with Robust Alignment Learning and Diverse Synthesis
This work introduces a predominantly parallel, end-to-end TTS model based on normalizing flows.
Unsupervised Disentanglement of Pose, Appearance and Background from Images and Videos
However, the reconstruction task of the entire image forces the model to allocate landmarks to model the background.
Video Interpolation and Prediction with Unsupervised Landmarks
Prediction and interpolation for long-range video data involves the complex task of modeling motion trajectories for each visible object, occlusions and dis-occlusions, as well as appearance changes due to viewpoint and lighting.
Unsupervised Video Interpolation Using Cycle Consistency
We further introduce a pseudo supervised loss term that enforces the interpolated frames to be consistent with predictions of a pre-trained interpolation model.
Ranked #1 on
Video Frame Interpolation
on UCF101
(PSNR (sRGB) metric)
Graphical Contrastive Losses for Scene Graph Parsing
The first, Entity Instance Confusion, occurs when the model confuses multiple instances of the same type of entity (e. g. multiple cups).
Improving Semantic Segmentation via Video Propagation and Label Relaxation
In this paper, we present a video prediction-based methodology to scale up training sets by synthesizing new training samples in order to improve the accuracy of semantic segmentation networks.
Ranked #2 on
Semantic Segmentation
on KITTI Semantic Segmentation
(using extra training data)
Partial Convolution based Padding
In this paper, we present a simple yet effective padding scheme that can be used as a drop-in module for existing convolutional neural networks.
Revisiting Image-Language Networks for Open-ended Phrase Detection
Most existing work that grounds natural language phrases in images starts with the assumption that the phrase in question is relevant to the image.
SDCNet: Video Prediction Using Spatially-Displaced Convolution
We present an approach for high-resolution video frame prediction by conditioning on both past frames and past optical flows.
SDC-Net: Video prediction using spatially-displaced convolution
We present an approach for high-resolution video frame prediction by conditioning on both past frames and past optical flows.
Ranked #1 on
Video Prediction
on YouTube-8M
Image Inpainting for Irregular Holes Using Partial Convolutions
Existing deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the masked holes (typically the mean value).
Learning Interpretable Spatial Operations in a Rich 3D Blocks World
In this paper, we study the problem of mapping natural language instructions to complex spatial actions in a 3D blocks world.
Where To Look: Focus Regions for Visual Question Answering
We present a method that learns to answer visual questions by selecting image regions relevant to the text-based query.
Part Localization using Multi-Proposal Consensus for Fine-Grained Categorization
We present a simple deep learning framework to simultaneously predict keypoint locations and their respective visibilities and use those to achieve state-of-the-art performance for fine-grained classification.
Learning Collections of Part Models for Object Recognition
We propose a method to learn a diverse collection of discriminative parts from object bounding box annotations.
