Baidu AI Cloud Media and Entertainment China Translated from Chinese

China National Diving Team (中国国家跳水队)

The China National Diving Team generated large volumes of short training videos but still relied on manual browsing and verbal coaching, making it hard to locate key frames quickly, quantify movement quality, or turn elite performance into reusable training knowledge.

Reported impact

Business outcome

The diving team gained faster video review, more objective movement analysis, and a way to turn champion-level performance data into repeatable coaching knowledge and richer fan-facing content possibilities.

  • Customer Experience
  • Productivity Improvement

Solution

Baidu AI Cloud built a '3D + AI' assisted training system for the China National Diving Team that combines cloud, edge, and on-site capture hardware with computer-vision analytics. High-speed cameras collect synchronized footage when an athlete steps onto the board and stop capture automatically when the athlete exits the water. AI services then classify and organize the clips and return them to coaches on tablets for immediate review. The system goes beyond conventional replay by applying 3D vision and deep neural networks to reconstruct the diver's body posture in three dimensions, estimate joint angles, and quantify key movements so coaches can diagnose the root causes of bad entries or weak takeoff. It also reconstructs the venue in 3D, extracts key actions, supports 360-degree free-angle replay, and automatically identifies dive types, difficulty coefficients, and scoring cues. That turns raw training video into a searchable, measurable coaching asset instead of an archive of unstructured footage.

Data flow

Training video is captured automatically by high-speed cameras, then passed into AI classification and clip-organization workflows that deliver results to coach tablets. Selected clips are reconstructed into 3D motion models, scored, and stored as reusable knowledge assets so coaches can compare posture, diagnose errors, and refine athlete guidance.

Solution architecture

4 components · 4 layers
  1. Ingest
    • High-speed Video Capture System Collects synchronized diving footage automatically when athletes begin and complete training actions.
  2. Compute
    • 3D Vision Pose Estimation Reconstructs athletes' body posture in three dimensions and measures movement quality through joint-angle analysis.
  3. Storage
    • Enterprise Knowledge Middle Platform Solution Helps turn elite training data into reusable knowledge models for coaching and future athlete development.
  4. Serving
    • AI Middle Platform Solution Organizes captured clips and supports analysis workflows that return training feedback to coaches rapidly.

Architecture clues

  • 3D venue reconstruction and 360-degree replay are core parts of the solution, not just downstream visualizations.
  • Automatic action identification links each clip to dive codes and difficulty coefficients for structured analysis.
  • The system uses a cloud-edge-device architecture rather than uploading all raw training footage into a purely centralized pipeline.

Evidence from the source

3D vision and deep neural networks estimate athletes' three-dimensional posture and joint angles to evaluate technique quantitatively (original: 利用3D视觉技术和深度神经网络估算运动员三维的姿态,获得每个关节的三维角度).
Baidu became the exclusive AI partner of the China National Diving Team and built the country's first '3D + AI' diving training system (original: 百度智能云正式成为中国国家跳水队独家AI合作伙伴,打造的国内首个"3D+AI"跳水训练系统).
The system can automatically identify dive actions, codes, difficulty coefficients, and scoring quality (original: 能够自动识别出是哪一个动作、对应的动作代码、难度系数,并对完成质量进行评分).
The system uses a cloud-edge-device integrated hardware and software setup with high-speed video capture, intelligent video organization, and encrypted data handling (original: 搭建了"云-边-端"一体的软硬件系统,核心算法包括高速视频智能采集、视频数据智能整理以及数据的安全加密).
Transparency Evidence & methodology 70/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value70/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Baidu AI Cloud

    China National Diving Team (中国国家跳水队) customer story

    First-party
    https://cloud.baidu.com/customer/case/tiaoshui.html
    Source type
    Vendor-published customer story
    Published
    May 15, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page May 15, 2025 · Baidu AI Cloud · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.