ByteLoom: Weaving Geometry-Consistent Human-Object Interactions through Progressive Curriculum Learning
This addresses challenges in human-object interaction video generation for applications like digital humans and robotics, representing an incremental improvement over existing methods.
The paper tackled the problem of generating realistic human-object interaction videos with poor cross-view consistency and reliance on fine-grained annotations by introducing ByteLoom, a Diffusion Transformer-based framework that uses an RCM-cache mechanism and progressive curriculum learning, achieving faithful preservation of human identity and object geometry.
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.