NextPlay Motion · Sports video R&D

Every movement leaves a signal.

One motion intelligence layer for athletes across sports, explored through pose, geometry and temporal signals. Universal concept first. Measured proof follows.

Enter the film room Read the evidence
Illustrative universal athlete motion structure with cross-sport signal echoesJOINT CHAINILLUSTRATIVESPORT MODES03 / UNIVERSALSIGNAL STATE: ILLUSTRATIVEUNIVERSAL ATHLETECROSS-SPORT FIELD
Illustrative · universal signal study
PLATFORM MOTION CONCEPT

Film room

Scrub the clip. Switch the layer.

tennis-preview.mp4 · genuine phone footageR&D preview · 00:07.00
Measured MediaPipe CPU output for frame 194 of the supplied tennis video
R&D previewMeasured MediaPipe CPU output · frame 194 / 209
Measured

Frame 194 output from the supplied MediaPipe CPU run.

Open full measured output
FRAME 194 / 209

Transformation strip

From footage to structure.

A focused frame sequence follows the transformation. The athlete-record stage remains a planned integration, not a completed product claim.

01Real input

Source capture

Supplied court footage

02Stable

Sample strand

209 sampled frames

03Stable

Subject lock

Selected person lock

04Stable

Angle trace

Frame-level geometry

05Stable

Range field

Min, max, mean, range

06Not yet unlocked

Athlete archive

Future integration

01 / 06 · Swipe to follow the transformation →

Measured pipeline artifact showing real input, keypoints, person lock, joint angles, temporal ranges and planned athlete record
Measured MediaPipe output from frame 194 of the supplied tennis clip, with selected-person joint angles and temporal ranges
Measured · MediaPipe CPU · frame 194 / 209

Movement anatomy

Inspect the movement, not the athlete.

This supplied run produced 13 keypoints, eight joint angles, and per-angle minimum, maximum, mean and range across 209 frames. Richer coaching interpretations are not claimed.

01
Right elbow · frame 194: 144.94°

At measured frame 194, the selected person's right-elbow angle is 144.94 degrees.

Experimental signal athlete

Interaction prototype. Body shell in development.

This chamber tests cross-sport poses, landmark inspection and temporal overlays. The procedural body is a fallback prototype, not the final athlete design.

Illustrative interactionGLB body shell planned
Universal signal athletePlatform motion concept
Illustrative signal avatarSport mode / Tennis
Sport modeTennisPhaseFollow-through

Engineering evidence

One video. 527.8 seconds.

Signal stateVerified CPU baseline
527.8seconds
ModelKeypoint R-CNN
ComputeCPU · FP32
Sample288 sampled frames
Decode + sampling · 0.78%Model forward · 98.99%Selection + analysis + serialize · 0.23%
Date15 Jul 2026
SystemIntel Core Ultra 7 258V · 31.51 GB RAM
ProvenanceCommit ccec398e5c38
Same sampled framesMediaPipe CPU · 26.925 s
Open research log Download summary

Both engines processed the same 288-frame identity on CPU at FP32, batch 1. These are steady-state processing measurements, not model-quality or coaching-accuracy claims. NVIDIA GPU profiling remains planned and no GPU result is presented.

Acceleration roadmap

The acceleration target is visible.

Stable now

Measured CPU baseline

  • Individual video
  • FP32 inference
  • Long processing time
  • Limited concurrency

Optimise · planned evaluation

NVIDIA evaluation

CPU
GPUNot yet measured
  • CUDA profiling
  • Mixed precision
  • Batch evaluation
  • TensorRT compatibility
  • Triton exploration

Scale · future direction

Platform video intelligence

  • Concurrent jobs
  • Reduced waiting time
  • Planned athlete-record integration
  • Future edge evaluation
NVIDIA technologies are planned development directions. No NVIDIA GPU or TensorRT performance is currently claimed.

Planned athlete context

Structure becomes useful when it can persist.

Existing NextPlay records are shown with solid connections. Future Motion signals use dotted lime connections to make the planned integration explicit.

Current record Planned Motion signal
Sample athlete recordConcept workflow
MatchesGameprintsVideosScoresPose signalsMotion sessions

Workflow concepts

Designed around real sporting workflows.

Genuine phone footage of a tennis player training on an outdoor court
01 · Genuine footage

Athletes

Review a real training clip and preserve evidence of movement over time.

02 · Measured frames in a coach-review concept

Coaches + academies

Inspect measured frame sets inside a review workflow that remains clearly conceptual.

03 · Programme workflow — concept preview

Sports programmes

Organise athlete groups, submitted sessions, review status and progress records.

Current status

What works now. What comes next.

Active R&D
  • Deterministic frame selection
  • MediaPipe baseline
  • Keypoint R-CNN benchmark
  • Output-consistency checks
  • Reproducible CPU workflow
  • NVIDIA GPU profiling
  • Precision and batch-size testing
  • Athlete-record integration
  • Useful movement summaries
  • Pilot evaluation
  • Sport-specific models
  • Longitudinal comparisons
  • Production inference serving
  • Field and edge evaluation

Research limits

What we cannot claim yet.

01

Occluded or partially visible athletes

02

Multiple athletes in the same analysis region

03

Low-light and unstable camera conditions

04

Validated sport-specific coaching interpretation

Abstract motion structure dissolving from a tennis actionJOINT CHAINILLUSTRATIVESPORT MODES03 / UNIVERSAL
Structure persists.

Pilot enquiry

Bring a real training question.

We are looking for careful pilot conversations with athletes, coaches, programmes, research partners and technology partners.

bhagyashrimeena@nextplaysports.in
Role

We typically respond within two working days. Your enquiry is sent to the NextPlay Motion team.

Samsung Solve for Tomorrow India 2025 National WinnerIncubated through FITT, IIT DelhiMember of NVIDIA Inception