Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

🔥1-min Interactive Video Generation with Multimodal Control🔥 Towards *long-context world model*, #LongVie is an end-to-end autoregressive framework for controllable ultra-long video generation - Page: - Paper: . Thanks AK

liuziwei7's profile picture

Ziwei Liu

13,421 subscribers

14,213 görüntüleme • 1 yıl önce •via X (Twitter)

#LongVie
LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

🔥Ultra-Long Video World Model up to 5min🔥 ✨ We introduce #LongVie2, an end-to-end autoregressive video world model that supports continuous video generation lasting up to 5min with: 🕹️ Strong Controllability 📷 Long-term Visual Fidelity 🔒 Temporal Consistency - Project: - Code: - Paper: . Thanks to AK !
0:29

🔥Ultra-Long Video World Model up to 5min🔥 ✨ We introduce #LongVie2, an end-to-end autoregressive video world model that supports continuous video generation lasting up to 5min with: 🕹️ Strong Controllability 📷 Long-term Visual Fidelity 🔒 Temporal Consistency - Project: - Code: - Paper: . Thanks to AK !

Ziwei Liu

82,798 görüntüleme • 10 ay önce

Helios Real Real-Time Long Video Generation Model paper:
1:13

Helios Real Real-Time Long Video Generation Model paper:

AK

25,951 görüntüleme • 8 ay önce

Infinity-RoPE Action-Controllable Infinite Video Generation Emerges From Autoregressive Self-Rollout
1:00

Infinity-RoPE Action-Controllable Infinite Video Generation Emerges From Autoregressive Self-Rollout

AK

19,588 görüntüleme • 11 ay önce

CineMaster A 3D-Aware and Controllable Framework for Cinematic Text-to-Video Generation
1:43

CineMaster A 3D-Aware and Controllable Framework for Cinematic Text-to-Video Generation

AK

18,843 görüntüleme • 1 yıl önce

Introducing 🔥BLIP-Diffusion🔥, a novel method for enabling Text-to-image Diffusion models with multimodal controllable generation/editing, powered by BLIP-2 pre-trained text-aligned subject representation. Paper: Project: (1/n)
2:03

Introducing 🔥BLIP-Diffusion🔥, a novel method for enabling Text-to-image Diffusion models with multimodal controllable generation/editing, powered by BLIP-2 pre-trained text-aligned subject representation. Paper: Project: (1/n)

Steven Hoi

40,615 görüntüleme • 3 yıl önce

LongLive Real-time Interactive Long Video Generation
0:59

LongLive Real-time Interactive Long Video Generation

AK

17,214 görüntüleme • 1 yıl önce

Hy-Nomas Towards Free-form Interactive World Generation paper: app:
2:21

Hy-Nomas Towards Free-form Interactive World Generation paper: app:

AK

17,653 görüntüleme • 5 gün önce

🚀 We open-sourced LongLive — interactive, real-time long-video generation. 👥Generates video in real time as users enter text prompts. ⚡️20.7 FPS on a single H100,⏱️up to 240s per clip. 🎬Fine-tunes SOTA short-video models (e.g., Wan) into long-video generators. 🌍One step closer to World Models. All code for training & inference, model weights, demo page, and videos released! Paper: Code: Model: Demo Page: Introduction Video:
3:18

🚀 We open-sourced LongLive — interactive, real-time long-video generation. 👥Generates video in real time as users enter text prompts. ⚡️20.7 FPS on a single H100,⏱️up to 240s per clip. 🎬Fine-tunes SOTA short-video models (e.g., Wan) into long-video generators. 🌍One step closer to World Models. All code for training & inference, model weights, demo page, and videos released! Paper: Code: Model: Demo Page: Introduction Video:

Yukang Chen

11,943 görüntüleme • 1 yıl önce

We propose Long Context Tuning (LCT) for scene-level video generation to bridge the gap between current single-shot generation and real-world narrative video productions. Homepage: Report:
4:29

We propose Long Context Tuning (LCT) for scene-level video generation to bridge the gap between current single-shot generation and real-world narrative video productions. Homepage: Report:

Ceyuan Yang

46,908 görüntüleme • 1 yıl önce

Looking Ahead Modeling Future Evolution in Autoregressive Video Generation paper:
0:44

Looking Ahead Modeling Future Evolution in Autoregressive Video Generation paper:

AK

19,481 görüntüleme • 11 gün önce

GMI Studio is officially here. We’ve moved beyond multimodal hubs to create a dedicated workflow engine for high-end AI creation. - Long-form video (2 min+). - 1 image, 10 video styles, cinematic output. - 1.4x faster generation speed, powered by GMI inference. - Social feed: Discover and like. The beta is over. The studio is open.
1:10

GMI Studio is officially here. We’ve moved beyond multimodal hubs to create a dedicated workflow engine for high-end AI creation. - Long-form video (2 min+). - 1 image, 10 video styles, cinematic output. - 1.4x faster generation speed, powered by GMI inference. - Social feed: Discover and like. The beta is over. The studio is open.

GMI Cloud

971,337 görüntüleme • 9 ay önce

Now autoregressive video models can remember. 🧠 Introducing MosaiChunk: persistent visual memory for long-horizon autoregressive video generation🧩. Instead of remembering everything, it remembers the right things. ✍️ 1/6
0:50

Now autoregressive video models can remember. 🧠 Introducing MosaiChunk: persistent visual memory for long-horizon autoregressive video generation🧩. Instead of remembering everything, it remembers the right things. ✍️ 1/6

Haven Feng

31,435 görüntüleme • 21 gün önce

Mode Seeking meets Mean Seeking for Fast Long Video Generation paper:
0:30

Mode Seeking meets Mean Seeking for Fast Long Video Generation paper:

AK

20,433 görüntüleme • 8 ay önce

Yume An Interactive World Generation Model
2:34

Yume An Interactive World Generation Model

AK

17,570 görüntüleme • 1 yıl önce

PixVerse just launched their first real-time video world model R1, powered by fal's high-performance compute. It turns AI video from static clips into a real-time, interactive world. 🔄 Continuous generation stream 🧠 Autoregressive memory system ⚡ Ultra-fast response engine
0:27

PixVerse just launched their first real-time video world model R1, powered by fal's high-performance compute. It turns AI video from static clips into a real-time, interactive world. 🔄 Continuous generation stream 🧠 Autoregressive memory system ⚡ Ultra-fast response engine

fal

19,243 görüntüleme • 9 ay önce

Remade donald' "upping my p(doom)" as a paper music video, end-to-end with Opus 5.5. 6M tokens + ~$65 of image/video generation 👇
2:21

Remade donald' "upping my p(doom)" as a paper music video, end-to-end with Opus 5.5. 6M tokens + ~$65 of image/video generation 👇

makevoid

119,351 görüntüleme • 1 ay önce

MuseSteamer just leveled up! Our video generation model now supports real-time interactive long-form video generation. It breaks the traditional 10-second limit, creating videos of any length with greater speed and control—enabling users to pause, rewrite storylines, or extend transitions any time during the process.
0:22

MuseSteamer just leveled up! Our video generation model now supports real-time interactive long-form video generation. It breaks the traditional 10-second limit, creating videos of any length with greater speed and control—enabling users to pause, rewrite storylines, or extend transitions any time during the process.

Baidu Inc.

142,732 görüntüleme • 1 yıl önce

🚀 Excited to release LongLive 2.0! 🎬 An end-to-end infrastructure for long video generation, with FP4 and parallelism at the core of both training and inference. ⚡45.7 FPS generation speed on 5B model⚡ ✨ LongLive 2.0 supports real-video training, few-step distillation, multi-shot training/inference, sequence-parallel acceleration, NVFP4 KV cache, and async VAE decoding deployment. 🧩 To our knowledge, this is the first open-source 4-bit long video generation infra that covers both training and inference. 🙌 Welcome to check it out, try it, and share feedback! 🔗 Code: 📰 Paper: 🎥 Demo: #LongVideoGeneration #VideoGeneration #Realtime #AIInfra #EfficientAI #FP4 #Parallel #NVIDIA
1:31

🚀 Excited to release LongLive 2.0! 🎬 An end-to-end infrastructure for long video generation, with FP4 and parallelism at the core of both training and inference. ⚡45.7 FPS generation speed on 5B model⚡ ✨ LongLive 2.0 supports real-video training, few-step distillation, multi-shot training/inference, sequence-parallel acceleration, NVFP4 KV cache, and async VAE decoding deployment. 🧩 To our knowledge, this is the first open-source 4-bit long video generation infra that covers both training and inference. 🙌 Welcome to check it out, try it, and share feedback! 🔗 Code: 📰 Paper: 🎥 Demo: #LongVideoGeneration #VideoGeneration #Realtime #AIInfra #EfficientAI #FP4 #Parallel #NVIDIA

Yukang Chen

60,351 görüntüleme • 5 ay önce

Glad to share Seaweed-7B, a cost-effective foundation model for video generation. Our tech report highlights the key designs that significantly improve compute efficiency and performance given limited resources, achieving comparable quality against other industry-level models. To unleash the power of the foundation model, Seaweed-7B further enables a wide range of downstream applications including image-to-video generation, human video generation, subject-consistent video generation, video-audio joint generation, long video generation and storytelling, real-time generation, super-resolution generation, camera controlled generation. Check out our webpage and report for more details: Webpage: Paper: It's a wonderful journey of the last year. Thanks to all teammates for their contributions, sincerely.
1:30

Glad to share Seaweed-7B, a cost-effective foundation model for video generation. Our tech report highlights the key designs that significantly improve compute efficiency and performance given limited resources, achieving comparable quality against other industry-level models. To unleash the power of the foundation model, Seaweed-7B further enables a wide range of downstream applications including image-to-video generation, human video generation, subject-consistent video generation, video-audio joint generation, long video generation and storytelling, real-time generation, super-resolution generation, camera controlled generation. Check out our webpage and report for more details: Webpage: Paper: It's a wonderful journey of the last year. Thanks to all teammates for their contributions, sincerely.

Ceyuan Yang

77,594 görüntüleme • 1 yıl önce