DreamActor-H1: High-Fidelity Human-Product Demonstration Video
  Generation via Motion-designed Diffusion Transformers
Authors: name: Lizhen Wang, name: Zhurong Xia, name: Tianshu Hu, name: Pengrui Wang, name: Pengfei Wang, name: Zerong Zheng, name: Ming Zhou

In e-commerce and digital marketing, generating high-fidelity human-product
demonstration videos is important for effective product presentation. However,
most existing frameworks either fail to preserve the identities of both humans
and products or lack an understanding of human-product spatial relationships,
leading to unrealistic representations and unnatural interactions. To address
these challenges, we propose a Diffusion Transformer (DiT)-based framework. Our
method simultaneously preserves human identities and product-specific details,
such as logos and textures, by injecting paired human-product reference
information and utilizing an additional masked cross-attention mechanism. We
employ a 3D body mesh template and product bounding boxes to provide precise
motion guidance, enabling intuitive alignment of hand gestures with product
placements. Additionally, structured text encoding is used to incorporate
category-level semantics, enhancing 3D consistency during small rotational
changes across frames. Trained on a hybrid dataset with extensive data
augmentation strategies, our approach outperforms state-of-the-art techniques
in maintaining the identity integrity of both humans and products and
generating realistic demonstration motions. Project page:
https://submit2025-dream.github.io/DreamActor-H1/.