arXiv subjects

Congyi Wang

Publications and source records attributed to Congyi Wang.

11 recordsLinked to original sources

arXiv2024

Time-Varying Strong Coupling and Its Induced Time Diffraction of Magnon Modes

Time-varying media break the temporal translation symmetry of wave propagation in materials, enabling advanced wave manipulations. However, this novel phenomenon has been rarely explored in magnonic systems due to the significant challenge of achieving a sudden and prominent change in magnon dispersion within materials. Here, we construct a time-varying strong coupling between two magnon modes, and observe a change in the beats of Rabi-like oscillations near the pulse edges. Using a frequency-comb spectroscopy technique developed in this work, we characterize the frequency conversion of magnon modes induced by the time-varying strong-coupling effect. Moreover, we construct time slits with adjacent time interfaces and demonstrate, for the first time, the double-slit time diffraction of magnon modes, analogous to the well-known Young's double-slit experiment. These findings rely solely on the time-varying strong magnon coupling, independent of device reconfiguration. Our results open avenues for applications such as all-magnetic mixers or on-chip GHz sources.

cond-mat.mes-hall↗

arXiv2023

T2M-HiFiGPT: Generating High Quality Human Motion from Textual Descriptions with Residual Discrete Representations

In this study, we introduce T2M-HiFiGPT, a novel conditional generative framework for synthesizing human motion from textual descriptions. This framework is underpinned by a Residual Vector Quantized Variational AutoEncoder (RVQ-VAE) and a double-tier Generative Pretrained Transformer (GPT) architecture. We demonstrate that our CNN-based RVQ-VAE is capable of producing highly accurate 2D temporal-residual discrete motion representations. Our proposed double-tier GPT structure comprises a temporal GPT and a residual GPT. The temporal GPT efficiently condenses information from previous frames and textual descriptions into a 1D context vector. This vector then serves as a context prompt for the residual GPT, which generates the final residual discrete indices. These indices are subsequently transformed back into motion data by the RVQ-VAE decoder. To mitigate the exposure bias issue, we employ straightforward code corruption techniques for RVQ and a conditional dropout strategy, resulting in enhanced synthesis performance. Remarkably, T2M-HiFiGPT not only simplifies the generative process but also surpasses existing methods in both performance and parameter efficacy, including the latest diffusion-based and GPT-based models. On the HumanML3D and KIT-ML datasets, our framework achieves exceptional results across nearly all primary metrics. We further validate the efficacy of our framework through comprehensive ablation studies on the HumanML3D dataset, examining the contribution of each component. Our findings reveal that RVQ-VAE is more adept at capturing precise 3D human motion with comparable computational demand compared to its VQ-VAE counterparts. As a result, T2M-HiFiGPT enables the generation of human motion with significantly increased accuracy, outperforming recent state-of-the-art approaches such as T2M-GPT and Att-T2M.

cs.CV↗

arXiv2023

HandMIM: Pose-Aware Self-Supervised Learning for 3D Hand Mesh Estimation

With an enormous number of hand images generated over time, unleashing pose knowledge from unlabeled images for supervised hand mesh estimation is an emerging yet challenging topic. To alleviate this issue, semi-supervised and self-supervised approaches have been proposed, but they are limited by the reliance on detection models or conventional ResNet backbones. In this paper, inspired by the rapid progress of Masked Image Modeling (MIM) in visual classification tasks, we propose a novel self-supervised pre-training strategy for regressing 3D hand mesh parameters. Our approach involves a unified and multi-granularity strategy that includes a pseudo keypoint alignment module in the teacher-student framework for learning pose-aware semantic class tokens. For patch tokens with detailed locality, we adopt a self-distillation manner between teacher and student network based on MIM pre-training. To better fit low-level regression tasks, we incorporate pixel reconstruction tasks for multi-level representation learning. Additionally, we design a strong pose estimation baseline using a simple vanilla vision Transformer (ViT) as the backbone and attach a PyMAF head after tokens for regression. Extensive experiments demonstrate that our proposed approach, named HandMIM, achieves strong performance on various hand mesh estimation tasks. Notably, HandMIM outperforms specially optimized architectures, achieving 6.29mm and 8.00mm PAVPE (Vertex-Point-Error) on challenging FreiHAND and HO3Dv2 test sets, respectively, establishing new state-of-the-art records on 3D hand mesh estimation.

cs.CV↗

arXiv2023

Giant Enhancement of Magnonic Frequency Combs by Exceptional Points

With their incomparable time-frequency accuracy, frequency combs have significantly advanced precision spectroscopy, ultra-sensitive detection, and atomic clocks. Traditional methods to create photonic, phononic, and magnonic frequency combs hinge on material nonlinearities which are often weak, necessitating high power densities to surpass their initiation thresholds, which subsequently limits their applications. Here, we introduce a novel nonlinear process to efficiently generate magnonic frequency combs (MFCs) by exploiting exceptional points (EPs) in a coupled system comprising a pump-induced magnon mode and a Kittel mode. Even without any cavity, our method greatly improves the efficiency of nonlinear frequency conversion and achieves optimal MFCs at low pump power. Additionally, our novel nonlinear process enables excellent tunability of EPs using the polarization and power of the pump, simplifying MFC generation and manipulation. Our work establishes a synergistic relationship between non-Hermitian physics and MFCs, which is advantages for coherent/quantum information processing and ultra-sensitive detection.

cond-mat.mes-hall↗

arXiv2023

MeMaHand: Exploiting Mesh-Mano Interaction for Single Image Two-Hand Reconstruction

Existing methods proposed for hand reconstruction tasks usually parameterize a generic 3D hand model or predict hand mesh positions directly. The parametric representations consisting of hand shapes and rotational poses are more stable, while the non-parametric methods can predict more accurate mesh positions. In this paper, we propose to reconstruct meshes and estimate MANO parameters of two hands from a single RGB image simultaneously to utilize the merits of two kinds of hand representations. To fulfill this target, we propose novel Mesh-Mano interaction blocks (MMIBs), which take mesh vertices positions and MANO parameters as two kinds of query tokens. MMIB consists of one graph residual block to aggregate local information and two transformer encoders to model long-range dependencies. The transformer encoders are equipped with different asymmetric attention masks to model the intra-hand and inter-hand attention, respectively. Moreover, we introduce the mesh alignment refinement module to further enhance the mesh-image alignment. Extensive experiments on the InterHand2.6M benchmark demonstrate promising results over the state-of-the-art hand reconstruction methods.

cs.CV↗

arXiv2021

Deep3DPose: Realtime Reconstruction of Arbitrarily Posed Human Bodies from Single RGB Images

We introduce an approach that accurately reconstructs 3D human poses and detailed 3D full-body geometric models from single images in realtime. The key idea of our approach is a novel end-to-end multi-task deep learning framework that uses single images to predict five outputs simultaneously: foreground segmentation mask, 2D joints positions, semantic body partitions, 3D part orientations and uv coordinates (uv map). The multi-task network architecture not only generates more visual cues for reconstruction, but also makes each individual prediction more accurate. The CNN regressor is further combined with an optimization based algorithm for accurate kinematic pose reconstruction and full-body shape modeling. We show that the realtime reconstruction reaches accurate fitting that has not been seen before, especially for wild images. We demonstrate the results of our realtime 3D pose and human body reconstruction system on various challenging in-the-wild videos. We show the system advances the frontier of 3D human body and pose reconstruction from single images by quantitative evaluations and comparisons with state-of-the-art methods.

cs.CV↗

arXiv2021

Improve GAN-based Neural Vocoder using Pointwise Relativistic LeastSquare GAN

GAN-based neural vocoders, such as Parallel WaveGAN and MelGAN have attracted great interest due to their lightweight and parallel structures, enabling them to generate high fidelity waveform in a real-time manner. In this paper, inspired by Relativistic GAN, we introduce a novel variant of the LSGAN framework under the context of waveform synthesis, named Pointwise Relativistic LSGAN (PRLSGAN). In this approach, we take the truism score distribution into consideration and combine the original MSE loss with the proposed pointwise relative discrepancy loss to increase the difficulty of the generator to fool the discriminator, leading to improved generation quality. Moreover, PRLSGAN is a general-purposed framework that can be combined with any GAN-based neural vocoder to enhance its generation quality. Experiments have shown a consistent performance boost based on Parallel WaveGAN and MelGAN, demonstrating the effectiveness and strong generalization ability of our proposed PRLSGAN neural vocoders.

cs.SD↗

arXiv2021

Polyphone Disambiguation in Mandarin Chinese with Semi-Supervised Learning

The majority of Chinese characters are monophonic, while a special group of characters, called polyphonic characters, have multiple pronunciations. As a prerequisite of performing speech-related generative tasks, the correct pronunciation must be identified among several candidates. This process is called Polyphone Disambiguation. Although the problem has been well explored with both knowledge-based and learning-based approaches, it remains challenging due to the lack of publicly available labeled datasets and the irregular nature of polyphone in Mandarin Chinese. In this paper, we propose a novel semi-supervised learning (SSL) framework for Mandarin Chinese polyphone disambiguation that can potentially leverage unlimited unlabeled text data. We explore the effect of various proxy labeling strategies including entropy-thresholding and lexicon-based labeling. Qualitative and quantitative experiments demonstrate that our method achieves state-of-the-art performance. In addition, we publish a novel dataset specifically for the polyphone disambiguation task to promote further research.

cs.CL↗

arXiv2021

A causal convolutional neural network for multi-subject motion modeling and generation

Inspired by the success of WaveNet in multi-subject speech synthesis, we propose a novel neural network based on causal convolutions for multi-subject motion modeling and generation. The network can capture the intrinsic characteristics of the motion of different subjects, such as the influence of skeleton scale variation on motion style. Moreover, after fine-tuning the network using a small motion dataset for a novel skeleton that is not included in the training dataset, it is able to synthesize high-quality motions with a personalized style for the novel skeleton. The experimental results demonstrate that our network can model the intrinsic characteristics of motions well and can be applied to various motion modeling and synthesis tasks.

cs.GR↗

arXiv2020

Towards 3D Dance Motion Synthesis and Control

3D human dance motion is a cooperative and elegant social movement. Unlike regular simple locomotion, it is challenging to synthesize artistic dance motions due to the irregularity, kinematic complexity and diversity. It requires the synthesized dance is realistic, diverse and controllable. In this paper, we propose a novel generative motion model based on temporal convolution and LSTM,TC-LSTM, to synthesize realistic and diverse dance motion. We introduce a unique control signal, dance melody line, to heighten controllability. Hence, our model, and its switch for control signals, promote a variety of applications: random dance synthesis, music-to-dance, user control, and more. Our experiments demonstrate that our model can synthesize artistic dance motion in various dance types. Compared with existing methods, our method achieved start-of-the-art results.

cs.GR↗

arXiv2020

Music2Dance: DanceNet for Music-driven Dance Generation

Synthesize human motions from music, i.e., music to dance, is appealing and attracts lots of research interests in recent years. It is challenging due to not only the requirement of realistic and complex human motions for dance, but more importantly, the synthesized motions should be consistent with the style, rhythm and melody of the music. In this paper, we propose a novel autoregressive generative model, DanceNet, to take the style, rhythm and melody of music as the control signals to generate 3D dance motions with high realism and diversity. To boost the performance of our proposed model, we capture several synchronized music-dance pairs by professional dancers, and build a high-quality music-dance pair dataset. Experiments have demonstrated that the proposed method can achieve the state-of-the-art results.

cs.CV↗