Pixelle-Video lets users input a single topic and, within minutes, automatically generates a short video with script, AI‑generated visuals, synthesized voice‑over, background music, and final rendering, eliminating the need for manual editing or specialized software.
This case study details how the 58.com UX team used AI tools to create a 40‑second pre‑video for the China Good Employer awards, outlining the visual framework, prompt engineering, segmented generation workflow, and the balance between automation and designer insight.
LongCat‑Video, an open‑source video generation model from Meituan, adopts a unified multi‑task architecture to handle text‑to‑video, image‑to‑video and video‑continuation, delivers minute‑long high‑quality clips with coarse‑to‑fine inference, achieves benchmark scores comparable to leading models like Wan2.2, and provides a one‑click deployment tutorial on HyperAI.
This guide explains why character continuity often breaks in AI‑generated long videos and presents two practical workflows—controlling consistency during image generation and during image‑to‑video conversion—along with tools, tips, and a side‑by‑side comparison to help creators produce seamless, emotionally engaging stories.
Fast‑forwarding image‑to‑video generation, the article introduces I2V‑Adapter, a lightweight plug‑in for Stable Diffusion‑based video diffusion models that converts a single static image into a coherent video without altering the original T2V architecture, and details its design, frame‑similarity prior, experimental results, and real‑world applications.
The I2V-Adapter paper introduces a plug‑and‑play lightweight module that enables static images to be converted into dynamic videos using Stable Diffusion‑based text‑to‑video diffusion models without altering the original architecture or pretrained parameters, achieving competitive quality with far less training cost.
Since 2023, AI‑generated video research has expanded across six algorithmic categories—text‑to‑video, image‑to‑video, editing, style transfer, human motion, and long‑video generation—highlighting works such as CogVideo, Imagen Video, MagicVideo, ControlVideo, DCTNet, NUWA‑XL and OpenAI’s Sora, while analysis shows short‑clip diffusion models excel, editing remains costly, style transfer is efficient, and truly long, temporally consistent videos remain an open challenge.
This article presents Alibaba's research on using diffusion‑based AIGC techniques, including a training‑free Noise Rectification module and the AtomoVideo model, to automatically convert static product images into high‑quality, detail‑preserving video motions for e‑commerce advertising.
Alibaba’s AI team introduced AtomoVideo, a diffusion‑based image‑to‑video generator enhanced by a training‑free Noise Rectification module that adds and corrects controlled noise to eliminate first‑frame errors, enabling merchants to automatically create high‑fidelity 4‑second 720p product videos with strong temporal consistency for e‑commerce advertising.
