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

• URL: http://arxiv.org/abs/2411.17383v2
• Date: Mon, 23 Jun 2025 06:27:21 GMT
• Title: AnchorCrafter: Animate Cyber-Anchors Selling Your Products via Human-Object Interacting Video Generation
• Authors: Ziyi Xu, Ziyao Huang, Juan Cao, Yong Zhang, Xiaodong Cun, Qing Shuai, Yuchen Wang, Linchao Bao, Jintao Li, Fan Tang,
• Abstract summary: The generation of anchor-style product promotion videos presents promising opportunities in e-commerce, advertising, and consumer engagement.<n>We introduce AnchorCrafter, a novel diffusion-based system designed to generate 2D videos featuring a target human and a customized object.<n>We propose two key innovations: the HOI-appearance perception, which enhances object appearance recognition from arbitrary multi-view perspectives, and the HOI-motion injection, which enables complex human-object interactions.
• Score: 40.81246588724407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 doubles the object localization accuracy 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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