DreamActor-H1: High-Fidelity Human-Product Demonstration Video
  Generation via Motion-designed Diffusion Transformers
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/.
684c06a83b733ba3336871fc
Lizhen Wang
684c06a83b733ba3336871fd
Zhurong Xia
684c06a83b733ba3336871fe
Tianshu Hu
684c06a83b733ba3336871ff
Pengrui Wang
684c06a83b733ba333687200
Pengfei Wang
684c06a83b733ba333687201
Zerong Zheng
684c06a83b733ba333687202
Ming Zhou
DreamActor-H1: High-Fidelity Human-Product Demonstration Video
  Generation via Motion-designed Diffusion Transformers
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/.
A Diffusion Transformer-based framework generates high-fidelity human-product demonstration videos by preserving identities and spatial relationships, using masked cross-attention and structured text encoding.
Diffusion Transformer (DiT) masked cross-attention 3D body mesh template product bounding boxes structured text encoding hybrid dataset data augmentation strategies identity integrity realistic demonstration motions