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February 12, 2026IEEE Transactions on Visualization and Computer Graphics0 citations

AnchorCrafter: Animate Cyber-Anchors Selling Your Products via Human-Object Interacting Video Generation

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ZXZiyi XuZHZiyao HuangJCJuan Cao

Key Points

  • The study aims to enhance the generation of anchor-style product promotion videos through improved human-object interactions.
  • Introduced a novel diffusion-based system named AnchorCrafter.
  • Developed HOI-appearance perception for enhanced object recognition.
  • Implemented HOI-motion injection for better human-object interaction dynamics.
  • Conducted extensive experiments to evaluate video quality and human motion consistency.
  • Achieved a 7.5% improvement in object appearance preservation.
  • Generated the highest video quality compared to existing approaches.
  • Outperformed others in maintaining human motion consistency and video generation.

Abstract

The generation of anchor-style product promotion videos presents promising opportunities in e-commerce, advertising, and consumer engagement. Despite advancements in pose-guided human video generation, creating product promotion videos remains challenging. In addressing this challenge, we identify the integration of human-object interactions (HOI) into pose-guided human video generation as a core issue. To this end, we introduce AnchorCrafter, a novel diffusion-based system designed to generate 2D videos featuring a target human and a customized object, achieving high visual fidelity and controllable interactions. Specifically, we propose two key innovations: the HOI-appearance perception, which enhances object appearance recognition from arbitrary multi-view perspectives and disentangles object and human appearance, and the HOI-motion injection, which enables complex human-object interactions by overcoming challenges in object trajectory conditioning and inter-occlusion management. Extensive experiments show that our system improves object appearance preservation by 7.5%, and achieves the best video quality compared to existing state-of-the-art approaches. It also outperforms existing approaches in maintaining human motion consistency and high-quality video generation. Project page including data, code, and Huggingface demo: https://github.com/cangcz/AnchorCrafter.

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Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/698d6d445be6419ac0d52355https://doi.org/10.1109/tvcg.2026.3662720
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