BREAKING NEWS
LATEST POSTS
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PixVerse – Prompt, lypsync and extended video generation
https://app.pixverse.ai/onboard
PixVerse now has 3 main features:
text to video
➡️ How To Generate Videos With Text Promptsimage to video
➡️ How To Animate Your Images And Bring Them To Lifeupscale
➡️ How to Upscale Your Video
Enhanced Capabilities
– Improved Prompt Understanding: Achieve more accurate prompt interpretation and stunning video dynamics.
– Supports Various Video Ratios: Choose from 16:9, 9:16, 3:4, 4:3, and 1:1 ratios.
– Upgraded Styles: Style functionality returns with options like Anime, Realistic, Clay, and 3D. It supports both text-to-video and image-to-video stylization.New Features
– Lipsync: The new Lipsync feature enables users to add text or upload audio, and PixVerse will automatically sync the characters’ lip movements in the generated video based on the text or audio.
– Effect: Offers 8 creative effects, including Zombie Transformation, Wizard Hat, Monster Invasion, and other Halloween-themed effects, enabling one-click creativity.
– Extend: Extend the generated video by an additional 5-8 seconds, with control over the content of the extended segment. -
Alibaba Group Tongyi Lab WanxAI Wan2.1 – open source model
👍 SOTA Performance: Wan2.1 consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks.
🚀 Supports Consumer-grade GPUs: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models.
🎉 Multiple tasks: Wan2.1 excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation.
🔮 Visual Text Generation: Wan2.1 is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications.
💪 Powerful Video VAE: Wan-VAE delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation.
https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files
https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/example%20workflows_Wan2.1
https://huggingface.co/Wan-AI/Wan2.1-T2V-14B
https://huggingface.co/Kijai/WanVideo_comfy/tree/main
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VES Cinematic Color – Motion-Picture Color Management
This paper presents an introduction to the color pipelines behind modern feature-film visual-effects and animation.
Authored by Jeremy Selan, and reviewed by the members of the VES Technology Committee including Rob Bredow, Dan Candela, Nick Cannon, Paul Debevec, Ray Feeney, Andy Hendrickson, Gautham Krishnamurti, Sam Richards, Jordan Soles, and Sebastian Sylwan.
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The VFX Reference Platform
The VFX Reference Platform is a set of tool and library versions to be used as a common target platform for building software for the VFX industry. Its purpose is to minimise incompatibilities between different software packages, ease the support burden for integrated pipelines and encourage further adoption of Linux by both studios and software vendors. The Reference Platform is updated annually by a group of software vendors in collaboration with the Visual Effects Society Technology Committee.
Each annual reference platform is designated by the calendar year in which major product releases should be targeting that particular reference.
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Deep Compositing in Nuke – a walkthrough
Depth Map: A depth map is a representation of the distance or depth information for each pixel in a scene. It is typically a two-dimensional array where each pixel contains a value that represents the distance from the camera to the corresponding point in the scene. The depth values are usually represented in metric units, such as meters. A depth map provides a continuous representation of the scene’s depth information.
For example, in Arnold this is achieved through a Z AOV, this collects depth of the shading points as seen from the camera.
(more…)
https://help.autodesk.com/view/ARNOL/ENU/?guid=arnold_user_guide_ac_output_aovs_ac_aovs_html
https://help.autodesk.com/view/ARNOL/ENU/?guid=arnold_for_3ds_max_ax_aov_tutorials_ax_zdepth_aov_html -
VFX Giant MPC and Parent Company Technicolor Shut Down Amid ‘Severe Financial Challenges
https://variety.com/2025/film/global/technicolor-vfx-mpc-shutter-severe-challenges-1236316354
Shaun Severi, Head of Creative Production at the Mill, claimed in a LinkedIn post that 4,500 had lost their jobs in 24 hours: “The problem wasn’t talent or execution — it was mismanagement at the highest levels…the incompetence at the top was nothing short of disastrous.”
According to Severi, successive company presidents “buried the company under massive debt by acquiring VFX Studios…the second president, after a disastrous merger of the post houses, took us public, artificially inflating the company’s value — only for it to come crashing down when the real numbers were revealed….and the third and final president, who came from a car rental company, had no vision of what she was building, selling or managing.”
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Moondream Gaze Detection – Open source code
This is convenient for captioning videos, understanding social dynamics, and for specific cases such as sports analytics, or detecting when drivers or operators are distracted.
https://huggingface.co/spaces/moondream/gaze-demo
https://moondream.ai/blog/announcing-gaze-detection
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X-Dyna – Expressive Dynamic Human Image Animation
https://x-dyna.github.io/xdyna.github.io
A novel zero-shot, diffusion-based pipeline for animating a single human image using facial expressions and body movements derived from a driving video, that generates realistic, context-aware dynamics for both the subject and the surrounding environment.
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Flex 1 Alpha – a pre-trained base 8 billion parameter rectified flow transformer
https://huggingface.co/ostris/Flex.1-alpha
Flex.1 started as the FLUX.1-schnell-training-adapter to make training LoRAs on FLUX.1-schnell possible.
FEATURED POSTS
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The Perils of Technical Debt – Understanding Its Impact on Security, Usability, and Stability
In software development, “technical debt” is a term used to describe the accumulation of shortcuts, suboptimal solutions, and outdated code that occur as developers rush to meet deadlines or prioritize immediate goals over long-term maintainability. While this concept initially seems abstract, its consequences are concrete and can significantly affect the security, usability, and stability of software systems.
The Nature of Technical Debt
Technical debt arises when software engineers choose a less-than-ideal implementation in the interest of saving time or reducing upfront effort. Much like financial debt, these decisions come with an interest rate: over time, the cost of maintaining and updating the system increases, and more effort is required to fix problems that stem from earlier choices. In extreme cases, technical debt can slow development to a crawl, causing future updates or improvements to become far more difficult than they would have been with cleaner, more scalable code.
Impact on Security
One of the most significant threats posed by technical debt is the vulnerability it creates in terms of software security. Outdated code often lacks the latest security patches or is built on legacy systems that are no longer supported. Attackers can exploit these weaknesses, leading to data breaches, ransomware, or other forms of cybercrime. Furthermore, as systems grow more complex and the debt compounds, identifying and fixing vulnerabilities becomes increasingly challenging. Failing to address technical debt leaves an organization exposed to security risks that may only become apparent after a costly incident.
Impact on Usability
Technical debt also affects the user experience. Systems burdened by outdated code often become clunky and slow, leading to poor usability. Engineers may find themselves continuously patching minor issues rather than implementing larger, user-centric improvements. Over time, this results in a product that feels antiquated, is difficult to use, or lacks modern functionality. In a competitive market, poor usability can alienate users, causing a loss of confidence and driving them to alternative products or services.
Impact on Stability
Stability is another critical area impacted by technical debt. As developers add features or make updates to systems weighed down by previous quick fixes, they run the risk of introducing bugs or causing system crashes. The tangled, fragile nature of code laden with technical debt makes troubleshooting difficult and increases the likelihood of cascading failures. Over time, instability in the software can erode both the trust of users and the efficiency of the development team, as more resources are dedicated to resolving recurring issues rather than innovating or expanding the system’s capabilities.
The Long-Term Costs of Ignoring Technical Debt
While technical debt can provide short-term gains by speeding up initial development, the long-term costs are much higher. Unaddressed technical debt can lead to project delays, escalating maintenance costs, and an ever-widening gap between current code and modern best practices. The more technical debt accumulates, the harder and more expensive it becomes to address. For many companies, failing to pay down this debt eventually results in a critical juncture: either invest heavily in refactoring the codebase or face an expensive overhaul to rebuild from the ground up.
Conclusion
Technical debt is an unavoidable aspect of software development, but understanding its perils is essential for minimizing its impact on security, usability, and stability. By actively managing technical debt—whether through regular refactoring, code audits, or simply prioritizing long-term quality over short-term expedience—organizations can avoid the most dangerous consequences and ensure their software remains robust and reliable in an ever-changing technological landscape.