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Open nowPosted 5 days ago

生成式动作算法工程实习生（T2M / A2G 方向）

agirobot/internrecruitment367 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now生成式动作算法工程实习生（T2M / A2G 方向）agirobot/internrecruitment · Shenzhen, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market. agirobot/internrecruitment postings stay open a median of 2 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 5 days ago

agirobot/internrecruitment median: 2 days open

The posting

岗位职责 算法研发与优化： 负责 Text-to-Motion (T2M) 和 Audio-to-Gesture/Motion (A2G) 算法的前沿探索与研发，提升生成动作的自然度、语义一致性、音步对齐度及物理合理性。 多模态模型构建： 基于 Diffusion Models、Transformer、VQ-VAE / Flow Matching 等主流生成式架构，构建高效的多模态动作生成与表达模型。 数据管线建设： 负责 3D 动作数据集（如 HumanML3D、BEAT、SHOW 等）及动捕数据的清洗、特征提取、时间轴对齐与重采样，搭建高性能数据处理 pipeline。 核心难题攻坚： 攻克长序列生成连贯性、脚滑/漂移修正（Foot Skating Correction）、精细化手部/面部与语音对齐、风格化控制等关键技术痛点。

任职要求 学历与专业： 计算机、人工智能、自动化、软件工程、图形学或机器人相关专业硕士及以上学历（或具备同等资深研发经验）。 生成式算法功底： 深入理解生成式 AI 核心原理，熟练掌握 Diffusion、Autoregressive、VQ-VAE、GAN 等模型及其在时序/动作数据上的应用。 3D 图形与动作基础： 熟练掌握 3D 动作表达格式（如 SMPL / SMPL-X、BVH、Joint Angles/Quaternion、URDF 等），熟悉动作重定向（Retargeting）与运动学基础。 工程研发能力： 精通 Python，熟练掌握 PyTorch 等深度学习框架，具备扎实的数据结构/算法基础与良好的工程实践习惯。 领域技术积累： 在 T2M 或 A2G/Audio-to-Motion 领域有实际项目经验，了解行业主流基线（如 MDM, T2M-GPT, DiffGesture, Geneea 等）。

加分项 学术成果： 在 CVPR、ICCV、ECCV、SIGGRAPH / SIGGRAPH Asia、NeurIPS、AAAI 等顶级学术会议/期刊发表过 3D 动作生成或多模态理解方向论文者优先。 业务场景经验： 具备具身智能 / 人形机器人动作规划、虚拟数字人实时驱动、XR 或游戏 AI 动作生成实战经验者优先。 数据与硬件经验： 熟悉 OptiTrack、Xsens 等主流动捕设备，或具备将生成动作映射至物理实体/仿真环境（如 Isaac Gym、MuJoCo）的经验。 模型部署： 具备模型量化、剪枝以及 TensorRT / ONNX Runtime 加速与 C++ 端侧部署经验者优先。

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