Zhengzhong Tu

🎉 [𝗡𝗲𝘂𝗿𝗜𝗣𝗦 𝟮𝟬𝟮𝟱] 𝗣𝗮𝗽𝗲𝗿 𝗔𝗰𝗰𝗲𝗽𝘁𝗲𝗱 𝗮𝗻𝗱 𝗖𝗼𝗱𝗲 𝗥𝗲𝗹𝗲𝗮𝘀𝗲! Not everyone is a photo pro — even with advanced AI tools. Restoring an image to professional grade often requires either 𝘥𝘦𝘦𝘱 𝘱𝘩𝘰𝘵𝘰𝘨𝘳𝘢𝘱𝘩𝘺 𝘦𝘹𝘱𝘦𝘳𝘵𝘪𝘴𝘦 or 𝘣𝘳𝘰𝘢𝘥 𝘬𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 of how to combine many different AI models. 🙋 𝗦𝗼 𝘄𝗲 𝗮𝘀𝗸𝗲𝗱 𝗼𝘂𝗿𝘀𝗲𝗹𝘃𝗲𝘀: Can we build a restoration agent that fully automates this process — no domain expertise required? To answer this, we Texas A&M University partnered with Beth Allison and scholars from Stanford University, Caltech, UT Austin, Snap Inc., UC Merced, CU Boulder 🦬 to develop the 𝘄𝗼𝗿𝗹𝗱’𝘀 𝗳𝗶𝗿𝘀𝘁 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗽𝗵𝗼𝘁𝗼 𝗿𝗲𝘀𝘁𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝘀𝘆𝘀𝘁𝗲𝗺. Our system brings together over 𝟱𝟬 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 — for denoising, deblurring, upscaling, face recovery, detail enhancement, and more. 🔬 It diagnoses the input image. 🦾 It plans and executes an action graph (just like a human editor). ☑️ After each step, it evaluates the output and adapts the plan if needed. The results are phenomenal — as you’ll see in the video. We believe democratizing professional photography will empower anyone to create images suitable for professional use cases. And because we see this as a responsibility, we are 𝗳𝘂𝗹𝗹𝘆 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗶𝗻𝗴 𝘁𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 — to accelerate progress in agentic photo restoration and support the broader community. 👉 𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝘁𝗵𝗲 𝗰𝗼𝗱𝗲: 📄 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝘄𝗲𝗯𝗽𝗮𝗴𝗲: 𝗜'𝗺 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿𝘄𝗮𝗿𝗱 𝘁𝗼 𝘀𝗲𝗲𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗯𝘂𝗶𝗹𝗱 𝘂𝘀𝗶𝗻𝗴 𝗼𝘂𝗿 𝗼𝗽𝗲𝗻–𝘀𝗼𝘂𝗿𝗰𝗲 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗮𝗻𝗱 𝘁𝗼 𝗿𝗲𝘃𝗶𝗲𝘄𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗱𝗲𝗺𝗼 𝗮𝗻𝗱 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸!