Timing Yang (@TimingYang99) profile picture

PhD @ JHU | MS USC | Prev. Intern at Mayo, Tsinghua IIIS | Computer Vision, Robotics & Medical AI

Joined March 2026
Photo by Timing Yang (@TimingYang99)
8. Let RGB Be the Language of Vision 🔑 Keywords: RGB In and RGB Out (RINO), unified vision-language systems, structured visual signals, zero-shot performance 💡 Category: Computer Vision 🌟 Research Objective: - This research introduces a unified formulation for vision models, termed as RGB In and RGB Out (RINO), to handle diverse visual information as RGB images and convert visual tasks into a common RGB-to-RGB image editing problem. 🛠️ Research Methods: - The study utilizes a generic image editing backbone without task-specific fine-tuning, allowing diverse visual tasks to share encoding and decoding architecture, similar to text operation in language models. 💬 Research Conclusions: - RINO displays robust zero-shot performance in both dense understanding tasks like segmentation and dense-conditioned generation tasks like pose-to-image generation, advancing towards creating general unified vision-language systems. 👉 Paper link: huggingface.co/papers/2607.1…
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🎉 Update: Our paper Fast SAM 3D Body has been accepted to ECCV 2026!
🚀 Fast SAM 3D Body — accelerating SAM 3D Body for real-time human mesh recovery! ⚡ 10.25× faster 3D body estimation ⚡ 10,426× faster MHR→SMPL conversion ⏱️ ~65ms end-to-end 🤖 Deployable on humanoid robots #Robotics
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Timing Yang retweeted
Two months ago, I vaguely posted a number: 0.9 FID, one-step, pixel space. Now it is 0.75, and can be even lower. Many wonder how. I thought it might end as a small FID prank: simple and deliberate. It started with one question: can FID be optimized directly, and what does it reveal? Introducing FD-loss.
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