Video Prediction
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Script for Amee Marketing & Trading Company Short Video
(Duration: 45-60 seconds)
Opening Scene (0:00-0:05):
- Visual: Close-up of fresh organic grains spilling gently into a wooden bowl. Sunlight filters through lush green fields.
- Text Overlay: "Nourishing Lives, Naturally."
- Music: Uplifting acoustic melody with a traditional touch.
Scene 1: Organic & Natural Offerings (0:05-0:15):
- Visual: Rapid montage of vibrant vegetables, ripe fruits, aromatic spices, Ayurvedic herbs, and fresh dairy products.
- Voiceover: "At Amee Marketing & Trading, we bring you the purest Vedic organic foods—grains, spices, herbs, and dairy—straight from nature’s bounty."
Scene 2: Engineering & Innovation (0:15-0:25):
- Visual: Split-screen transition:
- Left: Engineers working on agriculture machinery and water treatment systems.
- Right: Automation controls, aquaculture systems, and textile equipment in action.
- Voiceover: "Pioneering sustainable solutions—agriculture engineering, water and wastewater treatment, automation, and industrial innovations."
Scene 3: Waste & Resource Management (0:25-0:35):
- Visual: Waste management systems transforming waste into resources, followed by Roadtech equipment paving roads.
- Text Overlay: "Building a greener future."
- Voiceover: "From waste management to infrastructure, we engineer tomorrow’s world today."
Scene 4: Services & Global Reach (0:35-0:45):
- Visual: Factory assembly line, team meeting, and global map with location pins.
- Voiceover: "As manufacturers, traders, and suppliers, we bridge quality and trust worldwide."
Closing Scene (0:45-0:55):
- Visual: Amee logo fades in over a backdrop of their factory. Contact details appear.
- Text Overlay: "Connect with Us!"
- Phone: +91-8300874712
- Website: www.ameemarketingtredingcompany.in
- Location: Home & Factory (add brief map graphic).
- Voiceover: "Your partner in purity and progress. Contact Amee today!"
End Frame (0:55-1:00):
- Visual: Sunrise over fields with the tagline: "Amee Marketing & Trading – Where Tradition Meets Technology."
Production Notes:
- Music: Blend traditional Indian instruments with modern beats for cross-sector appeal.
- Color Palette: Earthy tones (greens, browns) for organic segments; metallic blues/greys for tech sections.
- Pacing: Quick cuts for energy, but hold 2-3 seconds on contact details.
Perfect for social media ads or website headers! 🌱🚀
Gif credit: MAGVIT
Source: Photo-Realistic Video Prediction on Natural Videos of Largely Changing Frames
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/georgeyiasemis/2D-Convolutional-Recurrent-Neural-Networks-with-PyTorch
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
https://github.com/google/e3d_lstm
https://github.com/hywang66/LARP
LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior
https://github.com/igloooo/weather-forecasting-video-prediction
https://github.com/jayleicn/singularity
The "something something" video database for learning and evaluating visual common sense
https://github.com/jerrywn121/TianChi_AIEarth
Convolutional Tensor-Train LSTM for Spatio-temporal Learning
https://github.com/jhhuang96/convlstm-pytorch
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
https://github.com/jhon-zelada/proyect
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
https://github.com/jinbeibei/VarNet
https://github.com/joelouismarino/amortized-variational-filtering
Self-Supervised Visual Planning with Temporal Skip Connections
https://github.com/juliuskunze/cwvae-jax
https://github.com/jy0205/Pyramid-Flow
Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
https://github.com/kamran0153/impact-of-data-freshness-in-learning
Self-Supervised Visual Planning with Temporal Skip Connections
https://github.com/kn-lambda/Pred-Net
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
https://github.com/ksm26/spatiotemporal-predictions
PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning
https://github.com/kunimasa-kawasaki/chainer-prednet
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
https://github.com/kunimasa-kawasaki/keras-prednet
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
https://github.com/kyafuk/3d-mim
https://github.com/latte488/smth-smth-v2
The "something something" video database for learning and evaluating visual common sense
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/moganet
https://github.com/liuem607/DYAN
https://github.com/liuziwei7/voxel-flow
https://github.com/lucidrains/ema-pytorch
https://github.com/lucidrains/magvit2-pytorch
Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
https://github.com/lucidrains/make-a-video-pytorch
https://github.com/lucidrains/nuwa-pytorch
NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion
https://github.com/lucidrains/phenaki-pytorch
Phenaki: Variable Length Video Generation From Open Domain Textual Description
https://github.com/lucidrains/recurrent-interface-network-pytorch
https://github.com/lucidrains/video-diffusion-pytorch
https://github.com/lxx1991/pytorch-voxel-flow
https://github.com/marisancans/frame-predict
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
https://github.com/matinhosseiny/Inception-inspired-LSTM-for-Video-frame-Prediction
https://github.com/mattiasxu/Video-VQVAE
https://github.com/metrofun/E3D-LSTM
https://github.com/microsoft/MLOps_VideoAnomalyDetection
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.