How LongVie 2 Solves AI Video Generation: Sharp, Steerable 5-Minute Clips
9 hours ago 高效码农LongVie 2 in Plain English: How to Keep AI-Generated Videos Sharp, Steerable, and Five-Minutes Long “ Short answer: LongVie 2 stacks three training tricks—multi-modal control, first-frame degradation, and history context—on top of a 14 B diffusion backbone so you can autoregressively create 3–5 minute clips that stay visually crisp and obey your depth maps and point tracks the whole way through. What problem is this article solving? “Why do today’s video models look great for 10 seconds, then turn into blurry, flickering soup?” Below we walk through LongVie 2’s pipeline, show exact commands to run it on a single A100, …
Scone AI: The Breakthrough in Precise Subject-Driven Image Generation
4 days ago 高效码农Scone: Teaching AI to “Pick the Right Person” in a Crowd – A Leap Towards Precise Subject-Driven Image Generation Snippet The Scone model addresses a critical challenge in subject-driven image generation: accurately identifying and generating only the instruction-specified subject from a reference image containing multiple candidates. It introduces an “understanding bridge strategy” within a unified understanding-generation architecture, leveraging the early semantic advantages of the understanding expert to guide the generation process. This results in superior composition and distinction capabilities, achieving a leading overall score of 8.50 among open-source models on the new SconeEval benchmark. Have you ever imagined handing an …
PersonaLive: The Real-Time Portrait Animation Breakthrough Changing Live Streaming
5 days ago 高效码农PersonaLive: A Breakthrough Framework for Real-Time Streaming Portrait Animation Abstract PersonaLive is a diffusion model-based portrait animation framework that enables real-time, streamable, infinite-length portrait animations on a single 12GB GPU. It balances low latency with high quality, supporting both offline and online inference, and delivers efficient, visually stunning results through innovative technical designs. What is PersonaLive? In today’s booming short-video social media landscape, live streamers and content creators have an urgent demand for high-quality portrait animation technology. Enter PersonaLive—a groundbreaking framework developed collaboratively by the University of Macau, Dzine.ai, and the GVC Lab at Great Bay University. Simply put, PersonaLive …
InfinityStar: Revolutionizing Video Generation with Unified Spacetime Autoregressive Modeling
8 days ago 高效码农InfinityStar: Unified Spacetime Autoregressive Modeling for Visual Generation Introduction: What is InfinityStar and How Does It Address Challenges in Visual Generation? This article aims to answer the core question: What is InfinityStar, how does it unify image and video generation tasks, and why does it improve efficiency and quality? InfinityStar is a unified spacetime autoregressive framework designed for high-resolution image and dynamic video synthesis. It leverages recent advances in autoregressive modeling from both vision and language domains, using a purely discrete approach to jointly capture spatial and temporal dependencies in a single architecture. Visual synthesis has seen remarkable advancements in …
OneStory: How Adaptive Memory Solves Multi-Shot Video Generation’s Biggest Challenge
11 days ago 高效码农OneStory: Redefining Multi-Shot Video Generation with Adaptive Memory Abstract OneStory addresses the critical challenge of maintaining narrative coherence across discontinuous video shots by introducing an adaptive memory system. This framework achieves a 58.74% improvement in character consistency and supports minute-scale video generation through next-shot prediction and dynamic context compression. By reformulating multi-shot generation as an autoregressive task, it bridges the gap between single-scene video models and complex storytelling requirements. What is Multi-Shot Video Generation? Imagine watching a movie where scenes seamlessly transition between different locations and characters. Traditional AI video generators struggle with this “multi-shot” structure—sequences of non-contiguous clips that …
LivingSwap: The Breakthrough in Cinematic Video Face Swapping Using Source Video Reference
11 days ago 高效码农Title: High-Fidelity Face Swapping for Cinematic Quality: When AI Learns to “Reference” the Source Video Snippet: LivingSwap is the first video face-swapping model to use the source video itself as a pixel-level reference. By combining keyframe-guided identity injection with a novel reference-guided generation architecture, it achieves unprecedented temporal consistency and attribute fidelity in long, complex video sequences, reducing manual editing effort by up to 40x for film production. Imagine this scenario: an actor becomes unavailable to complete filming, or a director wants to recast a role in post-production. Traditionally, this meant costly reshoots or painstaking, frame-by-frame manual editing prone to …
PaCo-RL: How This Breakthrough Solves AI Image Consistency with Reinforcement Learning
13 days ago 高效码农PaCo-RL: A Breakthrough in Consistent Image Generation Using Reinforcement Learning Introduction Have you ever tried using AI to generate a series of coherent images—for creating story characters or designing multiple advertisement visuals—only to find the results inconsistent in style, identity, or logical flow? Consistent image generation remains a fundamental challenge in AI content creation, requiring models to maintain shared elements like character appearance, artistic style, or scene continuity across multiple images. In this comprehensive guide, we explore PaCo-RL (Pairwise Consistency Reinforcement Learning), an innovative framework that addresses these challenges through specialized reward modeling and efficient reinforcement learning. Whether you’re a …
Live Avatar AI: How We Reached 20 FPS Real-Time Streaming with a 14B-Parameter Model
14 days ago 高效码农LiveAvatar under the hood: how a 14-billion-parameter diffusion model now runs live, lip-synced avatars at 20 FPS on five GPUs A plain-language walk-through of the paper, code and benchmarks—no hype, no hidden plugs. “We want an avatar that can talk forever, look like the reference photo, and run in real time.” —Authors’ opening line, arXiv:2512.04677 1. The problem in one sentence Big diffusion models give great faces, but they are slow (0.25 FPS) and drift out of look after a few hundred frames. LiveAvatar keeps the quality, removes the lag, and stops the drift—so you can stream an avatar for …
Gemini 3 Pro: How Google’s Vision AI Achieves True Visual Reasoning
16 days ago 高效码农Gemini 3 Pro: The Frontier of Vision AI – From Recognition to True Reasoning Core Question: What fundamental leaps does Google’s latest Gemini 3 Pro model deliver, and how does it move beyond traditional image recognition to solve real-world problems through genuine visual and spatial reasoning? In late 2025, Google DeepMind introduced its most capable multimodal model to date: Gemini 3 Pro. This is far more than a routine version update. It marks a paradigm shift for artificial intelligence in processing visual information, evolving from passive “recognition” to active “understanding” and “reasoning.” Whether it’s chaotic historical documents, dynamic and complex …
Video Difference Captioning: The Ultimate Guide to Dynamic Scene Analysis
17 days ago 高效码农Video Difference Captioning: Exploring Similarities and Differences in Dynamic Scenes This article addresses the core question: What is the Video Difference Captioning task, and how does it enhance our understanding of video editing and multimodal model capabilities? Video Difference Captioning (ViDiC) is a task where models generate natural language descriptions that precisely capture both static visual elements and temporal dynamics between two video clips, ensuring coherence and factual accuracy. It extends image difference captioning into the video realm, emphasizing motion, event progression, and stylistic shifts. Introduction: The Importance of Understanding Video Differences This section answers the core question: Why is …
OneThinker AI Model: The First Unified System for Image and Video Understanding
17 days ago 高效码农OneThinker: One Model to Understand Both Images and Videos Have you ever imagined an AI “polymath” capable of solving complex diagram-based math problems, precisely tracking objects in a video, and segmenting them—all within a single system? Traditionally, this required separate specialized models for tasks like visual question answering, video analysis, and object localization. This paradigm is now being reshaped by a unified generalist. Today, we delve into OneThinker—a multimodal reasoning model designed to unify image and video understanding. Within a single framework, it masters ten fundamental visual tasks, including question answering, captioning, grounding, tracking, and segmentation, marking a significant step …
Crisp Text-to-Image Generation: How Ovis-Image 7B Delivers 20B-Level Performance on One GPU
18 days ago 高效码农Ovis-Image: A 7-Billion-Parameter Text-to-Image Model That Punches at 20-Billion Scale—While Running on One GPU “ What makes a compact 7 B model able to render crisp, bilingual, layout-heavy text previously dominated by 20 B+ giants, and how can you deploy it today? TL;DR (the 30-second take) Architecture: 2 B multimodal Ovis 2.5 encoder frozen for alignment, 7 B MMDiT diffusion decoder trained from scratch, FLUX.1-schnell VAE stays frozen—10 B total, <24 GB VRAM. Training: four-stage pipeline (pre-train → instruction fine-tune → DPO preference → GRPO text-specialist) steadily improves word accuracy from 87 % → 92 %. Benchmarks: leads CVTG-2K English …
ViBT Image Generation: How Brownian Bridge Models Achieve 4× Faster AI Inference
20 days ago 高效码农ViBT: Vision Bridge Transformer at Scale – A Practical Deep Dive What is ViBT and why does it achieve up to 4× faster inference than token-heavy conditional diffusion models while maintaining comparable quality? ViBT is the first large-scale realization of Brownian Bridge generative models for vision tasks. Instead of the classic “noise-to-data” paradigm, it directly learns stochastic trajectories from a structured source (image/video) to a structured target, eliminating most conditioning tokens and dramatically reducing compute. Figure: Example results of ViBT across instruction-based editing, stylization, colorization, and frame interpolation. Why the Noise-to-Data Paradigm Feels Wrong for Conditional Generation Most modern image …
STARFlow-V: Inside Apple’s First Normalizing-Flow Video Generator You Can Actually Run
20 days ago 高效码农STARFlow-V: Inside Apple’s First Normalizing-Flow Video Generator That You Can Actually Run Today What is STARFlow-V in one sentence? It is a fully open-source, causal, normalizing-flow video model that produces 480p clips with a single forward pass—no diffusion schedule, no vector-quantization, just an invertible Transformer mapping noise to video. What exact question will this article answer? “How does STARFlow-V work, how good is it, and how do I reproduce the results on my own GPU cluster?” 1. Why Another Video Model? (The Motivation in Plain Words) Apple’s team asked a simple question: “Can we avoid the multi-step denoising circus and …
ReasonEdit: How AI Image Editing Learned to Think and Reflect Like Humans
21 days ago 高效码农ReasonEdit: How AI Image Editing Learned to Think and Reflect Image editing technology has evolved dramatically from early mask-based tools to sophisticated AI systems that understand natural language instructions. Yet even advanced models struggle when faced with abstract commands like “make this leaf show potassium deficiency symptoms” or “apply desertification control measures.” ReasonEdit introduces a breakthrough approach that enables AI to think through complex instructions and reflect on its own results—mimicking human cognitive processes to achieve unprecedented editing precision. The Core Challenge in AI Image Editing Modern image editing models typically combine a multimodal large language model (MLLM) encoder with …
Teaching Machines to Pause and Zoom: How Video-R4 Solves Text-Rich Video QA
21 days ago 高效码农Video-R4: Teaching Machines to Pause, Zoom and Re-read Text-Rich Videos “Why do most video-QA models hallucinate small, fleeting text? Because they never get a second look. Video-R4 fixes this by adding an explicit ‘visual rumination’ loop—select, zoom, re-encode, repeat—boosting M4-ViteVQA accuracy from 26 % to 64 % without extra data or a larger backbone.” What problem is this article solving? How to reliably answer questions that depend on tiny, transient text in the wild—news tickers, lecture slides, UI walk-throughs—when single-pass models routinely overlook or mis-read it. The single-pass ceiling: five pain-points in one shot Fixed frame budget → text appears …
Texo: The Ultimate Lightweight LaTeX OCR for Math Formula Recognition
21 days ago 高效码农Texo: A Lightweight, Open-Source LaTeX OCR Model for Effortless Math Formula Recognition Have you ever encountered a complex mathematical formula in a document or image and wished you could instantly convert it into editable LaTeX code? As students, researchers, or STEM professionals, we often need to extract mathematical expressions from images or handwritten notes. This is where LaTeX OCR (Optical Character Recognition) tools become invaluable. Today, we introduce Texo – a free, open-source, lightweight, yet powerful LaTeX OCR model. With only 20 million parameters, it efficiently handles formula recognition across various scenarios. What is Texo and Why Should You Care? …
Adv-GRPO: How Adversarial Reinforcement Learning Revolutionizes AI Image Generation
22 days ago 高效码农The Image as Its Own Reward: How Adversarial Reinforcement Learning Finally Fixes AI Image Generation What if the biggest problem in AI image generation isn’t the model’s ability, but how we tell it what “good” means? For years, researchers have struggled with a fundamental misalignment in reinforcement learning for text-to-image models: our reward functions keep teaching models to game the system rather than create genuinely better images. This article explores Adv-GRPO, a framework that treats images as their own reward source, eliminating reward hacking while delivering measurable improvements in quality, aesthetics, and text alignment. Why Do Existing RL Methods for …
Qwen3-VL: How a 256K-Token Vision Model Masters 500-Page Documents
24 days ago 高效码农Inside Qwen3-VL: How a 256K-Token Vision-Language Model Learns to Read 500-Page Documents and 2-Hour Videos Without Breaking a Sweat A plain-language walk-through of the technical report that introduced Qwen3-VL—no hype, no jargon, and no external facts beyond the original paper. Table of Contents The 30-Second Takeaway Model Family at a Glance Three Architectural Tweaks That Actually Matter Four-Stage Training From Scratch What the Model Was Fed (Data Ingredients) Post-Training: SFT, Distillation, and Reinforcement Learning “Thinking Mode” Explained Benchmark Scores in One Sitting Hardware-Friendly Deployment Answers to the Most-Asked Questions Key Limits and Next Steps 1. The 30-Second Takeaway Qwen3-VL is …
Inferix World Simulation: How The New Block-Diffusion Engine Enables Real-Time AI Video Worlds
24 days ago 高效码农Mind-Blowing: A Chinese Mega-Team Just Dropped Inferix — The Inference Engine That Turns “World Simulation” From Sci-Fi Into Reality You thought 2025 was already wild? Hold my coffee. On November 24, 2025, a joint force from Zhejiang University, HKUST, Alibaba DAMO Academy, and Alibaba TRE quietly released something that will be remembered as the real turning point of AI video: 「Inferix」. It’s not another video generation model. It’s the dedicated inference engine for the next era — the 「World Model era」. In plain English: 「Inferix lets normal GPUs run minute-long, physics-accurate, fully interactive, never-collapsing open-world videos — in real time.」 …