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.
👍 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.
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.
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.
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.
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.”
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.
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.
🔸 Gaussian Splats: imagine throwing thousands of tiny ellipsoidal paint drops. They overlap, blend, and create a smooth, photorealistic look. Fast, great for visualization, but less structured for measurements.
🔸 Point Clouds: every dot is a measured hit. LiDAR or photogrammetry gives us millions of them forming a constellation of reality. Amazing for accuracy, but they don’t connect the dots out of the box.
🔸 Meshes: take those points, connect them into triangles, and you get very realistic surfaces. Strong for 3D analysis, simulation as continues watertight models.
Basically, gamma is the relationship between the brightness of a pixel as it appears on the screen, and the numerical value of that pixel. Generally Gamma is just about defining relationships.
Three main types: – Image Gamma encoded in images – Display Gammas encoded in hardware and/or viewing time – System or Viewing Gamma which is the net effect of all gammas when you look back at a final image. In theory this should flatten back to 1.0 gamma.
The intricate relationship between the eyes and the brain, often termed the eye-mind connection, reveals that vision is predominantly a cognitive process. This understanding has profound implications for fields such as design, where capturing and maintaining attention is paramount. This essay delves into the nuances of visual perception, the brain’s role in interpreting visual data, and how this knowledge can be applied to effective design strategies.
This cognitive aspect of vision is evident in phenomena such as optical illusions, where the brain interprets visual information in a way that contradicts physical reality. These illusions underscore that what we “see” is not merely a direct recording of the external world but a constructed experience shaped by cognitive processes.
Understanding the cognitive nature of vision is crucial for effective design. Designers must consider how the brain processes visual information to create compelling and engaging visuals. This involves several key principles:
