Separate to Collaborate: Dual-Stream Diffusion Model for Coordinated Piano Hand Motion Synthesis
- URL: http://arxiv.org/abs/2504.09885v2
- Date: Thu, 04 Sep 2025 08:25:53 GMT
- Title: Separate to Collaborate: Dual-Stream Diffusion Model for Coordinated Piano Hand Motion Synthesis
- Abstract summary: The synthesis of coordinated bimanual piano performances poses significant challenges.<n>We propose a dual-stream neural framework designed to generate synchronized hand gestures for piano playing from audio input.
- Score: 24.37388595661383
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Automating the synthesis of coordinated bimanual piano performances poses significant challenges, particularly in capturing the intricate choreography between the hands while preserving their distinct kinematic signatures. In this paper, we propose a dual-stream neural framework designed to generate synchronized hand gestures for piano playing from audio input, addressing the critical challenge of modeling both hand independence and coordination. Our framework introduces two key innovations: (i) a decoupled diffusion-based generation framework that independently models each hand's motion via dual-noise initialization, sampling distinct latent noise for each while leveraging a shared positional condition, and (ii) a Hand-Coordinated Asymmetric Attention (HCAA) mechanism suppresses symmetric (common-mode) noise to highlight asymmetric hand-specific features, while adaptively enhancing inter-hand coordination during denoising. Comprehensive evaluations demonstrate that our framework outperforms existing state-of-the-art methods across multiple metrics. Our project is available at https://monkek123king.github.io/S2C_page/.
Related papers
- Polyphony: Diffusion-based Dual-Hand Action Segmentation with Alternating Vision Transformer and Semantic Conditioning [10.658773037388725]
Polyphony is an Alternating Dual-Hand Vision Transformer that alternates training between left and right-hand mini-batches.<n> Polyphony achieves state-the-art on both dual-hand datasets (HA-ViD, ATTACH) with improvements to 16.8 points, and on the single-stream Breakfast dataset (82.5%)<n> Notably, our unified model with a single shared backbone surpasses baselines requiring separate per-hand models.
arXiv Detail & Related papers (2026-05-29T10:26:50Z) - OmniForcing: Unleashing Real-time Joint Audio-Visual Generation [51.031726911410594]
Recent joint audio-visual diffusion models achieve remarkable generation quality but suffer from high latency.<n>We propose OmniForcing, the first framework to distill an offline, dual-stream bidirectional diffusion model into a high-fidelity streaming autore generator.
arXiv Detail & Related papers (2026-03-12T08:17:36Z) - UniHand: A Unified Model for Diverse Controlled 4D Hand Motion Modeling [45.29560152294065]
UniHand is a unified diffusion-based framework that formulates both estimation and generation as conditional motion synthesis.<n>Visual observations are encoded with a frozen backbone, while a dedicated hand perceptron extracts hand-specific cues directly from image features.<n>A latent diffusion model then synthesizes consistent motion sequences from diverse conditions.
arXiv Detail & Related papers (2026-02-25T06:53:15Z) - BHaRNet: Reliability-Aware Body-Hand Modality Expertized Networks for Fine-grained Skeleton Action Recognition [12.973657570368317]
This work presents a probabilistic dual-stream framework that unifies reliability modeling and multi-modal integration, generalizing expertized learning under uncertainty across both intra-skeleton and cross-modal domains.<n>The framework comprises three key components: (1) a calibration-free preprocessing pipeline that removes canonical-space transformations and learns directly from native coordinates; (2) a probabilistic Noisy-OR fusion that stabilizes reliability-aware dual-stream learning without requiring explicit confidence supervision; and (3) an intra- to cross-modal ensemble that couples four skeleton modalities (Joint, Bone, Joint Motion, and Bone Motion) to RGB representations, bridging structural and visual motion
arXiv Detail & Related papers (2026-01-01T15:13:11Z) - Harmony: Harmonizing Audio and Video Generation through Cross-Task Synergy [39.04292189640444]
Harmony is a novel framework that mechanistically enforces audio-visual synchronization.<n>It establishes a new state-of-the-art, significantly outperforming existing methods in both generation fidelity and, critically, in achieving fine-grained audio-visual synchronization.
arXiv Detail & Related papers (2025-11-26T16:53:05Z) - SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation [50.792027578906804]
We introduce SteadyDancer, an Image-to-Video (R2V) paradigm-based framework that achieves harmonized and coherent animation.<n> Experiments demonstrate that SteadyDancer achieves state-of-the-art performance in both appearance fidelity and motion control.
arXiv Detail & Related papers (2025-11-24T17:15:55Z) - AsynFusion: Towards Asynchronous Latent Consistency Models for Decoupled Whole-Body Audio-Driven Avatars [65.53676584955686]
Whole-body audio-driven avatar pose and expression generation is a critical task for creating lifelike digital humans.<n>We propose AsynFusion, a novel framework that leverages diffusion transformers to achieve cohesive expression and gesture synthesis.<n>AsynFusion achieves state-of-the-art performance in generating real-time, synchronized whole-body animations.
arXiv Detail & Related papers (2025-05-21T03:28:53Z) - Aligning Foundation Model Priors and Diffusion-Based Hand Interactions for Occlusion-Resistant Two-Hand Reconstruction [50.952228546326516]
Two-hand reconstruction from monocular images faces persistent challenges due to complex and dynamic hand postures and occlusions.<n>Existing approaches struggle with such alignment issues, often resulting in misalignment and penetration artifacts.<n>We propose a novel framework that attempts to precisely align hand poses and interactions by integrating foundation model-driven 2D priors with diffusion-based interaction refinement.
arXiv Detail & Related papers (2025-03-22T14:42:27Z) - Radio Frequency Signal based Human Silhouette Segmentation: A Sequential Diffusion Approach [26.987963024941635]
We propose a two-stage Sequential Diffusion Model (SDM) to synthesize high-quality segmentation jointly.
Cross-view blocks are devised to guide the diffusion model in a multi-scale manner.
temporal blocks are devised to fine-tune the frame-level model to incorporate frequency-temporal contexts and motion dynamics.
arXiv Detail & Related papers (2024-07-27T12:44:21Z) - InterHandGen: Two-Hand Interaction Generation via Cascaded Reverse Diffusion [53.90516061351706]
We present InterHandGen, a novel framework that learns the generative prior of two-hand interaction.
For sampling, we combine anti-penetration and synthesis-free guidance to enable plausible generation.
Our method significantly outperforms baseline generative models in terms of plausibility and diversity.
arXiv Detail & Related papers (2024-03-26T06:35:55Z) - 3D Hand Reconstruction via Aggregating Intra and Inter Graphs Guided by
Prior Knowledge for Hand-Object Interaction Scenario [8.364378460776832]
We propose a 3D hand reconstruction network combining the benefits of model-based and model-free approaches to balance accuracy and physical plausibility for hand-object interaction scenario.
Firstly, we present a novel MANO pose parameters regression module from 2D joints directly, which avoids the process of highly nonlinear mapping from abstract image feature.
arXiv Detail & Related papers (2024-03-04T05:11:26Z) - D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction [74.49121940466675]
We introduce centroid-fixed dual-stream conditional diffusion for monocular hand-held object reconstruction.
First, to avoid the object centroid from deviating, we utilize a novel hand-constrained centroid fixing paradigm.
Second, we introduce a dual-stream denoiser to semantically and geometrically model hand-object interactions.
arXiv Detail & Related papers (2023-11-23T20:14:50Z) - Spatio-temporal MLP-graph network for 3D human pose estimation [8.267311047244881]
Graph convolutional networks and their variants have shown significant promise in 3D human pose estimation.
We introduce a new weighted Jacobi feature rule obtained through graph filtering with implicit propagation fairing.
We also employ adjacency modulation with the aim of learning meaningful correlations beyond defined between body joints.
arXiv Detail & Related papers (2023-08-29T14:00:55Z) - Language-free Compositional Action Generation via Decoupling Refinement [67.50452446686725]
We introduce a novel framework to generate compositional actions without reliance on language auxiliaries.
Our approach consists of three main components: Action Coupling, Conditional Action Generation, and Decoupling Refinement.
arXiv Detail & Related papers (2023-07-07T12:00:38Z) - Object Segmentation by Mining Cross-Modal Semantics [68.88086621181628]
We propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features.
Specifically, we propose a novel network, termed XMSNet, consisting of (1) all-round attentive fusion (AF), (2) coarse-to-fine decoder (CFD), and (3) cross-layer self-supervision.
arXiv Detail & Related papers (2023-05-17T14:30:11Z) - A Non-Anatomical Graph Structure for isolated hand gesture separation in
continuous gesture sequences [42.20687552354674]
We propose a GCN model and combine it with the stacked Bi-LSTM and Attention modules to push the temporal information in the video stream.
Considering the breakthroughs of GCN models for skeleton modality, we propose a two-layer GCN model to empower the 3D hand skeleton features.
arXiv Detail & Related papers (2022-07-15T17:28:52Z) - Real-time Pose and Shape Reconstruction of Two Interacting Hands With a
Single Depth Camera [79.41374930171469]
We present a novel method for real-time pose and shape reconstruction of two strongly interacting hands.
Our approach combines an extensive list of favorable properties, namely it is marker-less.
We show state-of-the-art results in scenes that exceed the complexity level demonstrated by previous work.
arXiv Detail & Related papers (2021-06-15T11:39:49Z) - Learning Joint Articulatory-Acoustic Representations with Normalizing
Flows [7.183132975698293]
We find a joint latent representation between the articulatory and acoustic domain for vowel sounds via invertible neural network models.
Our approach achieves both articulatory-to-acoustic as well as acoustic-to-articulatory mapping, thereby demonstrating our success in achieving a joint encoding of both the domains.
arXiv Detail & Related papers (2020-05-16T04:34:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.