Ready-to-React: Online Reaction Policy for Two-Character Interaction Generation

• URL: http://arxiv.org/abs/2502.20370v1
• Date: Thu, 27 Feb 2025 18:40:30 GMT
• Title: Ready-to-React: Online Reaction Policy for Two-Character Interaction Generation
• Authors: Zhi Cen, Huaijin Pi, Sida Peng, Qing Shuai, Yujun Shen, Hujun Bao, Xiaowei Zhou, Ruizhen Hu,
• Abstract summary: We propose an online reaction policy, called Ready-to-React, to generate the next character pose based on past observed motions.<n>Each character has its own reaction policy as its "brain", enabling them to interact like real humans in a streaming manner.<n>Our approach can be controlled by sparse signals, making it well-suited for VR and other online interactive environments.
• Score: 82.73098356401725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper addresses the task of generating two-character online interactions. Previously, two main settings existed for two-character interaction generation: (1) generating one's motions based on the counterpart's complete motion sequence, and (2) jointly generating two-character motions based on specific conditions. We argue that these settings fail to model the process of real-life two-character interactions, where humans will react to their counterparts in real time and act as independent individuals. In contrast, we propose an online reaction policy, called Ready-to-React, to generate the next character pose based on past observed motions. Each character has its own reaction policy as its "brain", enabling them to interact like real humans in a streaming manner. Our policy is implemented by incorporating a diffusion head into an auto-regressive model, which can dynamically respond to the counterpart's motions while effectively mitigating the error accumulation throughout the generation process. We conduct comprehensive experiments using the challenging boxing task. Experimental results demonstrate that our method outperforms existing baselines and can generate extended motion sequences. Additionally, we show that our approach can be controlled by sparse signals, making it well-suited for VR and other online interactive environments.

Related papers

• Dispider: Enabling Video LLMs with Active Real-Time Interaction via Disentangled Perception, Decision, and Reaction [81.34648970317383]
  We present Dispider, a system that disentangles Perception, Decision, and Reaction. Experiments show that Dispider not only maintains strong performance in conventional video QA tasks, but also significantly surpasses previous online models in streaming scenario responses.
  arXiv  Detail & Related papers  (2025-01-06T18:55:10Z)
• InterDyn: Controllable Interactive Dynamics with Video Diffusion Models [50.38647583839384]
  We propose InterDyn, a framework that generates videos of interactive dynamics given an initial frame and a control signal encoding the motion of a driving object or actor. Our key insight is that large video generation models can act as both neurals and implicit physics simulators'', having learned interactive dynamics from large-scale video data.
  arXiv  Detail & Related papers  (2024-12-16T13:57:02Z)
• 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)
• 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)
• MAAIP: Multi-Agent Adversarial Interaction Priors for imitation from fighting demonstrations for physics-based characters [5.303375034962503]
  We propose a novel Multi-Agent Generative Adversarial Imitation Learning based approach. Our system trains control policies allowing each character to imitate the interactive skills associated with each actor. This approach has been tested on two different fighting styles, boxing and full-body martial art, to demonstrate the ability of the method to imitate different styles.
  arXiv  Detail & Related papers  (2023-11-04T20:40:39Z)
• 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)
• MRecGen: Multimodal Appropriate Reaction Generator [31.60823534748163]
  This paper proposes the first multiple and multimodal (verbal and nonverbal) appropriate human reaction generation framework. It can be applied to various human-computer interaction scenarios by generating appropriate virtual agent/robot behaviours.
  arXiv  Detail & Related papers  (2023-07-05T19:07:00Z)
• 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)
• A GAN-Like Approach for Physics-Based Imitation Learning and Interactive Character Control [2.2082422928825136]
  We present a simple and intuitive approach for interactive control of physically simulated characters. Our work builds upon generative adversarial networks (GAN) and reinforcement learning. We highlight the applicability of our approach in a range of imitation and interactive control tasks.
  arXiv  Detail & Related papers  (2021-05-21T00:03:29Z)

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.