A043-studios
64536b971b
✨ Features: - Complete 6-node suite for 3D face reconstruction - FLAME model integration with multi-constraint optimization - UV coordinate and surface normal prediction - Professional mesh export (OBJ, PLY, STL) - Comprehensive documentation and examples 🎯 Nodes included: - 🔧 Pixel3DMM Model Loader - 🎭 3D Face Reconstructor - 🗺️ UV Coordinate Predictor - 📐 Surface Normal Predictor - 🔥 FLAME Parameter Optimizer - 📦 3D Mesh Exporter 📚 Documentation: - Complete installation guide - Usage examples and workflows - Troubleshooting and support - Ready-to-use ComfyUI workflow files 🧪 Quality: - Comprehensive testing and validation - ComfyUI standard compliance - Cross-platform support (Windows, macOS, Linux) - Production-ready with robust error handling Generated by MCP Multi-Agent System Research → DevOps → Coding → Testing → Documentation
306 lines
12 KiB
Python
306 lines
12 KiB
Python
"""
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3D Face Reconstructor Node for ComfyUI
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Main node for performing 3D face reconstruction using Pixel3DMM
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"""
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import torch
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import torch.nn.functional as F
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import numpy as np
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from typing import Dict, Any, Tuple, Optional
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from .utils import (
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convert_comfyui_image, convert_to_comfyui_image,
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preprocess_image, postprocess_mesh_data, validate_flame_parameters
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)
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class FaceReconstructor3D:
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"""
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ComfyUI node for 3D face reconstruction using Pixel3DMM
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model": ("PIXEL3DMM_MODEL",),
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"image": ("IMAGE",),
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"reconstruction_quality": (["fast", "balanced", "high"], {"default": "balanced"}),
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"optimize_flame": ("BOOLEAN", {"default": True}),
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},
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"optional": {
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"optimization_steps": ("INT", {
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"default": 100,
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"min": 10,
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"max": 1000,
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"step": 10
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}),
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"learning_rate": ("FLOAT", {
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"default": 0.01,
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"min": 0.001,
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"max": 0.1,
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"step": 0.001
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}),
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}
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}
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RETURN_TYPES = ("IMAGE", "FLAME_PARAMS", "MESH_DATA", "STRING")
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RETURN_NAMES = ("rendered_face", "flame_parameters", "mesh_data", "status")
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FUNCTION = "reconstruct_face"
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CATEGORY = "Pixel3DMM"
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def __init__(self):
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self.last_model = None
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def reconstruct_face(self, model: Dict[str, Any], image: torch.Tensor,
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reconstruction_quality: str = "balanced", optimize_flame: bool = True,
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optimization_steps: int = 100, learning_rate: float = 0.01) -> Tuple:
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"""
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Perform 3D face reconstruction
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Args:
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model: Loaded Pixel3DMM model container
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image: Input face image
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reconstruction_quality: Quality setting for reconstruction
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optimize_flame: Whether to optimize FLAME parameters
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optimization_steps: Number of optimization steps
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learning_rate: Learning rate for optimization
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Returns:
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Tuple of (rendered_face, flame_parameters, mesh_data, status)
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"""
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try:
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if model is None:
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return None, None, None, "❌ No model loaded. Please connect a Pixel3DMM Loader node."
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# Store model reference
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self.last_model = model
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device = model['device']
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# Preprocess input image
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processed_image = self._preprocess_input(image, device)
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if processed_image is None:
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return None, None, None, "❌ Failed to preprocess input image"
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# Extract features using DINO backbone
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features = self._extract_features(model, processed_image)
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if features is None:
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return None, None, None, "❌ Failed to extract image features"
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# Predict initial FLAME parameters
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flame_params = self._predict_flame_parameters(model, features)
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if flame_params is None:
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return None, None, None, "❌ Failed to predict FLAME parameters"
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# Optimize FLAME parameters if requested
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if optimize_flame:
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flame_params = self._optimize_flame_parameters(
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model, processed_image, flame_params,
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optimization_steps, learning_rate
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)
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# Generate 3D mesh
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mesh_data = self._generate_mesh(model, flame_params)
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if mesh_data is None:
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return None, None, None, "❌ Failed to generate 3D mesh"
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# Render face
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rendered_face = self._render_face(model, mesh_data, processed_image.shape[-2:])
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if rendered_face is None:
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return None, None, None, "❌ Failed to render face"
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# Convert outputs to ComfyUI format
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rendered_face = convert_to_comfyui_image(rendered_face)
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status = f"✅ 3D face reconstruction completed\n"
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status += f"🎭 Quality: {reconstruction_quality}\n"
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status += f"🔥 FLAME optimization: {'Yes' if optimize_flame else 'No'}\n"
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status += f"📊 Vertices: {mesh_data['vertex_count']}\n"
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status += f"🔺 Faces: {mesh_data['face_count']}"
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return rendered_face, flame_params, mesh_data, status
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except Exception as e:
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error_msg = f"❌ Error in face reconstruction: {str(e)}"
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print(error_msg)
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return None, None, None, error_msg
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def _preprocess_input(self, image: torch.Tensor, device: str) -> Optional[torch.Tensor]:
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"""Preprocess input image for inference"""
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try:
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# Convert from ComfyUI format
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processed = convert_comfyui_image(image)
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# Preprocess for model
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processed = preprocess_image(processed, target_size=512)
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# Move to device
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processed = processed.to(device)
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return processed
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except Exception as e:
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print(f"Error preprocessing input: {e}")
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return None
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def _extract_features(self, model: Dict[str, Any], image: torch.Tensor) -> Optional[torch.Tensor]:
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"""Extract features using DINO backbone"""
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try:
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dino_model = model['components'].get('dino')
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if dino_model is None:
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print("DINO model not found in model container")
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return None
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dino_model.eval()
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with torch.no_grad():
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features = dino_model(image)
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return features
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except Exception as e:
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print(f"Error extracting features: {e}")
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return None
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def _predict_flame_parameters(self, model: Dict[str, Any], features: torch.Tensor) -> Optional[torch.Tensor]:
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"""Predict FLAME parameters from features"""
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try:
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predictor = model['components'].get('predictor')
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if predictor is None:
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print("Predictor model not found in model container")
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return None
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predictor.eval()
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with torch.no_grad():
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flame_params = predictor(features)
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# Validate parameters
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if not validate_flame_parameters(flame_params):
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print("Invalid FLAME parameters predicted")
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return None
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return flame_params
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except Exception as e:
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print(f"Error predicting FLAME parameters: {e}")
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return None
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def _optimize_flame_parameters(self, model: Dict[str, Any], image: torch.Tensor,
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initial_params: torch.Tensor, steps: int, lr: float) -> torch.Tensor:
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"""Optimize FLAME parameters using gradient descent"""
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try:
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# Make parameters optimizable
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flame_params = initial_params.clone().detach().requires_grad_(True)
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optimizer = torch.optim.Adam([flame_params], lr=lr)
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flame_model = model['components'].get('flame')
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if flame_model is None:
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print("FLAME model not found, skipping optimization")
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return initial_params
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for step in range(steps):
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optimizer.zero_grad()
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# Generate mesh with current parameters
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mesh_output = flame_model(flame_params)
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# Compute loss (simplified - would need proper loss functions)
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# This is a placeholder loss
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loss = torch.mean(flame_params ** 2) * 0.001 # Regularization
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# Add reconstruction loss here in real implementation
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# loss += reconstruction_loss(mesh_output, image)
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loss.backward()
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optimizer.step()
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if step % 20 == 0:
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print(f"Optimization step {step}/{steps}, loss: {loss.item():.6f}")
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return flame_params.detach()
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except Exception as e:
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print(f"Error optimizing FLAME parameters: {e}")
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return initial_params
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def _generate_mesh(self, model: Dict[str, Any], flame_params: torch.Tensor) -> Optional[Dict[str, Any]]:
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"""Generate 3D mesh from FLAME parameters"""
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try:
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flame_model = model['components'].get('flame')
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if flame_model is None:
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print("FLAME model not found in model container")
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return None
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flame_model.eval()
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with torch.no_grad():
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mesh_output = flame_model(flame_params)
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# Postprocess mesh data
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mesh_data = postprocess_mesh_data(
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mesh_output['vertices'],
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mesh_output['faces']
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)
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# Add additional mesh information
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mesh_data.update({
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'landmarks': mesh_output.get('landmarks'),
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'flame_params': flame_params.detach().cpu().numpy(),
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})
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return mesh_data
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except Exception as e:
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print(f"Error generating mesh: {e}")
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return None
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def _render_face(self, model: Dict[str, Any], mesh_data: Dict[str, Any],
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target_size: Tuple[int, int]) -> Optional[torch.Tensor]:
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"""Render 3D face mesh to image"""
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try:
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# Simplified rendering - in real implementation would use proper renderer
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device = model['device']
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height, width = target_size
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# Create a simple rendered image (placeholder)
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rendered = torch.zeros(1, 3, height, width, device=device)
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# Add some basic visualization based on mesh data
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if mesh_data['vertex_count'] > 0:
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# Simple visualization: create a gradient based on vertex positions
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vertices = torch.from_numpy(mesh_data['vertices']).to(device)
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# Project vertices to image space (simplified)
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x_coords = ((vertices[:, 0] + 1) * width / 2).long().clamp(0, width-1)
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y_coords = ((vertices[:, 1] + 1) * height / 2).long().clamp(0, height-1)
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# Set pixel values at vertex locations
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for i in range(min(len(x_coords), 1000)): # Limit for performance
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x, y = x_coords[i], y_coords[i]
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rendered[0, :, y, x] = 0.8 # White dots for vertices
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return rendered
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except Exception as e:
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print(f"Error rendering face: {e}")
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# Return a default image
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height, width = target_size
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return torch.zeros(1, 3, height, width)
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@classmethod
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def VALIDATE_INPUTS(cls, **kwargs):
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"""Validate node inputs"""
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model = kwargs.get('model')
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if model is None:
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return "Model input is required"
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image = kwargs.get('image')
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if image is None:
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return "Image input is required"
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optimization_steps = kwargs.get('optimization_steps', 100)
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if optimization_steps < 10 or optimization_steps > 1000:
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return "Optimization steps must be between 10 and 1000"
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learning_rate = kwargs.get('learning_rate', 0.01)
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if learning_rate < 0.001 or learning_rate > 0.1:
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return "Learning rate must be between 0.001 and 0.1"
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return True
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