SMART introduces a scalable system for zero‑shot sim‑to‑real articulated object manipulation by leveraging large‑scale synthetic pretraining. The core component, SMART‑Sim, is an articulation‑aware simulation platform that facilitates efficient task generation and demonstration collection. Using SMART‑Sim, the authors synthesize SMART‑Data, comprising over 1 million demonstrations across 44 task types, 5 robot setups, and 2,507 articulated objects, and demonstrate that a vision‑language‑action model pretrained on this data achieves competitive simulation performance and zero‑shot transfer to real‑world tasks.
By Jicong Ao, Shuhan Jiang, Yuling Zhong, Yanwen Liu, Yuhan Gao, Jiangyuan Zhao, Yang Zhang, Shiqiang Zhu, Chenjia Bai, Xuelong Li
We study 4D generation to synthesize temporally coherent sequences of 3D geometry for animation and content creation. In contrast to existing SDS-based optimization methods and video-driven animation approaches, we adopt a skeleton-driven animation framework aligned with standard industrial pipelines, which enables explicit control and editing.
The paper introduces a two-stage framework to incorporate the animation principle of exaggeration into motion generative models. During training, it fine‑tunes pre‑trained text‑to‑motion models on a curated exaggeration dataset. At inference, it uses a mathematical formulation based on dynamic movement primitives to guide diffusion and flow‑matching models toward more expressive, exaggerated motions while maintaining physical plausibility and the original motion intent.
By Amirhossein Zamani, Arianna Rampini, Bruno Roy
InternW0-Δ is a unified World Action Model that integrates pretrained visual dynamics, scene semantics, 4D geometry, and motion priors within a Mixture-of-Transformers framework to generate robot actions. It leverages a frozen VLM for semantic guidance, a 4D foundation model for geometric priors, and introduces Causal Imprint to learn future-relevant scene changes without future-video rollout. The model is pretrained on a newly curated 20K‑hour heterogeneous corpus of robot and human demonstrations, achieving superior performance on simulation benchmarks and real‑robot platforms.
By Xingyu Miao, Zizun Li, Baole Fang, Kaiwen Song, Tenghui Wang, Hanxue Zhang, Yating Wang, Xudong Li, Yuping He, Xueyuan Wei, Chao Gao, Xijie Yang, Yingxiang Xu, Kerui Ren, Wenqi Guo, Jianjun Zhou, Xinzhe Wang, Weiguang Zhao, Ni Yang, Zetao Cai, Yufei Xue, Hengjie Li, Zeyu He, Yuanzhen Zhou, Rong Fu, Jianyang Zhang, Siwei Cui, Fuxian Huang, Yunsong Zhou, Xing Gao, Yifei Yao, Qiaojun Yu, Kailin Li, Ming Zhou, Mu Huang, Xinyue Li, Wenze Cui, Bingqi Jiang, Xueyue Zhu, Junting Dong, Haoyu Guo, Tao Lu, Mulin Yu, Bowen Zhou, Bin Zhao, Tianfan Xue, Weinan Zhang, Chunhua Shen
arXiv:2412.00112v5 Announce Type: replace
Abstract: Generating natural and expressive human motions from textual descriptions is challenging due to the complexity of coordinating full-body dynamics a...
By Seong-Eun Hong, Soobin Lim, Juyeong Hwang, Minwook Chang, Hyeongyeop Kang
arXiv:2606. 28385v1 Announce Type: cross Abstract: Recent advances in robot world models enable synthetic video generation for embodied prediction and planning.
By Minh-Loi Nguyen, Nghiem Tuong Diep, Hung Khang Nguyen, Minh Le, Doanh Le Thien, Hoang H. Tran, Dung D. Le, Vu N. Duong, Daniel Sonntag, An Thai Le, Duy Minh Ho Nguyen, Vien Anh Ngo, Tran Van Nhiem
arXiv:2607. 08741v1 Announce Type: cross Abstract: Generating realistic 3D human motions in real-time within interactive applications is key for animation, simulation, and humanoid robotics.
By Kaifeng Zhao, Mathis Petrovich, Haotian Zhang, Tingwu Wang, Siyu Tang, Davis Rempe
arXiv:2610.13549v1 Announce Type: new
Abstract: Text-guided long-term human motion generation is essential for applications such as continuously operating digital avatars. Despite recent progress on...
By Shun Kato, Dixin Yang, Kotaro Amaya, Yuiga Wada, Kazumi Fukuda, Takashi Shibuya, Yuki Mitsufuji, Mariko Isogawa
arXiv:2603.08590v4 Announce Type: replace
Abstract: Text-to-motion generation has advanced with larger corpora and stronger generators, yet many models still rely on holistic frame- or clip-level lat...
By Zeyu Ling, Qing Shuai, Teng Zhang, Shiyang Li, Bo Han, Changqing Zou
arXiv:2604.26917v2 Announce Type: replace
Abstract: Recent advances in 4D content generation have attracted increasing attention, yet creating high-quality animated 3D models remains challenging due...
By Zijie Wu, Chaohui Yu, Fan Wang, Xiang Bai
arXiv:2607. 01766v1 Announce Type: new Abstract: LLM agents are increasingly used to translate natural language into 3D scenes in a procedural way, but existing systems focus on static output.
By Chunjiang Liu, Xiaoyuan Wang, Haoyu Chen, Yizhou Zhao, Ming-Hsuan Yang, L\'aszl\'o A. Jeni
arXiv:2606.18363v3 Announce Type: replace-cross
Abstract: Language models trained on large-scale vision-language data have demonstrated strong potential for embodied agents. Harnessing models through...
By Haowen Liu, Xirui Li, Shaoxiong Yao, Peng Shi, Tianyi Zhou, Jia-Bin Huang, Furong Huang, Jiayuan Mao