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Giới thiệu Virtual Try-On bắt đầu từ hiểu body shape của user. Bài này cover 3 phương pháp input body data — từ đơn giản nhất (height/weight) đến chính xác nhất (upload ảnh) — và cách convert tất cả thành 3D body parameters. 1.

Three Input Modes Mode 1: Basic Measurements (accuracy: ~70%) ├── Input: height (cm), weight (kg) ├── Process: Statistical regression → body shape params └── Output: Estimated SMPL β parameters Mode 2: Detailed Measurements (accuracy: ~85%) ├── Input: height, weight, chest, waist, shoulder ├── Process: Direct mapping → body shape params └── Output: Accurate SMPL β parameters Mode 3: Photo-based (accuracy: ~90%) ├── Input: 1-2 photos (front + side) ├── Process: Pose detection → body shape regression └── Output: Precise SMPL β parameters + pose 2.

Mode 1 — Statistical Body Estimation class BasicBodyEstimator: """Estimate body shape từ height/weight""" # Regression coefficients (trained trên body scan dataset) BODY_RATIOS = { "male": { "chest": lambda h, bmi: 76 + bmi * 1.3 + (h - 170) * 0.15, "waist": lambda h, bmi: 62 + bmi * 1.8 + (h - 170) * 0.1, "shoulder": lambda h, bmi: h * 0.256 + bmi * 0.1, "hip": lambda h, bmi: 80 + bmi * 1.0 + (h - 170) * 0.1, "arm_length": lambda h, bmi: h * 0.44, "torso_length": lambda h, bmi: h * 0.30, }, "female": { "chest": lambda h, bmi: 72 + bmi * 1.1 + (h - 160) * 0.12, "waist": lambda h, bmi: 56 + bmi * 1.6 + (h - 160) * 0.08, "shoulder": lambda h, bmi: h * 0.238 + bmi * 0.08, "hip": lambda h, bmi: 82 + bmi * 1.4 + (h - 160) * 0.12, "arm_length": lambda h, bmi: h * 0.43, "torso_length": lambda h, bmi: h * 0.29, }, } def estimate( self, height_cm: float, weight_kg: float, gender: str = "neutral", ) -> BodyMeasurements: bmi = weight_kg / (height_cm / 100) ** 2 if gender == "neutral": # Average male & female male = self._compute("male", height_cm, bmi) female = self._compute("female", height_cm, bmi) measurements = { k: (male[k] + female[k]) / 2 for k in male } else: measurements = self._compute(gender, height_cm, bmi) return BodyMeasurements( height=height_cm, weight=weight_kg, **measurements, estimation_method="basic", confidence=0.7, ) def _compute( self, gender: str, height: float, bmi: float ) -> dict: ratios = self.BODY_RATIOS[gender] return { key: round(func(height, bmi), 1) for key, func in ratios.items() } 3.

Mode 2 — Detailed Measurements class DetailedBodyMapper: """Map chi tiết measurements sang SMPL parameters""" def map_to_smpl( self, measurements: BodyMeasurements ) -> SMPLParams: """ Convert body measurements → SMPL β parameters SMPL uses 10 shape parameters (β) that control: β[0]: overall body size (height) β[1]: weight/mass β[2]: chest-to-waist ratio β[3]: shoulder width β[4]: hip width β[5-9]: finer shape details """ # Normalize measurements norm = self._normalize(measurements) # Linear mapping to β parameters # (trained via regression on CAESAR dataset) betas = np.array([ norm.height * 2.5 - 4.2, # β0: height norm.bmi * 1.8 - 2.0, # β1: mass norm.chest_waist_ratio * 1.2, # β2: proportions norm.shoulder * 0.8 - 1.0, # β3: shoulders norm.hip * 0.6 - 0.5, # β4: hips 0, 0, 0, 0, 0, # β5-9: default ], dtype=np.float32) return SMPLParams( betas=betas, gender=measurements.gender or "neutral", confidence=0.85, ) 4.

Mode 3 — Photo-based Body Estimation import mediapipe as mp import numpy as np from PIL import Image class PhotoBodyEstimator: """Estimate body từ 1-2 ảnh user""" def __init__(self): self.mp_pose = mp.solutions.pose self.pose = self.mp_pose.Pose( static_image_mode=True, model_complexity=2, min_detection_confidence=0.5, ) def estimate_from_photo( self, front_photo: Image.Image, side_photo: Image.Image | None = None, known_height: float | None = None, ) -> BodyMeasurements: # 1. Detect pose landmarks front_landmarks = self._detect_landmarks(front_photo) # 2. Calculate proportions from landmarks proportions = self._calculate_proportions( front_landmarks ) # 3.

If side photo, improve depth estimation if side_photo: side_landmarks = self._detect_landmarks(side_photo) proportions = self._refine_with_side( proportions, side_landmarks ) # 4.

Convert proportions to measurements if known_height: scale = known_height / proportions["body_height_ratio"] else: scale = 170 # Default height assumption measurements = self._proportions_to_measurements( proportions, scale ) return measurements def _detect_landmarks( self, photo: Image.Image ) -> dict: """Detect 33 pose landmarks""" img_array = np.array(photo.convert("RGB")) results = self.pose.process(img_array) if not results.pose_landmarks: raise ValueError("Không phát hiện được người trong ảnh") landmarks = {} for idx, lm in enumerate(results.pose_landmarks.landmark): name = self.mp_pose.PoseLandmark(idx).name landmarks[name] = { "x": lm.x, "y": lm.y, "z": lm.z, "visibility": lm.visibility, } return landmarks def _calculate_proportions( self, landmarks: dict ) -> dict: """Tính body proportions từ landmarks""" # Shoulder width l_shoulder = landmarks["LEFT_SHOULDER"] r_shoulder = landmarks["RIGHT_SHOULDER"] shoulder_width = abs(l_shoulder["x"] - r_shoulder["x"]) # Hip width l_hip = landmarks["LEFT_HIP"] r_hip = landmarks["RIGHT_HIP"] hip_width = abs(l_hip["x"] - r_hip["x"]) # Torso length mid_shoulder_y = (l_shoulder["y"] + r_shoulder["y"]) / 2 mid_hip_y = (l_hip["y"] + r_hip["y"]) / 2 torso_length = abs(mid_hip_y - mid_shoulder_y) # Body height (top of head to ankles) nose = landmarks.get("NOSE", landmarks["LEFT_EAR"]) l_ankle = landmarks["LEFT_ANKLE"] body_height = abs(l_ankle["y"] - nose["y"]) return { "shoulder_width": shoulder_width, "hip_width": hip_width, "torso_length": torso_length, "body_height_ratio": body_height, "shoulder_hip_ratio": shoulder_width / max(hip_width, 0.01), } 5.

Unified Body Pipeline class BodyEstimationPipeline: """Pipeline thống nhất cho 3 modes""" def __init__(self): self.basic = BasicBodyEstimator() self.detailed = DetailedBodyMapper() self.photo = PhotoBodyEstimator() async def estimate( self, height: float | None = None, weight: float | None = None, chest: float | None = None, waist: float | None = None, shoulder: float | None = None, front_photo: Image.Image | None = None, side_photo: Image.Image | None = None, ) -> BodyEstimationResult: # Determine best mode if front_photo: measurements = self.photo.estimate_from_photo( front_photo, side_photo, known_height=height ) smpl_params = self.detailed.map_to_smpl(measurements) elif chest and waist and shoulder: measurements = BodyMeasurements( height=height, weight=weight, chest=chest, waist=waist, shoulder=shoulder, ) smpl_params = self.detailed.map_to_smpl(measurements) elif height and weight: measurements = self.basic.estimate(height, weight) smpl_params = self.detailed.map_to_smpl(measurements) else: raise ValueError("Cần ít nhất height + weight") return BodyEstimationResult( measurements=measurements, smpl_params=smpl_params, ) Tổng kết Body Estimation module: - 3 input modes — basic (height/weight), detailed (measurements), photo - Statistical estimation — regression từ BMI & height - MediaPipe Pose — 33 landmarks từ ảnh người thật - SMPL mapping — convert measurements → 10 β parameters - Unified pipeline — auto-select best mode dựa trên available data Bài tiếp theo: 3D Avatar Generation — tạo avatar từ SMPL parameters.

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