InterSyn: Interleaved Learning for Dynamic Motion Synthesis in the Wild

• URL: http://arxiv.org/abs/2508.10297v1
• Date: Thu, 14 Aug 2025 03:00:06 GMT
• Title: InterSyn: Interleaved Learning for Dynamic Motion Synthesis in the Wild
• Authors: Yiyi Ma, Yuanzhi Liang, Xiu Li, Chi Zhang, Xuelong Li,
• Abstract summary: We present Interleaved Learning for Motion Synthesis (InterSyn), a novel framework that targets the generation of realistic interaction motions.<n>InterSyn employs an interleaved learning strategy to capture the natural, dynamic interactions and nuanced coordination inherent in real-world scenarios.
• Score: 65.29569330744056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Interleaved Learning for Motion Synthesis (InterSyn), a novel framework that targets the generation of realistic interaction motions by learning from integrated motions that consider both solo and multi-person dynamics. Unlike previous methods that treat these components separately, InterSyn employs an interleaved learning strategy to capture the natural, dynamic interactions and nuanced coordination inherent in real-world scenarios. Our framework comprises two key modules: the Interleaved Interaction Synthesis (INS) module, which jointly models solo and interactive behaviors in a unified paradigm from a first-person perspective to support multiple character interactions, and the Relative Coordination Refinement (REC) module, which refines mutual dynamics and ensures synchronized motions among characters. Experimental results show that the motion sequences generated by InterSyn exhibit higher text-to-motion alignment and improved diversity compared with recent methods, setting a new benchmark for robust and natural motion synthesis. Additionally, our code will be open-sourced in the future to promote further research and development in this area.

Related papers

• InteracTalker: Prompt-Based Human-Object Interaction with Co-Speech Gesture Generation [1.7523719472700858]
  We introduce InteracTalker, a novel framework that seamlessly integrates prompt-based object-aware interactions with co-speech gesture generation.<n>Our framework utilizes a Generalized Motion Adaptation Module that enables independent training, adapting to the corresponding motion condition.<n>InteracTalker successfully unifies these previously separate tasks, outperforming prior methods in both co-speech gesture generation and object-interaction synthesis.
  arXiv  Detail & Related papers  (2025-12-14T12:29:49Z)
• Uni-Inter: Unifying 3D Human Motion Synthesis Across Diverse Interaction Contexts [59.78384600454231]
  We present Uni-Inter, a unified framework for human motion generation that supports a wide range of interaction scenarios.<n>Uni-Inter introduces the Unified Interactive Volume (UIV), a volumetric representation that encodes heterogeneous interactive entities into a shared spatial field.
  arXiv  Detail & Related papers  (2025-11-17T06:32:38Z)
• MoReact: Generating Reactive Motion from Textual Descriptions [57.642436102978245]
  MoReact is a diffusion-based method designed to disentangle the generation of global trajectories and local motions sequentially.<n>Our experiments, utilizing data adapted from a two-person motion dataset, demonstrate the efficacy of our approach.
  arXiv  Detail & Related papers  (2025-09-28T14:31:41Z)
• 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)
• SyncDiff: Synchronized Motion Diffusion for Multi-Body Human-Object Interaction Synthesis [22.14972920585117]
  We introduce SyncDiff, a novel method for multi-body interaction synthesis using a synchronized motion diffusion strategy.<n>To enhance motion fidelity, we propose a frequency-domain motion decomposition scheme.<n>We also introduce a new set of alignment scores to emphasize the synchronization of different body motions.
  arXiv  Detail & Related papers  (2024-12-28T10:12:12Z)
• A Unified Framework for Motion Reasoning and Generation in Human Interaction [28.736843383405603]
  We introduce Versatile Interactive Motion-language model, which integrates both language and motion modalities.<n>VIM is capable of simultaneously understanding and generating both motion and text modalities.<n>We evaluate VIM across multiple interactive motion-related tasks, including motion-to-text, text-to-motion, reaction generation, motion editing, and reasoning about motion sequences.
  arXiv  Detail & Related papers  (2024-10-08T02:23:53Z)
• TextIM: Part-aware Interactive Motion Synthesis from Text [25.91739105467082]
  TextIM is a novel framework for synthesizing TEXT-driven human Interactive Motions. Our approach leverages large language models, functioning as a human brain, to identify interacting human body parts. For training and evaluation, we carefully selected and re-labeled interactive motions from HUMANML3D to develop a specialized dataset.
  arXiv  Detail & Related papers  (2024-08-06T17:08:05Z)
• ReMoS: 3D Motion-Conditioned Reaction Synthesis for Two-Person Interactions [66.87211993793807]
  We present ReMoS, a denoising diffusion based model that synthesizes full body motion of a person in two person interaction scenario. We demonstrate ReMoS across challenging two person scenarios such as pair dancing, Ninjutsu, kickboxing, and acrobatics. We also contribute the ReMoCap dataset for two person interactions containing full body and finger motions.
  arXiv  Detail & Related papers  (2023-11-28T18:59:52Z)
• InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint [67.6297384588837]
  We introduce a novel controllable motion generation method, InterControl, to encourage the synthesized motions maintaining the desired distance between joint pairs. We demonstrate that the distance between joint pairs for human-wise interactions can be generated using an off-the-shelf Large Language Model.
  arXiv  Detail & Related papers  (2023-11-27T14:32:33Z)
• Persistent-Transient Duality: A Multi-mechanism Approach for Modeling Human-Object Interaction [58.67761673662716]
  Humans are highly adaptable, swiftly switching between different modes to handle different tasks, situations and contexts. In Human-object interaction (HOI) activities, these modes can be attributed to two mechanisms: (1) the large-scale consistent plan for the whole activity and (2) the small-scale children interactive actions that start and end along the timeline. This work proposes to model two concurrent mechanisms that jointly control human motion.
  arXiv  Detail & Related papers  (2023-07-24T12:21:33Z)
• SoMoFormer: Social-Aware Motion Transformer for Multi-Person Motion Prediction [10.496276090281825]
  We propose a novel Social-Aware Motion Transformer (SoMoFormer) to model individual motion and social interactions in a joint manner. SoMoFormer extracts motion features from sub-sequences in displacement trajectory space to learn both local and global pose dynamics for each individual. In addition, we devise a novel social-aware motion attention mechanism in SoMoFormer to further optimize dynamics representations and capture interaction dependencies simultaneously.
  arXiv  Detail & Related papers  (2022-08-19T08:57:34Z)

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.