School of Cyber Science and Engineering, Southeast University（东南大学网络空间安全学院）；School of Computer Science and Engineering, Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Ministry of Education, Southeast University（东南大学计算机科学与工程学院、教育部新一代人工智能技术及其跨学科应用重点实验室）；Purple Mountain Laboratories（紫金山实验室）；Engineering Research Center of Blockchain Application, Supervision And Management (Southeast University), Ministry of Education（教育部区块链应用监管工程研究中心（东南大学））；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统全国重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Text-to-motion generation has attracted increasing attention in the research community recently, with potential applications in animation, virtual reality, robotics, and human-computer interaction. Diffusion and autoregressive models are two popular and parallel research directions for text-to-motion generation. However, diffusion models often suffer from error amplification during noise prediction, while autoregressive models exhibit mode collapse due to motion discretization. To address these limitations, we propose a flexible, high-fidelity, and semantically faithful text-to-motion framework, named Coordinate-based Dual-constrained Autoregressive Motion Generation (CDAMD). With motion coordinates as input, CDAMD follows the autoregressive paradigm and leverages diffusion-inspired multi-layer perceptrons to enhance the fidelity of predicted motions. Furthermore, a Dual-Constrained Causal Mask is introduced to guide autoregressive generation, where motion tokens act as priors and are concatenated with textual encodings. Since there is limited work on coordinate-based motion synthesis, we establish new benchmarks for both text-to-motion generation and motion editing. Experimental results demonstrate that our approach achieves state-of-the-art performance in terms of both fidelity and semantic consistency on these benchmarks.
Planning and Rendering in Concert: DeepFusion of Autoregressive Layouts and Diffusion for Visual Text Generation
University of Science and Technology of China（中国科学技术大学）；Central South University（中南大学）；Louisiana State University（路易斯安那州立大学）；Arizona State University（亚利桑那州立大学）；Sun Yat-sen University（中山大学）
MotionStreamer: Streaming Motion Generation via Diffusion-based Autoregressive Model in Causal Latent Space
Zhejiang University（浙江大学）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；The University of Hong Kong（香港大学）；Shanghai Jiao Tong University（上海交通大学）；DeepGlint；Shanghai AI Laboratory（上海人工智能实验室）
HOFAR: High-Order Augmentation of Flow Autoregressive Transformers
The University of Hong Kong（香港大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Tsinghua University（清华大学）；The Simons Institute for the Theory of Computing at UC Berkeley（加州大学伯克利分校西蒙斯理论计算研究所）；Anhui University（安徽大学）
Tsinghua University（清华大学）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（Mohamed bin Zayed人工智能大学）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Carnegie Mellon University（卡内基梅隆大学）
FlashMesh: Faster and Better Autoregressive Mesh Synthesis via Structured Speculation
South China University of Technology（南方科技大学）；Tsinghua University（清华大学）；Zhejiang University（浙江大学）；Tencent VISVISE（腾讯VISVISE）；Peking University（北京大学）；Singapore Management University（新加坡国立大学）
AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving
Shanghai Jiao Tong University（上海交通大学）；Cowa Robot（科沃机器人）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；The Chinese University of Hong Kong Shenzhen（香港中文大学（深圳））
Beyond Patches: Global-aware Autoregressive Model for Multimodal Few-Shot Font Generation
Wangxuan Institute of Computer Technology, Peking University（北京大学王学轩计算机技术研究所）；School of Electronics Engineering and Computer Science, Peking University（北京大学电子工程与计算机科学学院）
