🔍 Ill-posed inverse problems (deblurring, inpainting, phase retrieval) need strong priors, and diffusion models are great ones. But how you use them matters. Posterior samplers fold the prior & data term into the reverse diffusion, needing costly score backprop or inner MCMC. MAP variable-splitting methods decouple them, but lack a dual variable to fix constraint violations. DDiff tackles both. At #CVPR2026 🧵👇

In short, DDiff is an ADMM-inspired diffusion solver that delivers: ✅ Higher image quality across metrics ✅ Robustness to high measurement noise ✅ Better measurement fidelity / lower residual ✅ Faster sampling ✅ Latent-diffusion compatibility ✅ Provable convergence under mild assumptions, a first for diffusion-based posterior optimization

The era of ultra-high-resolution imaging has arrived. Modern image sensors exceeding 200 MP resolution are common in smartphones, with over 400 MP sensors under development. However, the large number of pixels poses significant challenges for acquisition and processing, especially on edge devices. Which pixels should be acquired, and when, for bandwidth-efficient imaging and perception? We introduce Policy-based Foveated Imaging and Perception, an on-device, real-time, predictive, and task-aware framework that dynamically allocates sensor resolution to prioritize important regions under specific perception objectives. This paper will be presented at #SIGGRAPH2026! [1/6]

We further demonstrate that our framework works in real-world settings on a 200 MP Samsung ISOCELL HP2 prototype. For both object tracking and scene text recognition tasks, our sensor attention policy reproduces the smooth pursuit and saccadic scanpaths seen in simulations, achieving 200 MP bandwidth-efficient sensing under real-world conditions with sensor noise. [5/6]

High-fidelity generation is hitting a scaling crisis as DiT compute grows with image resolution and video length. But do we need high-resolution denoising at every step? We introduce Spectral Progressive Diffusion, a plug-and-play framework for efficient image and video generation that directly exploits the spectral autoregression property of diffusion to grow resolution during denoising. [1/7]

The spectral-domain perspective of our approach further enables a new editing capability: keeping low-frequency structure and modifying high-frequency details. This allows for efficient texture and style edits while better preserving geometry compared to SDEdit-style editing. [6/7]