Introducing Stable Video 3D
Introducing Stable Video 3D: Quality Novel View Synthesis and 3D Generation from Single Images
Stability AI is introducing Stable Video 3D (SV3D), a generative model based on their previous Stable Video Diffusion model, aimed at making significant advancements in 3D technology. SV3D promises greatly improved quality and multi-view consistency. The model comes in two variants, SV3Du and SV3Dp - the first generates orbital videos from single image inputs, while the second extends this ability to accommodate orbital views, enabling the creation of 3D videos along specified paths.
Notably, the new model is touted to generate better quality 3D meshes from single image inputs and optimize 3D Neural Radiance Fields and mesh representations. The model is now available for commercial use with a Stability AI Membership, and non-commercial users can download the model weights on Hugging Face.
If you're reliant on Java, you shouldn't update to macOS Sonoma 14.4 yet
A bug in the recent MacOS Sonoma 14.4 update causes Java processes to terminate unexpectedly. Hence it is advised that Mac users who depend on Java delay this update. The problem is impacting all versions from Java 8 onwards. As per Oracle, there is no present workaround. Even though the flaw wasn't detected in the 14.4 beta versions, Apple Silicon Mac users who've already upgraded to this version are encouraged to wait for a fix from Apple.
10x Performance with SIMD Vectorized Code in C#/.NET
This blog post explains about achieving 10x performance optimization with SIMD vectorized code in C#.The discussion begins with how to efficiently find a specified int from an int using x86-64 SIMD instructions and it addresses the details of both scalar versions and SIMD versions of it. The Scalar versions are pretty simplified while the introduction of SIMD version brings noticeable performance improvement. Recent additions to .NET 7 allows the usage of generic SIMD code which simplifies the process and allows for easy transition from scalar to vectorized/SIMD.
The benefits of SIMD version over scalar version are shown with a set of benchmarks. They concluded that by just using plain generic SIMD instructions, a performance boost from 2x to 4x can be achieved. However, one could go further by using CPU specific SIMD intrinsics instructions with Intel x86-64 for greater performance enhancement. The post is highly detailed and serves as an educational piece on SIMD for developers unfamiliar with or intimidated by SIMD code.