在边缘设备上实时生成真实烹饪图像并监控进度
Real-Time Cooked Food Image Synthesis and Visual Cooking Progress Monitoring on Edge Devices
- 基于食谱和烹饪状态生成真实食物图像
- FID分数降低30%~60%，显著提升真实感
- 适合边缘部署的轻量级模型，支持个性化视觉目标
在边缘设备上从生食材图像生成真实烹饪图像是一项具有挑战性的生成任务，需捕捉烹饪过程中质地、颜色和结构的复杂变化。现有图像到图像生成方法常产生不真实结果或资源消耗过大，难以在边缘部署。本文提出首个基于烤箱的烹饪进度数据集，包含厨师标注的熟度等级，并设计一种轻量级、受食谱与烹饪状态引导的生成器，可基于生食材图像生成逼真食物图像。该方法支持用户自定义视觉目标，而非固定预设。为保证时间一致性与烹饪合理性，引入领域特定的烹饪图像相似性（Culinary Image Similarity, CIS）指标，既作为训练损失，也作为进度监控信号。模型在自建数据集上实现FID降低30%，在公开数据集上降低60%，显著优于现有基线。
原文摘要 · Abstract (English)
Synthesizing realistic cooked food images from raw inputs on edge devices is a challenging generative task, requiring models to capture complex changes in texture, color and structure during cooking. Existing image-to-image generation methods often produce unrealistic results or are too resource-intensive for edge deployment. We introduce the first oven-based cooking-progression dataset with chef-annotated doneness levels and propose an edge-efficient recipe and cooking state guided generator that synthesizes realistic food images conditioned on raw food image. This formulation enables user-preferred visual targets rather than fixed presets. To ensure temporal consistency and culinary plausibility, we introduce a domain-specific \textit{Culinary Image Similarity (CIS)} metric, which serves both as a training loss and a progress-monitoring signal. Our model outperforms existing baselines with significant reductions in FID scores (30\% improvement on our dataset; 60\% on public datasets)
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