arXiv:2405.04370v5 Announce Type: replace 
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.

تناقش الورقة المعنونة 'Diff-IP2D: توقع تفاعل اليد-الشيء القائم على الانتشار في مقاطع الفيديو الذاتية' طريقة جديدة لتوقع تفاعلات اليد-الشيء في مقاطع الفيديو الذاتية. تهدف هذه الطريقة، المسماة Diff-IP2D، إلى تحسين دقة التنبؤ بمسارات اليد وخصائص الأشياء، مع معالجة القيود التي تواجه النماذج التلقائية الحالية. تبرز الأبحاث أهمية فهم سلوك البشر في تفاعلات اليد-الشيء لتطبيقات في تشغيل الروبوتات الخدمية والواقع الممتد.

El artículo titulado 'Diff-IP2D: Predicción de Interacción Mano-Objeto Basada en Difusión en Videos Egocéntricos' presenta un nuevo método para predecir interacciones mano-objeto en videos egocéntricos. Este método, denominado Diff-IP2D, busca mejorar la precisión en la previsión de trayectorias de mano y affordances de objetos, abordando las limitaciones de los modelos autorregresivos existentes. La investigación destaca la importancia de entender el comportamiento humano en las interacciones mano-objeto para aplicaciones en la manipulación de robots de servicio y realidad extendida.

L'article intitulé 'Diff-IP2D: Prédiction d'interaction main-objet basée sur la diffusion dans des vidéos égocentriques' présente une nouvelle méthode pour prédire les interactions main-objet dans des vidéos égocentriques. Cette méthode, nommée Diff-IP2D, vise à améliorer la précision de la prévision des trajectoires de main et des affordances d'objet, en abordant les limitations des modèles autoregressifs existants. La recherche souligne l'importance de comprendre le comportement humain dans les interactions main-objet pour des applications dans la manipulation de robots de service et la réal…

The paper titled 'Diff-IP2D: Diffusion-Based Hand-Object Interaction Prediction on Egocentric Videos' discusses a new method for predicting hand-object interactions in egocentric videos. This method, named Diff-IP2D, aims to improve the accuracy of forecasting hand trajectories and object affordances, addressing limitations of existing autoregressive models. The research highlights the importance of understanding human behavior in hand-object interactions for applications in service robots and extended reality.

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

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