Asset-Driven Sematic Reconstruction of Dynamic Scene with Multi-Human-Object Interactions

• URL: http://arxiv.org/abs/2512.00547v1
• Date: Sat, 29 Nov 2025 16:36:22 GMT
• Title: Asset-Driven Sematic Reconstruction of Dynamic Scene with Multi-Human-Object Interactions
• Authors: Sandika Biswas, Qianyi Wu, Biplab Banerjee, Hamid Rezatofighi,
• Abstract summary: 3D geometry modeling of dynamic scenes is crucial for applications like AR/VR, gaming, and embodied AI.<n>We propose a hybrid approach that combines the advantages of 1) 3D generative models for generating high-fidelity meshes of the scene elements, 2) Semantic-aware deformation, and 3) GS-based optimization of the individual elements.<n>Our method outperforms the state-of-the-art method in producing better surface reconstruction of such scenes.
• Score: 41.29588736908775
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-world human-built environments are highly dynamic, involving multiple humans and their complex interactions with surrounding objects. While 3D geometry modeling of such scenes is crucial for applications like AR/VR, gaming, and embodied AI, it remains underexplored due to challenges like diverse motion patterns and frequent occlusions. Beyond novel view rendering, 3D Gaussian Splatting (GS) has demonstrated remarkable progress in producing detailed, high-quality surface geometry with fast optimization of the underlying structure. However, very few GS-based methods address multihuman, multiobject scenarios, primarily due to the above-mentioned inherent challenges. In a monocular setup, these challenges are further amplified, as maintaining structural consistency under severe occlusion becomes difficult when the scene is optimized solely based on GS-based rendering loss. To tackle the challenges of such a multihuman, multiobject dynamic scene, we propose a hybrid approach that effectively combines the advantages of 1) 3D generative models for generating high-fidelity meshes of the scene elements, 2) Semantic-aware deformation, \ie rigid transformation of the rigid objects and LBS-based deformation of the humans, and mapping of the deformed high-fidelity meshes in the dynamic scene, and 3) GS-based optimization of the individual elements for further refining their alignments in the scene. Such a hybrid approach helps maintain the object structures even under severe occlusion and can produce multiview and temporally consistent geometry. We choose HOI-M3 for evaluation, as, to the best of our knowledge, this is the only dataset featuring multihuman, multiobject interactions in a dynamic scene. Our method outperforms the state-of-the-art method in producing better surface reconstruction of such scenes.

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