Updated
Updated
AIApple researchers introduced Normalizing Trajectory Models (NTM), which model each reverse diffusion step as a conditional normalizing flow trained with exact likelihood. The model matches or outperforms strong image generation baselines on text-to-image benchmarks in just four sampling steps while retaining exact likelihood over the generative trajectory.
AIGoogle Research has introduced Diffusion Controller, a lightweight steering damper that precisely guides image generation to improve prompt alignment. The post says it improves image quality while preserving baseline stability, with details available in the linked blog.
AIGoogle Research introduced Diffusion Controller, a framework that treats image generation as a continuous control problem rather than separate inference-time guidance and fine-tuning fixes. Its lightweight add-on "steering damper" network keeps the base model frozen and works on black-box or gray-box models, and it outperformed the industry standard on human preference matching. In a Stable Diffusion v1.4 test, the fully unlocked version achieved a 90% win rate over the baseline.
AIHiggsfield AI released "Passport Rush," described as the first hybrid AI-animated short made with a Blender and Higgsfield pipeline. The film combines handcrafted 2D storyboards and 3D Blender previs with AI animation. The project is now open source, with every prompt and asset publicly available.
AIAmazon Neuron Science and Reactor collaborated to optimize Rolling Forcing, an autoregressive diffusion video model, for real-time generation on AWS Trainium. The model generates 16 frames per second, the standard video playback rate, meeting latency requirements for streaming video generation.
AIMeta AI announced Muse Realtime Avatar and directed readers to its research blog for details. The post itself provides no further technical specifications, features, or availability information.
AIStability AI's interactive research team presented new work at the 19th European Conference on Computer Vision (ECCV) aimed at keeping colors consistent across shots and AI-generated reference photos. The post frames color consistency as a persistent friction point in production.
AIFei-Fei Li praised GPIC, a new benchmark dataset for visual generation built for modern large-scale generative models. The corpus includes 100M VLM-captioned image-text pairs for training and 1M pairs for benchmarking, totaling about 28 trillion pixels. It is centrally hosted and fully permissive for research and commercial use.
AIResearchers introduced a simple, sample-efficient online reinforcement learning technique for post-training image generation models. It is presented as a possible steerable alternative to classifier-free guidance (CFG) that can be driven by any scalar reward, including human preference.
AITsinghua AIR and BAAI's DrugCLIP screened 10,000 proteins against 500 million molecules, identifying over 2 million drug candidates. The post claims a 1-million-fold speedup, reaching 10 trillion protein-molecule pairs per day, and positions DrugCLIP as bridging AlphaFold structures to drug candidates. The work is published in Science, with a platform available at drugclip.com.
AIApple has released SHARP, a model that generates a 3D Gaussian representation of a scene from a single photograph in less than a second on a standard GPU. The output renders in real time as high-resolution photorealistic views of nearby camera positions, with metric absolute scale, and the paper reports reductions of 25–34% in LPIPS and 21–43% in DISTS versus the best prior model.