SOTAVerified

Video Prediction

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

Papers

Showing 201250 of 394 papers

TitleStatusHype
Taylor saves for later: disentanglement for video prediction using Taylor representation0
Local Frequency Domain Transformer Networks for Video PredictionCode1
Object-centric Video Prediction without AnnotationCode0
Hierarchical Motion Understanding via Motion Programs0
Learning Semantic-Aware Dynamics for Video Prediction0
Comparing Correspondences: Video Prediction with Correspondence-wise LossesCode1
EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction taskCode1
Revisiting Hierarchical Approach for Persistent Long-Term Video PredictionCode1
GATSBI: Generative Agent-centric Spatio-temporal Object InteractionCode1
10,000 km Straight-line Transmission using a Real-time Software-defined GPU-Based Receiver0
Video Prediction Recalling Long-term Motion Context via Memory Alignment LearningCode1
Prediction-assistant Frame Super-Resolution for Video Streaming0
PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive LearningCode1
Self-Supervision by Prediction for Object Discovery in Videos0
Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction0
MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying MotionsCode1
Predicting Video with VQVAECode1
Deep Video Prediction for Time Series Forecasting0
SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving0
Clockwork Variational AutoencodersCode1
CMS-LSTM: Context Embedding and Multi-Scale Spatiotemporal Expression LSTM for Predictive LearningCode0
VAE^2: Preventing Posterior Collapse of Variational Video Predictions in the Wild0
Video Prediction with Variational Temporal Hierarchies0
Spatially Structured Recurrent Modules0
A Log-likelihood Regularized KL Divergence for Video Prediction with A 3D Convolutional Variational Recurrent Network0
EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impactsCode1
Modular Action Concept Grounding in Semantic Video Prediction0
Mutual Information Based Method for Unsupervised Disentanglement of Video RepresentationCode0
Revisiting Adaptive Convolutions for Video Frame Interpolation0
LagNetViP: A Lagrangian Neural Network for Video Prediction0
Spatially Structured Recurrent Modules0
Action Concept Grounding Network for Semantically-Consistent Video Generation0
Learning to Identify Physical Parameters from Video Using Differentiable Physics0
Making a Case for 3D Convolutions for Object Segmentation in VideosCode1
Recurrent Deconvolutional Generative Adversarial Networks with Application to Text Guided Video Generation0
Physics-Informed Deep Neural Networks for Transient Electromagnetic AnalysisCode0
Physics-informed Tensor-train ConvLSTM for Volumetric Velocity Forecasting of Loop Current0
Multi-view Action Recognition using Cross-view Video PredictionCode1
Can Learned Frame-Prediction Compete with Block-Motion Compensation for Video Coding?0
CloudCast: A Satellite-Based Dataset and Baseline for Forecasting CloudsCode0
S2RMs: Spatially Structured Recurrent Modules0
Video Prediction via Example GuidanceCode1
Deep Learning for Vision-based Prediction: A SurveyCode1
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over ModulesCode1
Compositional Video Synthesis with Action GraphsCode1
Latent Video TransformerCode1
Probabilistic Video Prediction From Noisy Data With a Posterior Confidence0
Time Dependence in Non-Autonomous Neural ODEs0
Model Based Reinforcement Learning for Atari0
Efficient and Information-Preserving Future Frame Prediction and BeyondCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Struct-VRNN (from Grid-keypoints)FVD395Unverified
2SV2P time-invariant (from Grid-keypoints)FVD253.5Unverified
3SV2P time-invariant (from Grid-keypoints)FVD209.5Unverified
4SAVP (from Grid-keypoints)FVD183.7Unverified
5SVG-LP (from Grid-keypoints)FVD157.9Unverified
6SAVP-VAE (from Grid-keypoints)FVD145.7Unverified
7Grid-keypointsFVD144.2Unverified
8SVG-LP (from SRVP)Cond10Unverified
9SLAMPCond10Unverified
10SAVP (from SRVP)Cond10Unverified
#ModelMetricClaimedVerifiedStatus
1ConvLSTMMSE103.3Unverified
2PredRNNMSE56.8Unverified
3MIMMSE52Unverified
4PredRNN-V2MSE48.4Unverified
5Causal LSTMMSE46.5Unverified
6MIM*MSE44.2Unverified
7SA-ConvLSTMMSE43.9Unverified
8LMCMSE41.5Unverified
9E3D-LSTMMSE41.3Unverified
10CrevNet+ConvLSTMMSE38.5Unverified
#ModelMetricClaimedVerifiedStatus
1LVTFVD224.73Unverified
2OmniTokenizer-ARFVD32.9Unverified
3RaMViDFVD16.46Unverified
4RIN (400 steps)FVD11.5Unverified
5RIN (1000 steps)FVD10.8Unverified
6LARPFVD5.1Unverified
7DVD-GAN-FPCond5Unverified
8MAGVIT (-L-FP)Cond5Unverified
9MAGVIT (-B-FP)Cond5Unverified
10TriVD-GAN-FPCond5Unverified
#ModelMetricClaimedVerifiedStatus
1IAM4VPSSIM0.94Unverified
2SwinLSTMSSIM0.91Unverified
3FFINetSSIM0.91Unverified
4SimVPSSIM0.9Unverified
5PhyDNetSSIM0.9Unverified
6E3D-LSTMSSIM0.87Unverified
7MIMSSIM0.79Unverified
8PredRNNSSIM0.78Unverified
9FRNNSSIM0.77Unverified
#ModelMetricClaimedVerifiedStatus
1SVG (from Hier-VRNN)FVD1,300.26Unverified
2Hier-VRNNFVD567.51Unverified
3SLAMPCond.10Unverified
4SRVPCond.10Unverified
5GHVAEsCond.2Unverified
#ModelMetricClaimedVerifiedStatus
1SVG-DetLPIPS0.07Unverified
2SVG-LPLPIPS0.07Unverified
3PhyDNetLPIPS0.05Unverified
4PredRNN++LPIPS0.05Unverified
5MSPredLPIPS0.03Unverified
#ModelMetricClaimedVerifiedStatus
1DVGFVD120.03Unverified
2DVD-GAN-FPFVD109.8Unverified
3PhenakiFVD97Unverified
4MMVGFVD85.2Unverified
#ModelMetricClaimedVerifiedStatus
1ODE2VAETest Error10.06Unverified
2ODE2VAE-KLTest Error8.09Unverified
3Latent ODETest Error5.98Unverified
4Latent SDETest Error4.03Unverified
#ModelMetricClaimedVerifiedStatus
1DVFLPIPS0.17Unverified
2FVSLPIPS0.09Unverified
3DMVFNLPIPS0.06Unverified
#ModelMetricClaimedVerifiedStatus
1DVFLPIPS0.32Unverified
2FVSLPIPS0.18Unverified
3DMVFNLPIPS0.11Unverified
#ModelMetricClaimedVerifiedStatus
1DVFLPIPS0.08Unverified
2DMVFNLPIPS0.04Unverified
3OPTLPIPS0.04Unverified
#ModelMetricClaimedVerifiedStatus
1ODE2VAETest Error93.07Unverified
2ODE2VAE-KLTest Error15.99Unverified
#ModelMetricClaimedVerifiedStatus
1DVFLPIPS0.23Unverified
2DMVFNLPIPS0.1Unverified
#ModelMetricClaimedVerifiedStatus
1MGP-VAE (with geodesic loss)MSE4.5Unverified
#ModelMetricClaimedVerifiedStatus
1SRVPFVD222Unverified
#ModelMetricClaimedVerifiedStatus
1MCnet [villegas2017mcnet]LPIPS0.22Unverified
#ModelMetricClaimedVerifiedStatus
1MGP-VAE (with geodesic loss)MSE61.6Unverified
#ModelMetricClaimedVerifiedStatus
1SDCNetAverage PSNR37.15Unverified