Proposed: Image to Full Body FACS

Context

This system builds a robust image-to-full-body-face mapping pipeline to capture real-world 3D full-body movements synchronized with visual data. Key metrics include anatomical accuracy of motion prediction, scalability across diverse body types, and real-time processing capabilities. This proposal addresses the growing demand for realistic full-body animation in virtual avatars, gaming, and biomechanical analysis. It matters now due to advancements in computer vision and the need for cost-effective motion capture solutions beyond lab-controlled environments.

Problem Statement

Primary operational challenges include:

Lack of accessible systems for accurate 3D full-body motion capture from 2D images/video
Difficulty generating anatomically plausible animations from imperfect visual data
High computational costs of existing marker-based motion capture systems
Limited adaptability to real-world environmental noise and occlusion

Proposed Solution

An image-to-FACS pipeline combining synthetic data generation, biomechanical constraints, and reinforcement learning:

Convert T-posed animations to VRM avatar format with COCO keypoint annotations
Generate synthetic training data using Godot Engine with marker/color variations
Leverage MediaPipe for video-to-keypoint conversion with vertex-based facial mapping
Train custom prediction models using temporal-spatial attention mechanisms

Implementation Steps:

Create VRM synthetic datasets with ground truth annotations
Develop MediaPipe integration for unified body-face-hand keypoint extraction
Implement reinforcement learning (GRPO) for motion generation from masked video

Implementation Plan

Phase 1: Synthetic Data Pipeline
- Develop Animation-to-VRM conversion tools
- Generate multi-format synthetic data in Godot Engine
Phase 2: Keypoint Prediction Model
- Train transformer-based model on synthetic/real data mixtures
- Integrate biomechanical constraints into loss functions
Validation:
- Success Criteria: >90% anatomical validity in user studies
- Failure Threshold: <15% improvement over baseline MediaPipe accuracy

Datasets

Dataset ID	Description	URI	License
ANIM_A_POSE	T-posed character animations for baseline modeling	N/A	Proprietary
ANIM_O3DE_COCO_FULL_BODY_FACS_RGB_8	Full-body FACS annotations with RGB video pairs	N/A	Apache 2.0
ANIM_O3DE_COCO_FULL_BODY_FACS_LUMINANCE_8	Luma-key marker data synchronized with RGB	N/A	Apache 2.0
ANIM_O3DE_MOTION_MATCHING	O3DE Motion Matching dataset	GitHub	Apache 2.0
ANIM_QUATERNIUS_UNIVERSAL_ANIMATION_LIBRARY	Universal Animation Library	N/A	CC0 1.0
ANIM_100_STYLES	Multi-style character animation dataset	N/A	Proprietary
COCO_2017_KEYPOINTS	COCO person keypoints dataset	Kaggle	CC BY 4.0
COCO_YOGA_16	Yoga pose classification dataset	Kaggle	MIT

Benefits

Enables markerless full-body motion capture from consumer-grade video
Maintains facial-phonetic synchronization during body movements
Reduces dependency on expensive motion capture hardware
Supports real-time applications through efficient architecture design

Risks and Limitations

Potential error accumulation in long-term motion prediction
Challenges maintaining facial-hand-body coordination
Dependency on quality synthetic training data
Computational intensity of real-time 3D mesh processing

Alternatives Considered

Option	Pros	Cons
Commercial mocap suits	High accuracy	Cost-prohibitive, lab-bound
Pure ML approaches	Fully automated	Limited anatomical plausibility

When to Avoid This Solution

Not suitable for applications requiring medical-grade biomechanical accuracy or environments with persistent heavy occlusion.

Organizational Alignment

Supports strategic goals in virtual humanoid development and aligns with open-source initiatives for accessible animation tools.