Dyadic Interaction Modeling for Social Behavior Generation

Human-human communication is like a delicate dance where listeners and speakers concurrently interact to maintain conversational dynamics. Hence, an effective model for generating listener nonverbal behaviors requires understanding the dyadic context and interaction. In this paper, we present an effective framework for creating 3D facial motions in dyadic interactions. Existing work consider a listener as a reactive agent with reflexive behaviors to the speaker's voice and facial motions. The heart of our framework is Dyadic Interaction Modeling (DIM), a pre-training approach that jointly models speakers' and listeners' motions through masking and contrastive learning to learn representations that capture the dyadic context. To enable the generation of non-deterministic behaviors, we encode both listener and speaker motions into discrete latent representations, through VQ-VAE. The pre-trained model is further fine-tuned for motion generation. Extensive experiments demonstrate the superiority of our framework in generating listener motions, establishing a new state-of-the-art according to the quantitative measures capturing the diversity and realism of generated motions. Qualitative results demonstrate the superior capabilities of the proposed approach in generating diverse and realistic expressions, eye blinks and head gestures.

Leveraging Synthetic Data for Generalizable and Fair Facial Action Unit Detection

Facial action unit (AU) detection is a fundamental block for objective facial expression analysis. Supervised learning approaches require a large amount of manual labeling which is costly. The limited labeled data are also not diverse in terms of gender which can affect model fairness. In this paper, we propose to use synthetically generated data and multi-source domain adaptation (MSDA) to address the problems of the scarcity of labeled data and the diversity of subjects. Specifically, we propose to generate a diverse dataset through synthetic facial expression re-targeting by transferring the expressions from real faces to synthetic avatars. Then, we use MSDA to transfer the AU detection knowledge from a real dataset and the synthetic dataset to a target dataset. Instead of aligning the overall distributions of different domains, we propose Paired Moment Matching (PM2) to align the features of the paired real and synthetic data with the same facial expression. To further improve gender fairness, PM2 matches the features of the real data with a female and a male synthetic image. Our results indicate that synthetic data and the proposed model improve both AU detection performance and fairness across genders, demonstrating its potential to solve AU detection in-the-wild.

Optimization of the Downlink Spectral- and Energy-Efficiency of RIS-aided Multi-user URLLC MIMO Systems

Modern wireless communication systems are expected to provide improved latency and reliability. To meet these expectations, a short packet length is needed, which makes the first-order Shannon rate an inaccurate performance metric for such communication systems. A more accurate approximation of the achievable rates of finite-block-length (FBL) coding regimes is known as the normal approximation (NA). It is therefore of substantial interest to study the optimization of the FBL rate in multi-user multiple-input multiple-output (MIMO) systems, in which each user may transmit and/or receive multiple data streams. Hence, we formulate a general optimization problem for improving the spectral and energy efficiency of multi-user MIMO-aided ultra-reliable low-latency communication (URLLC) systems, which are assisted by reconfigurable intelligent surfaces (RISs). We show that a RIS is capable of substantially improving the performance of multi-user MIMO-aided URLLC systems. Moreover, the benefits of RIS increase as the packet length and/or the tolerable bit error rate are reduced. This reveals that RISs can be even more beneficial in URLLC systems for improving the FBL rates than in conventional systems approaching Shannon rates.

Maximizing Spectral and Energy Efficiency in Multi-user MIMO OFDM Systems with RIS and Hardware Impairment

An emerging technology to enhance the spectral efficiency (SE) and energy efficiency (EE) of wireless communication systems is reconfigurable intelligent surface (RIS), which is shown to be very powerful in single-carrier systems. However, in multi-user orthogonal frequency division multiplexing (OFDM) systems, RIS may not be as promising as in single-carrier systems since an independent optimization of RIS elements at each sub-carrier is impossible in multi-carrier systems. Thus, this paper investigates the performance of various RIS technologies like regular (reflective and passive), simultaneously transmit and reflect (STAR), and multi-sector beyond diagonal (BD) RIS in multi-user multiple-input multiple-output (MIMO) OFDM broadcast channels (BC). This requires to formulate and solve a joint MIMO precoding and RIS optimization problem. The obtained solution reveals that RIS can significantly improve the system performance even when the number of RIS elements is relatively low. Moreover, we develop resource allocation schemes for STAR-RIS and multi-sector BD-RIS in MIMO OFDM BCs, and show that these RIS technologies can outperform a regular RIS, especially when the regular RIS cannot assist the communications for all the users.

MagicDance: Realistic Human Dance Video Generation with Motions & Facial Expressions Transfer

In this work, we propose MagicDance, a diffusion-based model for 2D human motion and facial expression transfer on challenging human dance videos. Specifically, we aim to generate human dance videos of any target identity driven by novel pose sequences while keeping the identity unchanged. To this end, we propose a two-stage training strategy to disentangle human motions and appearance (e.g., facial expressions, skin tone and dressing), consisting of the pretraining of an appearance-control block and fine-tuning of an appearance-pose-joint-control block over human dance poses of the same dataset. Our novel design enables robust appearance control with temporally consistent upper body, facial attributes, and even background. The model also generalizes well on unseen human identities and complex motion sequences without the need for any fine-tuning with additional data with diverse human attributes by leveraging the prior knowledge of image diffusion models. Moreover, the proposed model is easy to use and can be considered as a plug-in module/extension to Stable Diffusion. We also demonstrate the model's ability for zero-shot 2D animation generation, enabling not only the appearance transfer from one identity to another but also allowing for cartoon-like stylization given only pose inputs. Extensive experiments demonstrate our superior performance on the TikTok dataset.

Privacy-preserving Representation Learning for Speech Understanding

Existing privacy-preserving speech representation learning methods target a single application domain. In this paper, we present a novel framework to anonymize utterance-level speech embeddings generated by pre-trained encoders and show its effectiveness for a range of speech classification tasks. Specifically, given the representations from a pre-trained encoder, we train a Transformer to estimate the representations for the same utterances spoken by other speakers. During inference, the extracted representations can be converted into different identities to preserve privacy. We compare the results with the voice anonymization baselines from the VoicePrivacy 2022 challenge. We evaluate our framework on speaker identification for privacy and emotion recognition, depression classification, and intent classification for utility. Our method outperforms the baselines on privacy and utility in paralinguistic tasks and achieves comparable performance for intent classification.

Maximization of minimum rate in MIMO OFDM RIS-assisted Broadcast Channels

Reconfigurable intelligent surface (RIS) is a promising technology to enhance the spectral efficiency of wireless communication systems. By optimizing the RIS elements, the performance of the overall system can be improved. Yet, in contrast to single-carrier systems, in multi-carrier systems, it is not possible to independently optimize RIS elements at each sub-carrier, which may reduce the benefits of RIS in multi-user orthogonal frequency division multiplexing (OFDM) systems. To this end, we investigate the effectiveness of RIS in multiple-input, multiple-output (MIMO) OFDM broadcast channels (BC). We formulate and solve a joint precoding and RIS optimization problem. We show that RIS can significantly improve the system performance even when the number of RIS elements per sub-band is very low.

Personalized Adaptation with Pre-trained Speech Encoders for Continuous Emotion Recognition

There are individual differences in expressive behaviors driven by cultural norms and personality. This between-person variation can result in reduced emotion recognition performance. Therefore, personalization is an important step in improving the generalization and robustness of speech emotion recognition. In this paper, to achieve unsupervised personalized emotion recognition, we first pre-train an encoder with learnable speaker embeddings in a self-supervised manner to learn robust speech representations conditioned on speakers. Second, we propose an unsupervised method to compensate for the label distribution shifts by finding similar speakers and leveraging their label distributions from the training set. Extensive experimental results on the MSP-Podcast corpus indicate that our method consistently outperforms strong personalization baselines and achieves state-of-the-art performance for valence estimation.

LibreFace: An Open-Source Toolkit for Deep Facial Expression Analysis

Facial expression analysis is an important tool for human-computer interaction. In this paper, we introduce LibreFace, an open-source toolkit for facial expression analysis. This open-source toolbox offers real-time and offline analysis of facial behavior through deep learning models, including facial action unit (AU) detection, AU intensity estimation, and facial expression recognition. To accomplish this, we employ several techniques, including the utilization of a large-scale pre-trained network, feature-wise knowledge distillation, and task-specific fine-tuning. These approaches are designed to effectively and accurately analyze facial expressions by leveraging visual information, thereby facilitating the implementation of real-time interactive applications. In terms of Action Unit (AU) intensity estimation, we achieve a Pearson Correlation Coefficient (PCC) of 0.63 on DISFA, which is 7% higher than the performance of OpenFace 2.0 while maintaining highly-efficient inference that runs two times faster than OpenFace 2.0. Despite being compact, our model also demonstrates competitive performance to state-of-the-art facial expression analysis methods on AffecNet, FFHQ, and RAF-DB. Our code will be released at https://github.com/ihp-lab/LibreFace