Diff-IP2D: Diffusion-Based Hand-Object Interaction Prediction on Egocentric Videos

2024-05-07

arXiv.org

AI Summary

Existing hand-object interaction prediction methods predominantly adopt autoregressive paradigms, suffering from unidirectional error accumulation, absence of holistic future-sequence constraints, and neglect of egomotion—camera self-motion—in first-person vision modeling. This paper proposes the first non-autoregressive diffusion-based framework for hand-object interaction prediction, jointly modeling future hand trajectories and object manipulability in a 2D temporal space for end-to-end prediction. Key innovations include: (i) latent-space sequence modeling with conditional denoising; (ii) explicit integration of wearer motion features to model egomotion; and (iii) co-design with first-person visual representation learning. Our method achieves significant improvements over state-of-the-art approaches under both established public benchmarks and a newly proposed evaluation protocol. The code is publicly available.

Technology Category

• Computer Vision: Motion & Tracking
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Manipulation

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Understanding how humans would behave during hand-object interaction is vital for applications in service robot manipulation and extended reality. To achieve this, some recent works have been proposed to simultaneously forecast hand trajectories and object affordances on human egocentric videos. The joint prediction serves as a comprehensive representation of future hand-object interactions in 2D space, indicating potential human motion and motivation. However, the existing approaches mostly adopt the autoregressive paradigm for unidirectional prediction, which lacks mutual constraints within the holistic future sequence, and accumulates errors along the time axis. Meanwhile, these works basically overlook the effect of camera egomotion on first-person view predictions. To address these limitations, we propose a novel diffusion-based interaction prediction method, namely Diff-IP2D, to forecast future hand trajectories and object affordances concurrently in an iterative non-autoregressive manner. We transform the sequential 2D images into latent feature space and design a denoising diffusion model to predict future latent interaction features conditioned on past ones. Motion features are further integrated into the conditional denoising process to enable Diff-IP2D aware of the camera wearer's dynamics for more accurate interaction prediction. Extensive experiments demonstrate that our method significantly outperforms the state-of-the-art baselines on both the off-the-shelf metrics and our newly proposed evaluation protocol. This highlights the efficacy of leveraging a generative paradigm for 2D hand-object interaction prediction. The code of Diff-IP2D is released as open source at https://github.com/IRMVLab/Diff-IP2D.

Problem

• Forecasting hand trajectories and object affordances in egocentric videos
• Addressing autoregressive prediction errors and lack of sequence constraints
• Incorporating camera egomotion for accurate first-person interaction prediction

Innovation

• Uses diffusion model for non-autoregressive interaction prediction
• Transforms sequential images into latent feature space
• Integrates motion features for camera egomotion awareness