ByteLoom Introduces Diffusion‑Transformer Framework for Consistent Human‑Object Interaction Video Generation

Global: ByteLoom Introduces Diffusion‑Transformer Framework for Consistent Human‑Object Interaction Video Generation

Researchers have unveiled ByteLoom, a diffusion‑transformer‑based system designed to generate human‑object interaction (HOI) videos that maintain geometric consistency across multiple viewpoints. The framework aims to overcome two prevalent challenges in existing HOI video synthesis: insufficient incorporation of multi‑view object information and heavy dependence on detailed hand‑mesh annotations.

Background and Challenges

Current HOI generation approaches often produce videos with noticeable cross‑view inconsistencies, as they lack mechanisms to embed comprehensive 3‑D object representations. Additionally, many models require fine‑grained hand mesh data to simulate occlusions, limiting scalability to datasets where such annotations are unavailable.

ByteLoom Architecture

ByteLoom addresses these gaps by integrating a Diffusion Transformer (DiT) backbone with a novel Relative Coordinate Map (RCM) cache. The RCM serves as a universal descriptor of object geometry, enabling precise 6‑DoF transformations while preserving the object’s spatial integrity throughout the video sequence.

Relative Coordinate Maps

The RCM‑cache mechanism stores per‑frame coordinate information relative to the object’s reference frame. This representation allows the model to adjust object pose dynamically, ensuring that each rendered view aligns with the underlying 3‑D structure, thereby reducing visual artifacts associated with viewpoint changes.

Training Strategy

To mitigate the scarcity of HOI‑specific datasets, the authors propose a progressive curriculum that gradually introduces complexity during training. The strategy lessens reliance on hand mesh inputs by leveraging simplified human conditioning, which focuses on skeletal or pose data rather than full mesh detail.

Experimental Findings

Extensive evaluations reported that ByteLoom successfully preserves human identity and maintains consistent multi‑view object geometry while delivering smooth motion trajectories. Quantitative metrics indicated improvements over baseline methods in both visual fidelity and geometric alignment.

Implications

The ability to generate realistic HOI videos without exhaustive hand‑mesh annotations expands potential applications in digital avatar creation, e‑commerce product demonstrations, advertising, and robotic imitation learning. Future work may explore scaling the framework to larger, more diverse datasets and integrating real‑time inference capabilities.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.