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/1Konny/HierarchicalVideoPrediction
공식 코드Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction
https://github.com/AIS-Bonn/FreqNet
공식 코드https://github.com/AIS-Bonn/LocDepVideoPrediction
공식 코드https://github.com/AIS-Bonn/Local_Freq_Transformer_Net
공식 코드Local Frequency Domain Transformer Networks for Video Prediction
https://github.com/AIS-Bonn/MSPred
공식 코드MSPred: Video Prediction at Multiple Spatio-Temporal Scales with Hierarchical Recurrent Networks
https://github.com/AIS-Bonn/MotionSegmentation
공식 코드Motion Segmentation using Frequency Domain Transformer Networks
https://github.com/AIS-Bonn/OCVP-object-centric-video-prediction
공식 코드Object-Centric Video Prediction via Decoupling of Object Dynamics and Interactions
https://github.com/Bei-Jin/STMFANet
공식 코드https://github.com/HuQyang/learning-direction
공식 코드https://github.com/Kay1794/Motion-Graph-Video-Prediction
공식 코드Motion Graph Unleashed: A Novel Approach to Video Prediction
https://github.com/NVIDIA/semantic-segmentation
공식 코드SDCNet: Video Prediction Using Spatially-Displaced Convolution
https://github.com/NVIDIA/vid2vid
공식 코드https://github.com/NVlabs/conv-tt-lstm
공식 코드Convolutional Tensor-Train LSTM for Spatio-temporal Learning
https://github.com/NagabhushanSN95/DeCOMPnet
공식 코드https://github.com/NagabhushanSN95/PVQA
공식 코드https://github.com/NellyElsayed/rgcLSTM
공식 코드Reduced-Gate Convolutional LSTM Using Predictive Coding for Spatiotemporal Prediction
https://github.com/Pachark/SVQ-Forecasting
공식 코드https://github.com/PallottaEnrico/SyncVP
공식 코드SyncVP: Joint Diffusion for Synchronous Multi-Modal Video Prediction
https://github.com/SUTDBrainLab/MGP-VAE
공식 코드https://github.com/SongTang-x/SwinLSTM
공식 코드SwinLSTM:Improving Spatiotemporal Prediction Accuracy using Swin Transformer and LSTM
https://github.com/TUM-LMF/FutureGAN
공식 코드https://github.com/Tobi-r9/RaMViD
공식 코드https://github.com/Westlake-AI/MogaNet
공식 코드https://github.com/Westlake-AI/openmixup
공식 코드https://github.com/XiYe20/NPVP
공식 코드https://github.com/YueWuHKUST/CVPR2022-Optimizing-Video-Prediction-via-Video-Frame-Interpolation
공식 코드https://github.com/YueWuHKUST/FutureVideoSynthesis
공식 코드https://github.com/Yunbo426/MIM
공식 코드https://github.com/Yunbo426/predrnn-pp
공식 코드PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
https://github.com/ZdenekStraka/precnet
공식 코드PreCNet: Next-Frame Video Prediction Based on Predictive Coding
https://github.com/ais-bonn/pred_semantic
공식 코드Semantic Prediction: Which One Should Come First, Recognition or Prediction?
https://github.com/alexlee-gk/video_prediction
공식 코드https://github.com/amazon-science/earth-forecasting-transformer
공식 코드Earthformer: Exploring Space-Time Transformers for Earth System Forecasting
https://github.com/andrewjywang/SEENet
공식 코드Order Matters: Shuffling Sequence Generation for Video Prediction
https://github.com/anonymous202203/moso
공식 코드MOSO: Decomposing MOtion, Scene and Object for Video Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.