Text-to-motion generation is a crucial task in computer vision, which generates the target 3D motion by the given text. The existing annotated datasets are limited in scale, resulting in most existing methods overfitting to the small datasets and unable to generalize to the motions of the open domain. Some methods attempt to solve the open-vocabulary motion generation problem by aligning to the CLIP space or using the Pretrain-then-Finetuning paradigm. However, the current annotated dataset's limited scale only allows them to achieve mapping from sub-text-space to sub-motion-space, instead of mapping between full-text-space and full-motion-space (full mapping), which is the key to attaining open-vocabulary motion generation. To this end, this paper proposes to leverage the atomic motion (simple body part motions over a short time period) as an intermediate representation, and leverage two orderly coupled steps, i.e., Textual Decomposition and Sub-motion-space Scattering, to address the full mapping problem. For Textual Decomposition, we design a fine-grained description conversion algorithm, and combine it with the generalization ability of a large language model to convert any given motion text into atomic texts. Sub-motion-space Scattering learns the compositional process from atomic motions to the target motions, to make the learned sub-motion-space scattered to form the full-motion-space. For a given motion of the open domain, it transforms the extrapolation into interpolation and thereby significantly improves generalization. Our network, $DSO$-Net, combines textual $d$ecomposition and sub-motion-space $s$cattering to solve the $o$pen-vocabulary motion generation. Extensive experiments demonstrate that our DSO-Net achieves significant improvements over the state-of-the-art methods on open-vocabulary motion generation. Code is available at https://vankouf.github.io/DSONet/.
AnySkill: Learning Open-Vocabulary Physical Skill for Interactive Agents
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；BIGAI（北京智源人工智能研究院）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）
Re$^2$MoGen: Open-Vocabulary Motion Generation via LLM Reasoning and Physics-Aware Refinement
East China University of Science and Technology（东华大学）；Hong Kong University of Science and Technology（香港科技大学）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））
MoSAT: Human Motion Generation from Spatial Audio and Textual Description
The University of Hong Kong（香港大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Pennsylvania（宾夕法尼亚大学）；Brown University（布朗大学）；Shanghai AI Lab（上海人工智能实验室）；Macau University of Science and Technology（澳门科技大学）；Hong Kong University of Science and Technology（香港科技大学）；Texas A&M University（德克萨斯农工大学）
Fondazione Bruno Kessler (FBK)（布鲁诺·凯斯勒基金会）；University of Trento（特伦托大学）；Queen Mary University of London（伦敦大学玛丽女王学院）；Idiap Research Institute（伊迪亚普研究所）；École polytechnique fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院）
SynCLIP: Synonym-Coherent Language-Image Pretraining for Robust Open-Vocabulary Dense Perception
Beihang University（北京航空航天大学）；State Key Laboratory of Space Information System and Integrated Application（空间信息系统与集成应用国家重点实验室）；Nanjing University（南京大学）；Harbin Engineering University（哈尔滨工程大学）；Beijing Zhongguancun Academy（北京中关村科学城）
Catch Me If You Can Describe Me: Open-Vocabulary Camouflaged Instance Segmentation with Diffusion
The Hong Kong University of Science and Technology（香港科学与技术大学）；CFAR & IHPC, A*STAR（CFAR与IHPC，A*STAR）；Deakin University（德克萨斯大学）；Nankai University（南开大学）；National University of Singapore（新加坡国立大学）；Trinity College Dublin（都柏林大学）
ViewSRD: 3D Visual Grounding via Structured Multi-View Decomposition
South China University of Technology（华南理工大学）；Guangdong Engineering Center for Large Model and GenAI Technology（广东省大模型与生成式人工智能技术工程中心）；State Key Laboratory of Subtropical Building and Urban Science（亚热带建筑科学国家重点实验室）；Ministry of Education Key Laboratory of Big Data and Intelligent Robot（教育部大数据与智能机器人重点实验室）；Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information（广东省计算智能与网络信息重点实验室）；Singapore Management University（新加坡国立大学）
RT-OVAD: Real-Time Open-Vocabulary Aerial Object Detection via Image-Text Collaboration
Nanjing University of Science and Technology（南京理工大学）；Northwestern Polytechnical University（西北工业大学）；Zhejiang Lab（浙江实验室）；Intellifusion Inc.（Intellifusion公司）
ImagineAgent: What if Agents Could Imagine? Reinforcing Open-Vocabulary HOI Comprehension through Generation
Dream-X Team（Dream-X团队）；Stanford University（斯坦福大学）；National University of Singapore（新加坡国立大学）；Independent Researcher（独立研究者）；Case Western Reserve University（凯斯西储大学）；UC Santa Cruz（加州大学圣克鲁兹分校）
WAVER: Writing-style Agnostic Text-Video Retrieval via Distilling Vision-Language Models Through Open-Vocabulary Knowledge
FPT Software AI Center（FPT软件人工智能中心）；International University, VNU-HCM（越南胡志明市国家大学国际大学）；RMIT University, Vietnam（澳大利亚皇家墨尔本理工大学越南分校）；University of Liverpool, UK（英国利物浦大学）；University of Arkansas, USA（美国阿肯色大学）
Revisiting Natural Language Prompts from Large Language Models for Open-Vocabulary Video Recognition
School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；School of Software Engineering, Beijing Jiaotong University（北京交通大学软件学院）
