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+# movement: Human Pose Estimation Format Support (Peter Nelson Subrata)
+
+## Personal details
+
+- **Full name:** Peter Nelson Subrata
+- **Email:** peterns1609@gmail.com
+- **GitHub username:** PewterZz
+- **Zulip username:** Peter Nelson Subrata
+- **Location & time-zone:** Jakarta, Indonesia — GMT+7
+- **Code contribution:** https://github.com/neuroinformatics-unit/movement/pull/914
+- **Proposal discussion link:** https://github.com/neuroinformatics-unit/gsoc/pull/97
+
+## Project proposal
+
+### Synopsis
+
+movement currently supports animal pose estimation formats (DeepLabCut, SLEAP,
+LightningPose, Anipose) but has no support for human-focused formats, MMPose,
+COCO keypoints, FreeMocap, motionBIDS, BVH, and C3D. Researchers studying
+human movement in rehabilitation, sports science, or clinical gait analysis
+cannot load their data into movement without writing custom preprocessing
+scripts. This project implements loaders for at least three of these formats,
+following the existing from_sleap_file/from_dlc_file pattern and producing
+the standard xarray.Dataset output that works with all of movement's
+kinematics and visualisation tools. The wanted result is that movement becomes
+a unified interface across pose estimation frameworks that extends
+to human data as well.
+
+### Implementation timeline
+
+**Minimal deliverables:**
+- MMPose JSON loader (COCO-17, Halpe-26, custom schemas, multi-individual)
+- COCO keypoint annotation loader (visibility-to-confidence mapping)
+- FreeMocap 3D loader (first 3D format in movement)
+- motionBIDS loader (BIDS directory traversal, TSV + JSON sidecar)
+- Unit and integration tests for each loader
+- Gallery examples for each format
+
+**Stretch goals:**
+- BVH and C3D motion capture loaders
+- from_file() format detection utility
+
+**Weekly timeline** (12 weeks, ~30 hours/week):
+
+| Weeks | Work |
+|-------|------|
+| Bonding | Read codebase, finalise MMPose loader from pre-proposal prototype, discuss any API decisions with mentor |
+| 1–2 | MMPose loader: complete implementation, COCO-17/Halpe-26/COCO-133 schemas, multi-individual grouping, unit tests |
+| 3–4 | COCO keypoint loader, visibility-to-confidence mapping, integration tests, MMPose gallery example |
+| 5–6 | FreeMocap 3D loader, verify kinematics tools handle 3D space coord, gallery example |
+| 7–8 | motionBIDS loader, BIDS directory traversal, sidecar metadata extraction |
+| 9–10 | BVH loader (stretch), forward kinematics via bvhio library |
+| 11 | C3D loader (stretch), from_file format detection utility |
+| 12 | Documentation, gallery examples, cleanup, final PR review |
+
+### Communication plan
+
+Weekly async updates on the movement GitHub discussions or Zulip covering
+what was completed and any blockers. All work submitted as focused PRs, one
+loader per PR. Available for video calls during European morning hours.
+
+## Personal statement
+
+### Past experience
+
+I have a background in computer vision engineering. One area I worked on was
+video capture models for collecting gameplay data — writing pipelines that
+process video frames, track objects and joints across frames, and feed that
+data into downstream systems. That work gave me hands-on experience with
+pose estimation tools including MMPose and MediaPipe, which are the backbone
+of two of the formats this project targets (MMPose JSON and FreeMocap).
+
+On the open source side: I have a merged PR to pytorch/ignite and sktime, an approved
+PR to kornia, and an open fix in movement itself (PR #914, fixing a silent
+crash in compute_time_derivative on single-frame data). I also built a
+proof of concept MMPose loader on a fork branch that correctly parses
+MMPose JSON predictions into the movement xarray.Dataset schema.
+
+### Motivation: why this project?
+
+I want to contribute to serious open source scientific software and this
+project sounds like a perfect fit for me. Building a common interface so researchers
+don't have to reinvent the loading logic for every tool they use is something I am interested in. 
+Extending that to human pose formats is a natural next step and I believe I am well positioned
+to do that given my background with the relevant tools.
+
+### Match: why me?
+
+The MMPose loader proof-of-concept is already working on my fork. I
+understand the from_numpy builder pattern from reading the existing loaders
+and have already made a fix to the kinematics module. The CV background means
+I won't be learning the formats from scratch — I have used MMPose, COCO
+keypoint format, and MediaPipe in production work and understand their
+quirks (schema mismatches, multi-individual grouping, 3D coordinate handling).
+
+### Availability
+
+I am essentially fully available during the GSoC period with no competing
+commitments.
+
+## GSoC
+
+### GSoC experience
+
+I expect structured mentorship, high quality code reviews to learn from, and the experience of working in a frontier computer vision library.
+
+### Other applications
+
+Yes, I am also applying to Gemini CLI (Google) and MesaLLM.
+My preference is GeminiCLI if there is a tie.