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Open nowPosted 54 days ago

高级算法工程师（3D 人体与手部姿态）

zcnma1sv5kma/10121017 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now高级算法工程师（3D 人体与手部姿态）zcnma1sv5kma/101210 · Beijing, Chinese Mainland

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.0%1 day
  2. 4.3%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 54 days ago

The posting

1. 人体与手部姿态算法研发：参与 3D 人体姿态、手部姿态、MANO / SMPL-X 等相关模型的方案设计、训练、调优和效果分析。 2. 模型问题定位与优化：针对遮挡、快速运动、复杂姿态、视角变化、时序抖动、出画和多人等问题，分析误差来源并提出可验证的算法改进方案。 3. 多视角三维视觉建模：参与多视角图像、人体关键点、骨架或其他传感器信息的建模与融合，推进三维几何、时序信息和学习模型的结合。 4. 数据与真值协同：根据模型问题参与数据筛选、采集方案设计、真值质量分析和评测集建设，理解硬件观测、算法产线输出与 Mesh 真值之间的边界。 5. 实验与效果验证：建立清晰的实验设计和指标分析方法，完成离线评测、对比实验、消融分析和问题复盘，并沉淀可复用的算法结论。 6. 工程落地协作：与数据、平台和业务同学协作，推动算法从原型验证进入稳定训练、评测或应用流程；根据需要参与模型推理效率和部署效果优化。 3. 任职要求 1. 本科及以上学历，计算机、软件工程、自动化、电子信息、数学或相关专业。 2. 3–5 年算法研发或相关研究经验，具备独立承担算法模块研发和持续优化的能力。 3. 至少具备以下一类项目经验或研究经历：人体姿态估计、手部姿态估计、三维人体建模、三维视觉或相关 3D 视觉任务。 4. 具备扎实的机器学习和计算机视觉基础，熟悉深度学习模型训练、数据处理、实验设计和评测分析。 5. 熟练使用 Python 和 PyTorch 等开发工具，能够阅读、复现和改进相关论文或开源方法。 6. 能够将研究问题转化为可执行的算法方案，并通过实验和数据分析推动方案落地。 4. 优先考虑 - 有成熟的 3D 人体或手部算法项目经验，并能清楚说明个人负责的技术问题、方案取舍和最终效果。 - 熟悉 MANO、SMPL、SMPL-X 或其他人体 / 手部参数化模型、Mesh 拟合和姿态回归方法。 - 有多视角几何、相机标定、三维重建、姿态跟踪、动作捕捉或视觉与 IMU 等传感器融合经验。 - 有遮挡处理、时序建模、视频理解、姿态后处理或运动学约束相关经验。 - 有大规模数据集构建、真值生产、自动质检、评测体系或困难样本挖掘经验。 - 有模型推理加速、端侧 / 服务端部署、训练基础设施或线上效果优化经验。 - 有高质量论文、专利、开源项目或竞赛成果，能够体现持续研究和技术表达能力。

1. 人体与手部姿态算法研发：参与 3D 人体姿态、手部姿态、MANO / SMPL-X 等相关模型的方案设计、训练、调优和效果分析。 2. 模型问题定位与优化：针对遮挡、快速运动、复杂姿态、视角变化、时序抖动、出画和多人等问题，分析误差来源并提出可验证的算法改进方案。 3. 多视角三维视觉建模：参与多视角图像、人体关键点、骨架或其他传感器信息的建模与融合，推进三维几何、时序信息和学习模型的结合。 4. 数据与真值协同：根据模型问题参与数据筛选、采集方案设计、真值质量分析和评测集建设，理解硬件观测、算法产线输出与 Mesh 真值之间的边界。 5. 实验与效果验证：建立清晰的实验设计和指标分析方法，完成离线评测、对比实验、消融分析和问题复盘，并沉淀可复用的算法结论。 6. 工程落地协作：与数据、平台和业务同学协作，推动算法从原型验证进入稳定训练、评测或应用流程；根据需要参与模型推理效率和部署效果优化。 3. 任职要求 1. 本科及以上学历，计算机、软件工程、自动化、电子信息、数学或相关专业。 2. 3–5 年算法研发或相关研究经验，具备独立承担算法模块研发和持续优化的能力。 3. 至少具备以下一类项目经验或研究经历：人体姿态估计、手部姿态估计、三维人体建模、三维视觉或相关 3D 视觉任务。 4. 具备扎实的机器学习和计算机视觉基础，熟悉深度学习模型训练、数据处理、实验设计和评测分析。 5. 熟练使用 Python 和 PyTorch 等开发工具，能够阅读、复现和改进相关论文或开源方法。 6. 能够将研究问题转化为可执行的算法方案，并通过实验和数据分析推动方案落地。 4. 优先考虑 - 有成熟的 3D 人体或手部算法项目经验，并能清楚说明个人负责的技术问题、方案取舍和最终效果。 - 熟悉 MANO、SMPL、SMPL-X 或其他人体 / 手部参数化模型、Mesh 拟合和姿态回归方法。 - 有多视角几何、相机标定、三维重建、姿态跟踪、动作捕捉或视觉与 IMU 等传感器融合经验。 - 有遮挡处理、时序建模、视频理解、姿态后处理或运动学约束相关经验。 - 有大规模数据集构建、真值生产、自动质检、评测体系或困难样本挖掘经验。 - 有模型推理加速、端侧 / 服务端部署、训练基础设施或线上效果优化经验。 - 有高质量论文、专利、开源项目或竞赛成果，能够体现持续研究和技术表达能力。

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