63 citations · 79 across the 18 of their papers we have counts for
19 papers
Velox: Learning Representations of 4D Geometry and Appearance
Anagh Malik, Dorian Chan, Xiaoming Zhao +3
We introduce a framework for learning latent representations of 4D objects which are descriptive, faithfully capturing object geometry and appearance; compressive, aiding in downst…
LiTo: Surface Light Field Tokenization
Jen-Hao Rick Chang, Xiaoming Zhao, Dorian Chan +1
We propose a 3D latent representation that jointly models object geometry and view-dependent appearance. Most prior works focus on either reconstructing 3D geometry or predicting v…
RayRoPE: Projective Ray Positional Encoding for Multi-view Attention
Yu Wu, Minsik Jeon, Jen-Hao Rick Chang +2
We study positional encodings for multi-view transformers that process tokens from a set of posed input images, and seek a mechanism that encodes patches uniquely, allows SE(3)-inv…
Mutual Reinforcement of LLM Dialogue Synthesis and Summarization Capabilities for Few-Shot Dialogue Summarization
Yen-Ju Lu, Ting-Yao Hu, Hema Swetha Koppula +8
In this work, we propose Mutual Reinforcing Data Synthesis (MRDS) within LLMs to improve few-shot dialogue summarization task. Unlike prior methods that require external knowledge,…
3D Shape Tokenization via Latent Flow Matching
Jen-Hao Rick Chang, Yuyang Wang, Miguel Angel Bautista Martin +4
We introduce a latent 3D representation that models 3D surfaces as probability density functions in 3D, i.e., p(x,y,z), with flow-matching. Our representation is specifically desig…
Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum
Hadi Pouransari, Chun-Liang Li, Jen-Hao Rick Chang +4
Large language models (LLMs) are commonly trained on datasets consisting of fixed-length token sequences. These datasets are created by randomly concatenating documents of various…
Probabilistic Speech-Driven 3D Facial Motion Synthesis: New Benchmarks, Methods, and Applications
Karren D. Yang, Anurag Ranjan, Jen-Hao Rick Chang +2
We consider the task of animating 3D facial geometry from speech signal. Existing works are primarily deterministic, focusing on learning a one-to-one mapping from speech signal to…
HUGS: Human Gaussian Splats
Muhammed Kocabas, Jen-Hao Rick Chang, James Gabriel +2
Recent advances in neural rendering have improved both training and rendering times by orders of magnitude. While these methods demonstrate state-of-the-art quality and speed, they…
Efficient-3DiM: Learning a Generalizable Single-image Novel-view Synthesizer in One Day
Yifan Jiang, Hao Tang, Jen-Hao Rick Chang +3
The task of novel view synthesis aims to generate unseen perspectives of an object or scene from a limited set of input images. Nevertheless, synthesizing novel views from a single…
Novel-View Acoustic Synthesis from 3D Reconstructed Rooms
Byeongjoo Ahn, Karren Yang, Brian Hamilton +5
We investigate the benefit of combining blind audio recordings with 3D scene information for novel-view acoustic synthesis. Given audio recordings from 2-4 microphones and the 3D g…
Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models
Hsuan Su, Ting-Yao Hu, Hema Swetha Koppula +5
While Automatic Speech Recognition (ASR) systems are widely used in many real-world applications, they often do not generalize well to new domains and need to be finetuned on data…
Pointersect: Neural Rendering with Cloud-Ray Intersection
Jen-Hao Rick Chang, Wei-Yu Chen, Anurag Ranjan +2
We propose a novel method that renders point clouds as if they are surfaces. The proposed method is differentiable and requires no scene-specific optimization. This unique capabili…
FaceLit: Neural 3D Relightable Faces
Anurag Ranjan, Kwang Moo Yi, Jen-Hao Rick Chang +1
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 image…
Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis
Karren Yang, Ting-Yao Hu, Jen-Hao Rick Chang +2
Adapting generic speech recognition models to specific individuals is a challenging problem due to the scarcity of personalized data. Recent works have proposed boosting the amount…
Synt++: Utilizing Imperfect Synthetic Data to Improve Speech Recognition
Ting-Yao Hu, Mohammadreza Armandpour, Ashish Shrivastava +3
With recent advances in speech synthesis, synthetic data is becoming a viable alternative to real data for training speech recognition models. However, machine learning with synthe…
Data Incubation -- Synthesizing Missing Data for Handwriting Recognition
Jen-Hao Rick Chang, Martin Bresler, Youssouf Chherawala +4
In this paper, we demonstrate how a generative model can be used to build a better recognizer through the control of content and style. We are building an online handwriting recogn…
Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models
Jen-Hao Rick Chang, Ashish Shrivastava, Hema Swetha Koppula +2
Controllable generative sequence models with the capability to extract and replicate the style of specific examples enable many applications, including narrating audiobooks in diff…
SapAugment: Learning A Sample Adaptive Policy for Data Augmentation
Ting-Yao Hu, Ashish Shrivastava, Jen-Hao Rick Chang +5
Data augmentation methods usually apply the same augmentation (or a mix of them) to all the training samples. For example, to perturb data with noise, the noise is sampled from a N…
One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models
J. H. Rick Chang, Chun-Liang Li, Barnabas Poczos +2
While deep learning methods have achieved state-of-the-art performance in many challenging inverse problems like image inpainting and super-resolution, they invariably involve prob…