Search Results for author:
Found 17 papers, 2 papers with code
Probabilistic Speech-Driven 3D Facial Motion Synthesis: New Benchmarks, Methods, and Applications
While these models can achieve high-quality lip articulation for speakers in the training set, they are unable to capture the full and diverse distribution of 3D facial motions that accompany speech in the real world.
HUGS: Human Gaussian Splats
We achieve state-of-the-art rendering quality with a rendering speed of 60 FPS while being ~100x faster to train over previous work.
Novel-View Acoustic Synthesis from 3D Reconstructed Rooms
Given audio recordings from 2-4 microphones and the 3D geometry and material of a scene containing multiple unknown sound sources, we estimate the sound anywhere in the scene.
Efficient-3DiM: Learning a Generalizable Single-image Novel-view Synthesizer in One Day
Although the fidelity and generalizability are greatly improved, training such a powerful diffusion model requires a vast volume of training data and model parameters, resulting in a notoriously long time and high computational costs.
Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models
In this paper, we propose a new strategy for adapting ASR models to new target domains without any text or speech from those domains.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Pointersect: Neural Rendering with Cloud-Ray Intersection
Specifically, we train a set transformer that, given a small number of local neighbor points along a light ray, provides the intersection point, the surface normal, and the material blending weights, which are used to render the outcome of this light ray.
FaceLit: Neural 3D Relightable Faces
We propose a generative framework, FaceLit, capable of generating a 3D face that can be rendered at various user-defined lighting conditions and views, learned purely from 2D images in-the-wild without any manual annotation.
Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis
Here, we ask two fundamental questions about this strategy: when is synthetic data effective for personalization, and why is it effective in those cases?
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Synt++: Utilizing Imperfect Synthetic Data to Improve Speech Recognition
With recent advances in speech synthesis, synthetic data is becoming a viable alternative to real data for training speech recognition models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Data Incubation -- Synthesizing Missing Data for Handwriting Recognition
We use the framework to optimize data synthesis and demonstrate significant improvement on handwriting recognition over a model trained on real data only.
Token Pooling in Vision Transformers
Token Pooling is a simple and effective operator that can benefit many architectures.
Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models
However, under an unsupervised-style setting, typical training algorithms for controllable sequence generative models suffer from the training-inference mismatch, where the same sample is used as content and style input during training but unpaired samples are given during inference.
SapAugment: Learning A Sample Adaptive Policy for Data Augmentation
Our policy adapts the augmentation parameters based on the training loss of the data samples.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Towards Occlusion-Aware Multifocal Displays
Multifocal displays, one of the classic approaches to satisfy the accommodation cue, place virtual content at multiple focal planes, each at a di erent depth.
Towards Multifocal Displays with Dense Focal Stacks
We present a virtual reality display that is capable of generating a dense collection of depth/focal planes.
Random Features for Sparse Signal Classification
Random features is an approach for kernel-based inference on large datasets.
Propagated Image Filtering
In this paper, we propose the propagation filter as a novel image filtering operator, with the goal of smoothing over neighboring image pixels while preserving image context like edges or textural regions.
