ReCoM: Realistic Co-Speech Motion Generation with Recurrent Embedded Transformer

• URL: http://arxiv.org/abs/2503.21847v1
• Date: Thu, 27 Mar 2025 16:39:40 GMT
• Title: ReCoM: Realistic Co-Speech Motion Generation with Recurrent Embedded Transformer
• Authors: Yong Xie, Yunlian Sun, Hongwen Zhang, Yebin Liu, Jinhui Tang,
• Abstract summary: We present ReCoM, an efficient framework for generating high-fidelity and generalizable human body motions synchronized with speech.<n>The core innovation lies in the Recurrent Embedded Transformer (RET), which integrates Dynamic Embedding Regularization (DER) into a Vision Transformer (ViT) core architecture.<n>To enhance model robustness, we incorporate the proposed DER strategy, which equips the model with dual capabilities of noise resistance and cross-domain generalization.
• Score: 58.49950218437718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present ReCoM, an efficient framework for generating high-fidelity and generalizable human body motions synchronized with speech. The core innovation lies in the Recurrent Embedded Transformer (RET), which integrates Dynamic Embedding Regularization (DER) into a Vision Transformer (ViT) core architecture to explicitly model co-speech motion dynamics. This architecture enables joint spatial-temporal dependency modeling, thereby enhancing gesture naturalness and fidelity through coherent motion synthesis. To enhance model robustness, we incorporate the proposed DER strategy, which equips the model with dual capabilities of noise resistance and cross-domain generalization, thereby improving the naturalness and fluency of zero-shot motion generation for unseen speech inputs. To mitigate inherent limitations of autoregressive inference, including error accumulation and limited self-correction, we propose an iterative reconstruction inference (IRI) strategy. IRI refines motion sequences via cyclic pose reconstruction, driven by two key components: (1) classifier-free guidance improves distribution alignment between generated and real gestures without auxiliary supervision, and (2) a temporal smoothing process eliminates abrupt inter-frame transitions while ensuring kinematic continuity. Extensive experiments on benchmark datasets validate ReCoM's effectiveness, achieving state-of-the-art performance across metrics. Notably, it reduces the Fr\'echet Gesture Distance (FGD) from 18.70 to 2.48, demonstrating an 86.7% improvement in motion realism. Our project page is https://yong-xie-xy.github.io/ReCoM/.

Related papers

• Reconstruction-Anchored Diffusion Model for Text-to-Motion Generation [34.87535133080741]
  Diffusion models have seen widespread adoption for text-driven human motion generation and related tasks.<n>Current motion diffusion models face two major limitations: a representational gap caused by pre-trained text encoders that lack motion-specific information, and error propagation during the iterative denoising process.<n>This paper introduces Reconstruction-Anchored Diffusion Model (RAM) to address these challenges.
  arXiv  Detail & Related papers  (2026-01-21T09:11:45Z)
• Towards Arbitrary Motion Completing via Hierarchical Continuous Representation [64.6525112550758]
  We propose a novel parametric activation-induced hierarchical implicit representation framework, called NAME, based on Implicit Representations (INRs)<n>Our method introduces a hierarchical temporal encoding mechanism that extracts features from motion sequences at multiple temporal scales, enabling effective capture of intricate temporal patterns.
  arXiv  Detail & Related papers  (2025-12-24T14:07:04Z)
• EchoMotion: Unified Human Video and Motion Generation via Dual-Modality Diffusion Transformer [64.69014756863331]
  We introduce EchoMotion, a framework designed to model the joint distribution of appearance and human motion.<n>We also propose MVS-RoPE, which offers unified 3D positional encoding for both video and motion tokens.<n>Our findings reveal that explicitly representing human motion is to appearance, significantly boosting the coherence and plausibility of human-centric video generation.
  arXiv  Detail & Related papers  (2025-12-21T17:08:14Z)
• MotionVerse: A Unified Multimodal Framework for Motion Comprehension, Generation and Editing [53.98607267063729]
  MotionVerse is a framework to comprehend, generate, and edit human motion in both single-person and multi-person scenarios.<n>We employ a motion tokenizer with residual quantization, which converts continuous motion sequences into multi-stream discrete tokens.<n>We also introduce a textitDelay Parallel Modeling strategy, which temporally staggers the encoding of residual token streams.
  arXiv  Detail & Related papers  (2025-09-28T04:20:56Z)
• Bidirectional Feature-aligned Motion Transformation for Efficient Dynamic Point Cloud Compression [97.66080040613726]
  We propose a Bidirectional Feature-aligned Motion Transformation (Bi-FMT) framework that implicitly models motion in the feature space.<n>Bi-FMT aligns features across both past and future frames to produce temporally consistent latent representations.<n>We show Bi-FMT surpasses D-DPCC and AdaDPCC in both compression efficiency and runtime.
  arXiv  Detail & Related papers  (2025-09-18T03:51:06Z)
• M2DAO-Talker: Harmonizing Multi-granular Motion Decoupling and Alternating Optimization for Talking-head Generation [65.08520614570288]
  We reformulate talking head generation into a unified framework comprising video preprocessing, motion representation, and rendering reconstruction.<n>M2DAO-Talker achieves state-of-the-art performance, with the 2.43 dB PSNR improvement in generation quality and 0.64 gain in user-evaluated video realness.
  arXiv  Detail & Related papers  (2025-07-11T04:48:12Z)
• MOGO: Residual Quantized Hierarchical Causal Transformer for High-Quality and Real-Time 3D Human Motion Generation [3.6669020073583756]
  MOGO is a novel autoregressive framework tailored for efficient and real-time 3D motion generation.<n>MoGO comprises two key components: MoSA-VQ, a motion scale-adaptive residual vector quantization module, and RQHC-Transformer, a residual quantized hierarchical causal transformer.<n>To enhance semantic fidelity, we introduce a text condition alignment mechanism that improves motion decoding under textual control.
  arXiv  Detail & Related papers  (2025-06-06T10:26:54Z)
• 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)
• REWIND: Real-Time Egocentric Whole-Body Motion Diffusion with Exemplar-Based Identity Conditioning [95.07708090428814]
  We present REWIND, a one-step diffusion model for real-time, high-fidelity human motion estimation from egocentric image inputs. We introduce cascaded body-hand denoising diffusion, which effectively models the correlation between egocentric body and hand motions. We also propose a novel identity conditioning method based on a small set of pose exemplars of the target identity, which further enhances motion estimation quality.
  arXiv  Detail & Related papers  (2025-04-07T11:44:11Z)
• Streaming Generation of Co-Speech Gestures via Accelerated Rolling Diffusion [0.881371061335494]
  We introduce Accelerated Rolling Diffusion, a novel framework for streaming gesture generation.<n>RDLA restructures the noise schedule into a stepwise ladder, allowing multiple frames to be denoised simultaneously.<n>This significantly improves sampling efficiency while maintaining motion consistency, achieving up to a 2x speedup.
  arXiv  Detail & Related papers  (2025-03-13T15:54:45Z)
• Motion-Aware Generative Frame Interpolation [23.380470636851022]
  Flow-based frame methods ensure motion stability through estimated intermediate flow but often introduce severe artifacts in complex motion regions. Recent generative approaches, boosted by large-scale pre-trained video generation models, show promise in handling intricate scenes. We propose Motion-aware Generative frame (MoG) that synergizes intermediate flow guidance with generative capacities to enhance fidelity.
  arXiv  Detail & Related papers  (2025-01-07T11:03:43Z)
• Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Model [63.336123527432136]
  We introduce Bench2Drive-R, a generative framework that enables reactive closed-loop evaluation.<n>Unlike existing video generative models for autonomous driving, the proposed designs are tailored for interactive simulation.<n>We compare the generation quality of Bench2Drive-R with existing generative models and achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-12-11T06:35:18Z)
• Event-boosted Deformable 3D Gaussians for Dynamic Scene Reconstruction [50.873820265165975]
  We introduce the first approach combining event cameras, which capture high-temporal-resolution, continuous motion data, with deformable 3D-GS for dynamic scene reconstruction.<n>We propose a GS-Threshold Joint Modeling strategy, creating a mutually reinforcing process that greatly improves both 3D reconstruction and threshold modeling.<n>We contribute the first event-inclusive 4D benchmark with synthetic and real-world dynamic scenes, on which our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-11-25T08:23:38Z)
• Motion-compensated MR CINE reconstruction with reconstruction-driven motion estimation [11.432602522235742]
  Motion-compensated MR reconstruction (MCMR) is an effective approach to address highly undersampled acquisitions. We propose a novel perspective for addressing the MCMR problem and a more integrated and efficient solution to the MCMR field. Our approach is unique in that the motion estimation is directly driven by the ultimate goal, reconstruction, but not by the canonical motion-warping loss.
  arXiv  Detail & Related papers  (2023-02-05T22:51:27Z)
• JNMR: Joint Non-linear Motion Regression for Video Frame Interpolation [47.123769305867775]
  Video frame (VFI) aims to generate frames by warping learnable motions from the bidirectional historical references. We reformulate VFI as a Joint Non-linear Motion Regression (JNMR) strategy to model the complicated motions of inter-frame. We show that the effectiveness and significant improvement of joint motion regression compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-09T02:47:29Z)
• Representation Learning for Compressed Video Action Recognition via Attentive Cross-modal Interaction with Motion Enhancement [28.570085937225976]
  This paper proposes a novel framework, namely Attentive Cross-modal Interaction Network with Motion Enhancement. It follows the two-stream architecture, i.e. one for the RGB modality and the other for the motion modality. Experiments on the UCF-101, HMDB-51 and Kinetics-400 benchmarks demonstrate the effectiveness and efficiency of MEACI-Net.
  arXiv  Detail & Related papers  (2022-05-07T06:26:49Z)
• Unsupervised Motion Representation Learning with Capsule Autoencoders [54.81628825371412]
  Motion Capsule Autoencoder (MCAE) models motion in a two-level hierarchy. MCAE is evaluated on a novel Trajectory20 motion dataset and various real-world skeleton-based human action datasets.
  arXiv  Detail & Related papers  (2021-10-01T16:52:03Z)
• Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment [60.31418655784291]
  We propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i.e., both kinematic and visual data, from simulator to real robot. It remedies the domain gap with enhanced transferable features by using temporal cues in videos, and inherent correlations in multi-modal towards recognizing gesture. Results show that our approach recovers the performance with great improvement gains, up to 12.91% in ACC and 20.16% in F1score without using any annotations in real robot.
  arXiv  Detail & Related papers  (2021-03-06T09:10:03Z)

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.