99 citations · 257 across the 18 of their papers we have counts for
21 papers
AudioDec: An Open-source Streaming High-fidelity Neural Audio Codec
Yi-Chiao Wu, Israel D. Gebru, Dejan Marković +1
A good audio codec for live applications such as telecommunication is characterized by three key properties: (1) compression, i.e.\ the bitrate that is required to transmit the sig…
Novel-View Acoustic Synthesis
Changan Chen, Alexander Richard, Roman Shapovalov +4
We introduce the novel-view acoustic synthesis (NVAS) task: given the sight and sound observed at a source viewpoint, can we synthesize the sound of that scene from an unseen targe…
End-to-End Binaural Speech Synthesis
Wen Chin Huang, Dejan Markovic, Alexander Richard +2
In this work, we present an end-to-end binaural speech synthesis system that combines a low-bitrate audio codec with a powerful binaural decoder that is capable of accurate speech…
Multiface: A Dataset for Neural Face Rendering
Cheng-hsin Wuu, Ningyuan Zheng, Scott Ardisson +28
Photorealistic avatars of human faces have come a long way in recent years, yet research along this area is limited by a lack of publicly available, high-quality datasets covering…
Implicit Neural Spatial Filtering for Multichannel Source Separation in the Waveform Domain
Dejan Markovic, Alexandre Defossez, Alexander Richard
We present a single-stage casual waveform-to-waveform multichannel model that can separate moving sound sources based on their broad spatial locations in a dynamic acoustic scene.…
Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis
Karren Yang, Dejan Markovic, Steven Krenn +2
Since facial actions such as lip movements contain significant information about speech content, it is not surprising that audio-visual speech enhancement methods are more accurate…
LiP-Flow: Learning Inference-time Priors for Codec Avatars via Normalizing Flows in Latent Space
Emre Aksan, Shugao Ma, Akin Caliskan +5
Neural face avatars that are trained from multi-view data captured in camera domes can produce photo-realistic 3D reconstructions. However, at inference time, they must be driven b…
Conditional Diffusion Probabilistic Model for Speech Enhancement
Yen-Ju Lu, Zhong-Qiu Wang, Shinji Watanabe +3
Speech enhancement is a critical component of many user-oriented audio applications, yet current systems still suffer from distorted and unnatural outputs. While generative models…
Deep Impulse Responses: Estimating and Parameterizing Filters with Deep Networks
Alexander Richard, Peter Dodds, Vamsi Krishna Ithapu
Impulse response estimation in high noise and in-the-wild settings, with minimal control of the underlying data distributions, is a challenging problem. We propose a novel framewor…
MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement
Alexander Richard, Michael Zollhoefer, Yandong Wen +2
This paper presents a generic method for generating full facial 3D animation from speech. Existing approaches to audio-driven facial animation exhibit uncanny or static upper face…
Audio- and Gaze-driven Facial Animation of Codec Avatars
Alexander Richard, Colin Lea, Shugao Ma +3
Codec Avatars are a recent class of learned, photorealistic face models that accurately represent the geometry and texture of a person in 3D (i.e., for virtual reality), and are al…
On Evaluating Weakly Supervised Action Segmentation Methods
Yaser Souri, Alexander Richard, Luca Minciullo +1
Action segmentation is the task of temporally segmenting every frame of an untrimmed video. Weakly supervised approaches to action segmentation, especially from transcripts have be…
A Hybrid RNN-HMM Approach for Weakly Supervised Temporal Action Segmentation
Hilde Kuehne, Alexander Richard, Juergen Gall
Action recognition has become a rapidly developing research field within the last decade. But with the increasing demand for large scale data, the need of hand annotated data for t…
Mining YouTube - A dataset for learning fine-grained action concepts from webly supervised video data
Hilde Kuehne, Ahsan Iqbal, Alexander Richard +1
Action recognition is so far mainly focusing on the problem of classification of hand selected preclipped actions and reaching impressive results in this field. But with the perfor…
NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning
Alexander Richard, Hilde Kuehne, Ahsan Iqbal +1
Video learning is an important task in computer vision and has experienced increasing interest over the recent years. Since even a small amount of videos easily comprises several m…
When will you do what? - Anticipating Temporal Occurrences of Activities
Yazan Abu Farha, Alexander Richard, Juergen Gall
Analyzing human actions in videos has gained increased attention recently. While most works focus on classifying and labeling observed video frames or anticipating the very recent…
Recurrent Residual Learning for Action Recognition
Ahsan Iqbal, Alexander Richard, Hilde Kuehne +1
Action recognition is a fundamental problem in computer vision with a lot of potential applications such as video surveillance, human computer interaction, and robot learning. Give…
Action Sets: Weakly Supervised Action Segmentation without Ordering Constraints
Alexander Richard, Hilde Kuehne, Juergen Gall
Action detection and temporal segmentation of actions in videos are topics of increasing interest. While fully supervised systems have gained much attention lately, full annotation…
A Bag-of-Words Equivalent Recurrent Neural Network for Action Recognition
Alexander Richard, Juergen Gall
The traditional bag-of-words approach has found a wide range of applications in computer vision. The standard pipeline consists of a generation of a visual vocabulary, a quantizati…
Weakly Supervised Action Learning with RNN based Fine-to-coarse Modeling
Alexander Richard, Hilde Kuehne, Juergen Gall
We present an approach for weakly supervised learning of human actions. Given a set of videos and an ordered list of the occurring actions, the goal is to infer start and end frame…
Weakly supervised learning of actions from transcripts
Hilde Kuehne, Alexander Richard, Juergen Gall
We present an approach for weakly supervised learning of human actions from video transcriptions. Our system is based on the idea that, given a sequence of input data and a transcr…