{"ID":2838450,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.16965","arxiv_id":"2511.16965","title":"Real-Time Cooked Food Image Synthesis and Visual Cooking Progress Monitoring on Edge Devices","abstract":"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)","short_abstract":"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 depl...","url_abs":"https://arxiv.org/abs/2511.16965","url_pdf":"https://arxiv.org/pdf/2511.16965v1","authors":"[\"Jigyasa Gupta\",\"Soumya Goyal\",\"Anil Kumar\",\"Ishan Jindal\"]","published":"2025-11-21T05:38:15Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\"]","methods":"[]","has_code":false}