Multi-Person Human Motion Forecasting in Complex Scenes
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 into a unified framework remains particularly challenging. To address this, we propose Object-Conditioned Social Diffusion (OCSD), a conditional diffusion model that integrates motion history, multi-person interactions, and object cues into a single framework. OCSD uses an object-conditioning mechanism that modulates denoising at every timestep, enabling fine-grained human-object reasoning, and a social encoder that models the interactions between all humans in the...
| Authors | Serdar Ozsoy, Lars Doorenbos, Juergen Gall |
|---|---|
| Primary category | cs.CV |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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