✨ArtHOI: Taming Foundation Models for Monocular 4D Reconstruction of Hand-Articulated-Object Interactions

📝 Summary:
ArtHOI presents an optimization-based framework that integrates foundation model priors to reconstruct 4D human-articulated-object interactions from single monocular RGB videos using adaptive sampling...

🔹 Publication Date: Published on Mar 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25791
• PDF: https://arxiv.org/pdf/2603.25791
• Project Page: https://arthoi-reconstruction.github.io/
• Github: https://github.com/hitcs-zikaiwang/ArtHOI-4D-Reconstruction

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#4DReconstruction #FoundationModels #ComputerVision #HumanObjectInteraction #AI

🔥 HOMIE: Human-object Centric Video Personalization via Multimodal Intelligent Enchancement

💡 The paper proposes a framework called HOMIE for human-object centric video personalization, which is a core task in subject-driven video generation. Existing methods suffer from two key limitations: they struggle to balance high subject fidelity and accurate interaction patterns between humans and diverse objects, and they lack mechanisms to understand latent correspondence in intra-subject references. To address these challenges, HOMIE tackles both inter- and intra-subject input settings in a unified manner. The framework proposes a better integration strategy to extract knowledge of reference-level relationships without compromising the controllability of text encoders or incurring costly re-alignment. Specifically, it introduces global multimodal guidance within self-attention to better align semantic features derived from large language models with tokens from variational autoencoders. Additionally, it proposes modality-reference embedding to differentiate tokens from large language model features and variational autoencoder tokens and associate intra-subject reference image tokens. Extensive experiments validate that the method achieves state-of-the-art performance across various human-object centric video personalization tasks. The results demonstrate the effectiveness of HOMIE in addressing the limitations of existing methods and providing a unified framework for video personalization.

📅 Published on Jul 20

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.18217
• PDF: https://arxiv.org/pdf/2607.18217
• Project Page: https://yiyangcai.github.io/homie-page.github.io/

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#HumanCentricVideoPersonalization #MultimodalIntelligentEnhancement #SubjectDrivenVideoGeneration #HumanObjectInteraction #VideoPersonalizationFrameworks