arXiv subjects

Frank Yu

Publications and source records attributed to Frank Yu.

7 recordsLinked to original sources

arXiv2025

BinauralFlow: A Causal and Streamable Approach for High-Quality Binaural Speech Synthesis with Flow Matching Models

Binaural rendering aims to synthesize binaural audio that mimics natural hearing based on a mono audio and the locations of the speaker and listener. Although many methods have been proposed to solve this problem, they struggle with rendering quality and streamable inference. Synthesizing high-quality binaural audio that is indistinguishable from real-world recordings requires precise modeling of binaural cues, room reverb, and ambient sounds. Additionally, real-world applications demand streaming inference. To address these challenges, we propose a flow matching based streaming binaural speech synthesis framework called BinauralFlow. We consider binaural rendering to be a generation problem rather than a regression problem and design a conditional flow matching model to render high-quality audio. Moreover, we design a causal U-Net architecture that estimates the current audio frame solely based on past information to tailor generative models for streaming inference. Finally, we introduce a continuous inference pipeline incorporating streaming STFT/ISTFT operations, a buffer bank, a midpoint solver, and an early skip schedule to improve rendering continuity and speed. Quantitative and qualitative evaluations demonstrate the superiority of our method over SOTA approaches. A perceptual study further reveals that our model is nearly indistinguishable from real-world recordings, with a $42\%$ confusion rate.

cs.SD↗

arXiv2024

SqueezeMe: Mobile-Ready Distillation of Gaussian Full-Body Avatars

Gaussian-based human avatars have achieved an unprecedented level of visual fidelity. However, existing approaches based on high-capacity neural networks typically require a desktop GPU to achieve real-time performance for a single avatar, and it remains non-trivial to animate and render such avatars on mobile devices including a standalone VR headset due to substantially limited memory and computational bandwidth. In this paper, we present SqueezeMe, a simple and highly effective framework to convert high-fidelity 3D Gaussian full-body avatars into a lightweight representation that supports both animation and rendering with mobile-grade compute. Our key observation is that the decoding of pose-dependent Gaussian attributes from a neural network creates non-negligible memory and computational overhead. Inspired by blendshapes and linear pose correctives widely used in Computer Graphics, we address this by distilling the pose correctives learned with neural networks into linear layers. Moreover, we further reduce the parameters by sharing the correctives among nearby Gaussians. Combining them with a custom splatting pipeline based on Vulkan, we achieve, for the first time, simultaneous animation and rendering of 3 Gaussian avatars in real-time (72 FPS) on a Meta Quest 3 VR headset. Demo videos are available at https://forresti.github.io/squeezeme.

cs.CV↗

arXiv2022

Scaling Neural Face Synthesis to High FPS and Low Latency by Neural Caching

Recent neural rendering approaches greatly improve image quality, reaching near photorealism. However, the underlying neural networks have high runtime, precluding telepresence and virtual reality applications that require high resolution at low latency. The sequential dependency of layers in deep networks makes their optimization difficult. We break this dependency by caching information from the previous frame to speed up the processing of the current one with an implicit warp. The warping with a shallow network reduces latency and the caching operations can further be parallelized to improve the frame rate. In contrast to existing temporal neural networks, ours is tailored for the task of rendering novel views of faces by conditioning on the change of the underlying surface mesh. We test the approach on view-dependent rendering of 3D portrait avatars, as needed for telepresence, on established benchmark sequences. Warping reduces latency by 70$\%$ (from 49.4ms to 14.9ms on commodity GPUs) and scales frame rates accordingly over multiple GPUs while reducing image quality by only 1$\%$, making it suitable as part of end-to-end view-dependent 3D teleconferencing applications. Our project page can be found at: https://yu-frank.github.io/lowlatency/.

cs.CV↗

arXiv2021

A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose

While deep learning reshaped the classical motion capture pipeline with feed-forward networks, generative models are required to recover fine alignment via iterative refinement. Unfortunately, the existing models are usually hand-crafted or learned in controlled conditions, only applicable to limited domains. We propose a method to learn a generative neural body model from unlabelled monocular videos by extending Neural Radiance Fields (NeRFs). We equip them with a skeleton to apply to time-varying and articulated motion. A key insight is that implicit models require the inverse of the forward kinematics used in explicit surface models. Our reparameterization defines spatial latent variables relative to the pose of body parts and thereby overcomes ill-posed inverse operations with an overparameterization. This enables learning volumetric body shape and appearance from scratch while jointly refining the articulated pose; all without ground truth labels for appearance, pose, or 3D shape on the input videos. When used for novel-view-synthesis and motion capture, our neural model improves accuracy on diverse datasets. Project website: https://lemonatsu.github.io/anerf/ .

cs.CV↗

arXiv2020

PCLs: Geometry-aware Neural Reconstruction of 3D Pose with Perspective Crop Layers

Local processing is an essential feature of CNNs and other neural network architectures - it is one of the reasons why they work so well on images where relevant information is, to a large extent, local. However, perspective effects stemming from the projection in a conventional camera vary for different global positions in the image. We introduce Perspective Crop Layers (PCLs) - a form of perspective crop of the region of interest based on the camera geometry - and show that accounting for the perspective consistently improves the accuracy of state-of-the-art 3D pose reconstruction methods. PCLs are modular neural network layers, which, when inserted into existing CNN and MLP architectures, deterministically remove the location-dependent perspective effects while leaving end-to-end training and the number of parameters of the underlying neural network unchanged. We demonstrate that PCL leads to improved 3D human pose reconstruction accuracy for CNN architectures that use cropping operations, such as spatial transformer networks (STN), and, somewhat surprisingly, MLPs used for 2D-to-3D keypoint lifting. Our conclusion is that it is important to utilize camera calibration information when available, for classical and deep-learning-based computer vision alike. PCL offers an easy way to improve the accuracy of existing 3D reconstruction networks by making them geometry aware. Our code is publicly available at github.com/yu-frank/PerspectiveCropLayers.

cs.CV↗

arXiv2020

Few-shot Scene-adaptive Anomaly Detection

We address the problem of anomaly detection in videos. The goal is to identify unusual behaviours automatically by learning exclusively from normal videos. Most existing approaches are usually data-hungry and have limited generalization abilities. They usually need to be trained on a large number of videos from a target scene to achieve good results in that scene. In this paper, we propose a novel few-shot scene-adaptive anomaly detection problem to address the limitations of previous approaches. Our goal is to learn to detect anomalies in a previously unseen scene with only a few frames. A reliable solution for this new problem will have huge potential in real-world applications since it is expensive to collect a massive amount of data for each target scene. We propose a meta-learning based approach for solving this new problem; extensive experimental results demonstrate the effectiveness of our proposed method.

cs.CV↗

arXiv2012

BigFoot: Analysis, monitoring, tracking and sharing of bio-medical features of human appendages using consumer-grade home and office based imaging devices

Here we describe a system for personal and professional management and analysis of bio-medical images captured using off-the-shelf, consumer-grade imaging devices such as scanners, digital cameras, cellphones, webcams and tablet PCs. Specifically, we describe an implementation of this system for the analysis, monitoring and tracking of conditions and features of human feet using a flatbed scanner as the image capture device and a custom-designed set of algorithms and software to manage and analyze the acquired data.

cs.OH↗