Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.13120 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916654484553728 |
|---|---|
| author | Fan, Siyuan Huang, Wenke Cai, Xiantao Du, Bo |
| author_facet | Fan, Siyuan Huang, Wenke Cai, Xiantao Du, Bo |
| contents | 3D human interaction generation has emerged as a key research area, focusing on producing dynamic and contextually relevant interactions between humans and various interactive entities. Recent rapid advancements in 3D model representation methods, motion capture technologies, and generative models have laid a solid foundation for the growing interest in this domain. Existing research in this field can be broadly categorized into three areas: human-scene interaction, human-object interaction, and human-human interaction. Despite the rapid advancements in this area, challenges remain due to the need for naturalness in human motion generation and the accurate interaction between humans and interactive entities. In this survey, we present a comprehensive literature review of human interaction generation, which, to the best of our knowledge, is the first of its kind. We begin by introducing the foundational technologies, including model representations, motion capture methods, and generative models. Subsequently, we introduce the approaches proposed for the three sub-tasks, along with their corresponding datasets and evaluation metrics. Finally, we discuss potential future research directions in this area and conclude the survey. Through this survey, we aim to offer a comprehensive overview of the current advancements in the field, highlight key challenges, and inspire future research works. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13120 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 3D Human Interaction Generation: A Survey Fan, Siyuan Huang, Wenke Cai, Xiantao Du, Bo Computer Vision and Pattern Recognition 3D human interaction generation has emerged as a key research area, focusing on producing dynamic and contextually relevant interactions between humans and various interactive entities. Recent rapid advancements in 3D model representation methods, motion capture technologies, and generative models have laid a solid foundation for the growing interest in this domain. Existing research in this field can be broadly categorized into three areas: human-scene interaction, human-object interaction, and human-human interaction. Despite the rapid advancements in this area, challenges remain due to the need for naturalness in human motion generation and the accurate interaction between humans and interactive entities. In this survey, we present a comprehensive literature review of human interaction generation, which, to the best of our knowledge, is the first of its kind. We begin by introducing the foundational technologies, including model representations, motion capture methods, and generative models. Subsequently, we introduce the approaches proposed for the three sub-tasks, along with their corresponding datasets and evaluation metrics. Finally, we discuss potential future research directions in this area and conclude the survey. Through this survey, we aim to offer a comprehensive overview of the current advancements in the field, highlight key challenges, and inspire future research works. |
| title | 3D Human Interaction Generation: A Survey |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.13120 |