InterAct: A Large-Scale Dataset of Dynamic, Expressive and Interactive Activities between Two People in Daily Scenarios

• URL: http://arxiv.org/abs/2509.05747v1
• Date: Sat, 06 Sep 2025 15:36:47 GMT
• Title: InterAct: A Large-Scale Dataset of Dynamic, Expressive and Interactive Activities between Two People in Daily Scenarios
• Authors: Leo Ho, Yinghao Huang, Dafei Qin, Mingyi Shi, Wangpok Tse, Wei Liu, Junichi Yamagishi, Taku Komura,
• Abstract summary: We propose to simultaneously model two people's activities, and target objective-driven, dynamic, and semantically consistent interactions.<n>We capture a new multi-modal dataset dubbed InterAct composed of 241 motion sequences.<n>InterAct contains diverse and complex motions of individuals and interesting and relatively long-term interaction patterns barely seen before.
• Score: 40.42003202491803
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We address the problem of accurate capture of interactive behaviors between two people in daily scenarios. Most previous works either only consider one person or solely focus on conversational gestures of two people, assuming the body orientation and/or position of each actor are constant or barely change over each interaction. In contrast, we propose to simultaneously model two people's activities, and target objective-driven, dynamic, and semantically consistent interactions which often span longer duration and cover bigger space. To this end, we capture a new multi-modal dataset dubbed InterAct, which is composed of 241 motion sequences where two people perform a realistic and coherent scenario for one minute or longer over a complete interaction. For each sequence, two actors are assigned different roles and emotion labels, and collaborate to finish one task or conduct a common interaction activity. The audios, body motions, and facial expressions of both persons are captured. InterAct contains diverse and complex motions of individuals and interesting and relatively long-term interaction patterns barely seen before. We also demonstrate a simple yet effective diffusion-based method that estimates interactive face expressions and body motions of two people from speech inputs. Our method regresses the body motions in a hierarchical manner, and we also propose a novel fine-tuning mechanism to improve the lip accuracy of facial expressions. To facilitate further research, the data and code is made available at https://hku-cg.github.io/interact/ .

Related papers

• Interact2Ar: Full-Body Human-Human Interaction Generation via Autoregressive Diffusion Models [80.28579390566298]
  We introduce Interact2Ar, a text-conditioned autoregressive diffusion model for generating full-body, human-human interactions.<n>Hand kinematics are incorporated through dedicated parallel branches, enabling high-fidelity full-body generation.<n>Our model enables a series of downstream applications, including temporal motion composition, real-time adaptation to disturbances, and extension beyond dyadic to multi-person scenarios.
  arXiv  Detail & Related papers  (2025-12-22T18:59:50Z)
• 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)
• Multi-Person Interaction Generation from Two-Person Motion Priors [7.253302825595181]
  Graph-driven Interaction Sampling is a method that can generate realistic and diverse multi-person interactions.<n>We decompose the generation task into simultaneous single-person motion generation conditioned on one other's motion.<n>Our approach consistently outperforms existing methods in reducing artifacts when generating a wide range of two-person and multi-person interactions.
  arXiv  Detail & Related papers  (2025-05-23T13:13:00Z)
• It Takes Two: Real-time Co-Speech Two-person's Interaction Generation via Reactive Auto-regressive Diffusion Model [34.94330722832987]
  We introduce an audio-driven, auto-regressive system designed to synthesize dynamic movements for two characters during a conversation.<n>To the best of our knowledge, this is the first system capable of generating interactive full-body motions for two characters from speech in an online manner.
  arXiv  Detail & Related papers  (2024-12-03T12:31:44Z)
• InterAct: Capture and Modelling of Realistic, Expressive and Interactive Activities between Two Persons in Daily Scenarios [12.300105542672163]
  We capture 241 motion sequences where two persons perform a realistic scenario over the whole sequence. The audios, body motions, and facial expressions of both persons are all captured in our dataset. We also demonstrate the first diffusion model based approach that directly estimates the interactive motions between two persons from their audios alone.
  arXiv  Detail & Related papers  (2024-05-19T22:35:02Z)
• 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)
• InterGen: Diffusion-based Multi-human Motion Generation under Complex Interactions [49.097973114627344]
  We present InterGen, an effective diffusion-based approach that incorporates human-to-human interactions into the motion diffusion process. We first contribute a multimodal dataset, named InterHuman. It consists of about 107M frames for diverse two-person interactions, with accurate skeletal motions and 23,337 natural language descriptions. We propose a novel representation for motion input in our interaction diffusion model, which explicitly formulates the global relations between the two performers in the world frame.
  arXiv  Detail & Related papers  (2023-04-12T08:12:29Z)
• Interaction Transformer for Human Reaction Generation [61.22481606720487]
  We propose a novel interaction Transformer (InterFormer) consisting of a Transformer network with both temporal and spatial attentions. Our method is general and can be used to generate more complex and long-term interactions.
  arXiv  Detail & Related papers  (2022-07-04T19:30:41Z)

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.