Timing Yang retweeted
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…
🎉 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
🎉 Update: Our paper Fast SAM 3D Body has been accepted to ECCV 2026!
yangtiming.github.io/Fast-SA…
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.
🚀 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





