Diffusion models have recently emerged as powerful tools for camera simulation, enabling both geometric transformations and realistic optical effects. Among these, image-based bokeh rendering has shown promising results, but diffusion for video bokeh remains unexplored. Existing image-based methods are plagued by temporal flickering and inconsistent blur transitions, while current video editing methods lack explicit control over the focus plane and bokeh intensity. These issues limit their applicability for controllable video bokeh. In this work, we propose a one-step diffusion framework for generating temporally coherent, depth-aware video bokeh rendering. The framework employs a multi-plane image (MPI) representation adapted to the focal plane to condition the video diffusion model, thereby enabling it to exploit strong 3D priors from pretrained backbones. To further enhance temporal stability, depth robustness, and detail preservation, we introduce a progressive training strategy. Experiments on synthetic and real-world benchmarks demonstrate superior temporal coherence, spatial accuracy, and controllability, outperforming prior baselines. This work represents the first dedicated diffusion framework for video bokeh generation, establishing a new baseline for temporally coherent and controllable depth-of-field effects.

In November, the San Francisco Police Department proposed approving the use of remote-controlled robots with deadly force. This was after a law came into effect requiring California officials to define the authorized use of military-grade equipment. It would have allowed police to equip robots with explosives "to contact, incapacitate or disorient violent, armed or dangerous suspects." San Francisco's Board of Supervisors approved this proposal, initially, despite opposition by civil rights groups. However, during the second of two required votes, the board voted to ban the use of lethal force by police robots. According to the San Francisco Chronicle, this is unusual as the board's second votes typically echo the first results.

Many smartphones these days offer artificial bokeh in their portrait photography modes, but with the help of a retractable camera, you can achieve true optical bokeh without missing any edges. Chinese brand Tecno is now bringing such a feature to its latest flagship device, the Phantom X2 Pro 5G, which packs a "world-first" pop-out portrait lens. This is just a little over two years after Xiaomi showed off a retractable 120mm-equivalent wide aperture lens, but it never left the prototype stage. Tecno's intriguing portrait camera has a 50-megapixel resolution with a relatively large 1/2.7-inch Optically, this 2.5x zoom lens offers an f/1.49

Dask is a powerful open-source Python parallel computing framework. Dask scales Python programs from single-core local workstations to huge distributed cloud clusters. Dask provides a familiar user experience by replicating the APIs of other PyData ecosystem programs like Pandas, Scikit-learn, and NumPy. It also offers low-level APIs that allow programmers to execute bespoke algorithms concurrently.

A photographer has created portraits of people who do not exist but were instead made with the artificial intelligence (AI) program Dall-E 2. Mathieu Stern, a French photographer, used the nascent software that is not yet easily available to the public to create photorealistic portraits of fictitious people that he documented in a YouTube video. Stern, who recently made a series of wild camera designs on the program, started by instructing Dall-E to create an image of "a young beautiful woman wearing a yellow kimono, in a tropical greenhouse." "At first the lack of information about the camera, the lens, and the general look of the image, led to rather unimpressive results," Stern explains on YouTube. "So to help Dall-E, some details must be added to the general description, like the lens, the camera, the film, and adding some words like bokeh." Stern says the best results came after adding the word "Graflex."

Python continues to lead the way when it comes to Machine Learning, AI, Deep Learning and Data Science tasks. Because of this, we've decided to start a series investigating the top Python libraries across several categories: Of course, these lists are entirely subjective as many libraries could easily place in multiple categories. Now, let's get onto the list (GitHub figures correct as of November 16th, 2018): "pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python." "Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell (à la MATLAB or Mathematica), web application servers, and various graphical user interface toolkits."

Recently, I was going through a video from SciPy 2015 conference, "Building Python Data Apps with Blaze and Bokeh", recently held at Austin, Texas, USA. I couldn't stop thinking about the power these two libraries provide to data scientists using Python across the globe. In this article, I will introduce you to the world of possibilities in data visualization using Bokeh and why I think this is a must learn / use library for every data scientist out there. Bokeh is a Python library for interactive visualization that targets web browsers for representation. This is the core difference between Bokeh and other visualization libraries.

Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, but also deliver this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.

Django continues to be the pick of libraries for Python developers. However, there are some not-so-well-known libraries that happened to gain traction among Python developers in 2016. In this blog, I am unveiling 7 Python libraries, excluding the established ones like Django, Flask, etc, that Python developers may find worth considering in 2017. Mobile apps are everywhere, and are often meant for global population - be it for games, social media, health monitoring and whatnot. However, the problem with the standard data/time library for Python is that it doesn't meet the requirements of modern apps that have their target audience living in different regions and countries.

Django continues to be the pick of libraries for Python developers. However, there are some not-so-well-known libraries that happened to gain traction among Python developers in 2016. In this blog, I am unveiling 7 Python libraries, excluding the established ones like Django, Flask, etc, that Python developers may find worth considering in 2017. Mobile apps are everywhere, and are often meant for global population - be it for games, social media, health monitoring and whatnot. However, the problem with the standard data/time library for Python is that it doesn't meet the requirements of modern apps that have their target audience living in different regions and countries.