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New diffusion model forecasts multi-person motion in complex scenes

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Object-Conditioned Social Diffusion (OCSD), a new conditional diffusion model designed to improve multi-person human motion forecasting in complex environments. OCSD integrates motion history, inter-person interactions, and object cues into a unified framework. The model utilizes an object-conditioning mechanism for fine-grained human-object reasoning and a social encoder to model interactions between individuals. Experiments demonstrate OCSD's state-of-the-art performance on the Humans in Kitchens (HiK) and HOI-M3 benchmarks, significantly reducing path errors and producing more realistic long-term forecasts. AI

IMPACT This research advances AI's ability to predict human behavior in complex environments, potentially impacting robotics and autonomous systems.

RANK_REASON The cluster contains an academic paper detailing a new model and benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New diffusion model forecasts multi-person motion in complex scenes

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24 / 100
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The cluster contains an academic paper detailing a new model and benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Serdar Ozsoy, Lars Doorenbos, Juergen Gall ·

    Multi-Person Human Motion Forecasting in Complex Scenes

    arXiv:2608.27039v1 Announce Type: cross Abstract: Accurately forecasting the movement of people in complex scenes requires reasoning over the past and present state of the entire environment. In this context, effectively incorporating object information and social interactions in…

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