ByteLoom: Weaving Geometry-Consistent Human-Object Interactions through Progressive Curriculum Learning

• URL: http://arxiv.org/abs/2512.22854v1
• Date: Sun, 28 Dec 2025 09:38:36 GMT
• Title: ByteLoom: Weaving Geometry-Consistent Human-Object Interactions through Progressive Curriculum Learning
• Authors: Bangya Liu, Xinyu Gong, Zelin Zhao, Ziyang Song, Yulei Lu, Suhui Wu, Jun Zhang, Suman Banerjee, Hao Zhang,
• Abstract summary: We introduce ByteLoom, a framework that generates realistic HOI videos with geometrically consistent object illustration.<n>We first propose an RCM-cache mechanism that leverages Relative Coordinate Maps (RCM) as a universal representation to maintain object's geometry consistency.<n>We then design a training curriculum that enhances model capabilities in a progressive style and relaxes the demand of hand mesh.
• Score: 19.292101162897975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human-object interaction (HOI) video generation has garnered increasing attention due to its promising applications in digital humans, e-commerce, advertising, and robotics imitation learning. However, existing methods face two critical limitations: (1) a lack of effective mechanisms to inject multi-view information of the object into the model, leading to poor cross-view consistency, and (2) heavy reliance on fine-grained hand mesh annotations for modeling interaction occlusions. To address these challenges, we introduce ByteLoom, a Diffusion Transformer (DiT)-based framework that generates realistic HOI videos with geometrically consistent object illustration, using simplified human conditioning and 3D object inputs. We first propose an RCM-cache mechanism that leverages Relative Coordinate Maps (RCM) as a universal representation to maintain object's geometry consistency and precisely control 6-DoF object transformations in the meantime. To compensate HOI dataset scarcity and leverage existing datasets, we further design a training curriculum that enhances model capabilities in a progressive style and relaxes the demand of hand mesh. Extensive experiments demonstrate that our method faithfully preserves human identity and the object's multi-view geometry, while maintaining smooth motion and object manipulation.

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