Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Video Analysis and Generation via a Semantic Progress Function paper:

_akhaliq's profile picture

AK

488,872 subscribers

34,219 görüntüleme • 1 ay önce •via X (Twitter)

EğitimHaberler & PolitikaBilim & Teknoloji
LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

Meta announces ControlRoom3D Room Generation using Semantic Proxy Rooms paper page: ControlRoom3D creates diverse and plausible 3D room meshes aligning well with user-defined room layouts and textual descriptions of the room style.
0:34

Meta announces ControlRoom3D Room Generation using Semantic Proxy Rooms paper page: ControlRoom3D creates diverse and plausible 3D room meshes aligning well with user-defined room layouts and textual descriptions of the room style.

AK

120,485 görüntüleme • 2 yıl önce

T-Display K230 LoRa video transmission function progress update.
0:28

T-Display K230 LoRa video transmission function progress update.

LILYGO

39,068 görüntüleme • 7 ay 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

20,312 görüntüleme • 3 ay önce

AnchorWeave World-Consistent Video Generation with Retrieved Local Spatial Memories paper:
0:24

AnchorWeave World-Consistent Video Generation with Retrieved Local Spatial Memories paper:

AK

23,256 görüntüleme • 3 ay önce

HandX Scaling Bimanual Motion and Interaction Generation paper:
0:24

HandX Scaling Bimanual Motion and Interaction Generation paper:

AK

10,562 görüntüleme • 2 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

77,375 görüntüleme • 1 yıl önce

FlowRVS - segmentation as a continuous deformation, mapping video latents directly to masks via an ODE. Built on Wan’s T2V. - complex semantic understanding with temporal consistency. - no flickering
0:28

FlowRVS - segmentation as a continuous deformation, mapping video latents directly to masks via an ODE. Built on Wan’s T2V. - complex semantic understanding with temporal consistency. - no flickering

Wildminder

26,166 görüntüleme • 3 ay önce

Collaborative Video Diffusion Consistent Multi-video Generation with Camera Control Research on video generation has recently made tremendous progress, enabling high-quality videos to be generated from text prompts or images. Adding control to the video generation
2:16

Collaborative Video Diffusion Consistent Multi-video Generation with Camera Control Research on video generation has recently made tremendous progress, enabling high-quality videos to be generated from text prompts or images. Adding control to the video generation

AK

29,262 görüntüleme • 2 yıl önce

Inspired by Hugging Face's official MCP server, I built my own to expose my semantic search API for the HF ecosystem! Features AI-powered search, parameter analysis via safetensors, and tools to find similar models/datasets. Try: "Find non maths reasoning datasets from 2025"!
0:23

Inspired by Hugging Face's official MCP server, I built my own to expose my semantic search API for the HF ecosystem! Features AI-powered search, parameter analysis via safetensors, and tools to find similar models/datasets. Try: "Find non maths reasoning datasets from 2025"!

Daniel van Strien

15,147 görüntüleme • 1 yıl önce

Make Pixels Dance: High-Dynamic Video Generation paper page: Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence. Unfortunately, current state-of-the-art video generation methods, primarily focusing on text-to-video generation, tend to produce video clips with minimal motions despite maintaining high fidelity. We argue that relying solely on text instructions is insufficient and suboptimal for video generation. In this paper, we introduce PixelDance, a novel approach based on diffusion models that incorporates image instructions for both the first and last frames in conjunction with text instructions for video generation. Comprehensive experimental results demonstrate that PixelDance trained with public data exhibits significantly better proficiency in synthesizing videos with complex scenes and intricate motions, setting a new standard for video generation.
3:09

Make Pixels Dance: High-Dynamic Video Generation paper page: Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence. Unfortunately, current state-of-the-art video generation methods, primarily focusing on text-to-video generation, tend to produce video clips with minimal motions despite maintaining high fidelity. We argue that relying solely on text instructions is insufficient and suboptimal for video generation. In this paper, we introduce PixelDance, a novel approach based on diffusion models that incorporates image instructions for both the first and last frames in conjunction with text instructions for video generation. Comprehensive experimental results demonstrate that PixelDance trained with public data exhibits significantly better proficiency in synthesizing videos with complex scenes and intricate motions, setting a new standard for video generation.

AK

101,514 görüntüleme • 2 yıl önce

Another video of Hudson Williams making a heart hand, throwing paper airplanes with autographs and messages (and a clementine) to fans earlier in Paris via sofianealarcon on IG
0:42

Another video of Hudson Williams making a heart hand, throwing paper airplanes with autographs and messages (and a clementine) to fans earlier in Paris via sofianealarcon on IG

Hudson Williams Photos – fanpage

15,058 görüntüleme • 2 ay önce

MatAnyone 2 is out on Hugging Face Scaling Video Matting via a Learned Quality Evaluator paper: app:
0:40

MatAnyone 2 is out on Hugging Face Scaling Video Matting via a Learned Quality Evaluator paper: app:

AK

34,753 görüntüleme • 2 ay önce

Wan-Move Motion-controllable Video Generation via Latent Trajectory Guidance
2:00

Wan-Move Motion-controllable Video Generation via Latent Trajectory Guidance

AK

14,853 görüntüleme • 5 ay önce

Procedural Generation Progress
0:28

Procedural Generation Progress

guilherme #AVOIDONSWEEP

10,823 görüntüleme • 7 gün önce

EchoX Towards Mitigating Acoustic-Semantic Gap via Echo Training for Speech-to-Speech LLMs
1:30

EchoX Towards Mitigating Acoustic-Semantic Gap via Echo Training for Speech-to-Speech LLMs

AK

10,452 görüntüleme • 8 ay önce

ByteDance released BindWeave Subject-Consistent Video Generation via Cross-Modal Integration
0:50

ByteDance released BindWeave Subject-Consistent Video Generation via Cross-Modal Integration

AK

37,173 görüntüleme • 7 ay önce

Meta announces Movie Gen A Cast of Media Foundation Models We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabilities such as precise instruction-based video editing and generation of personalized videos based on a user’s image. Our models set a new state-of-the-art on multiple tasks: text-to-video synthesis, video personalization, video editing, video-to-audio generation, and text-to-audio generation. Our largest video generation model is a 30B parameter transformer trained with a maximum context length of 73K video tokens, corresponding to a generated video of 16 seconds at 16 frames-per-second. We show multiple technical innovations and simplifications on the architecture, latent spaces, training objectives and recipes, data curation, evaluation protocols, parallelization techniques, and inference optimizations that allow us to reap the benefits of scaling pre-training data, model size, and training compute for training large scale media generation models. We hope this paper helps the research community to accelerate progress and innovation in media generation models
0:30

Meta announces Movie Gen A Cast of Media Foundation Models We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabilities such as precise instruction-based video editing and generation of personalized videos based on a user’s image. Our models set a new state-of-the-art on multiple tasks: text-to-video synthesis, video personalization, video editing, video-to-audio generation, and text-to-audio generation. Our largest video generation model is a 30B parameter transformer trained with a maximum context length of 73K video tokens, corresponding to a generated video of 16 seconds at 16 frames-per-second. We show multiple technical innovations and simplifications on the architecture, latent spaces, training objectives and recipes, data curation, evaluation protocols, parallelization techniques, and inference optimizations that allow us to reap the benefits of scaling pre-training data, model size, and training compute for training large scale media generation models. We hope this paper helps the research community to accelerate progress and innovation in media generation models

AK

62,702 görüntüleme • 1 yıl önce

ESPN does a great game breakdown analysis of SGA foul hunting in WCF Game 6 and contrasts it to Spurs guards playing through contact "Every play is a negotiation with the refs" Via NBA on ESPN Video via:
2:27

ESPN does a great game breakdown analysis of SGA foul hunting in WCF Game 6 and contrasts it to Spurs guards playing through contact "Every play is a negotiation with the refs" Via NBA on ESPN Video via:

MrBuckBuck

132,160 görüntüleme • 5 gün önce

Grok Imagine Video is now dominating the Artificial Analysis Arena #1 in both Text→Video and Image→Video Outranking Veo 3.1, Sora 2 and Kling 2.6 Pro in every way - Best overall quality - Fastest generation times - Lowest cost-to-performance ratio - Native audio-video support xAI is leading the next wave of AI video generation
0:30

Grok Imagine Video is now dominating the Artificial Analysis Arena #1 in both Text→Video and Image→Video Outranking Veo 3.1, Sora 2 and Kling 2.6 Pro in every way - Best overall quality - Fastest generation times - Lowest cost-to-performance ratio - Native audio-video support xAI is leading the next wave of AI video generation

X Freeze

16,972 görüntüleme • 3 ay önce