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 226–250 of 394 papers

TitleStatusHype
Multi-Task Video Captioning with Video and Entailment Generation—0
Novel Video Prediction for Large-scale Scene using Optical Flow—0
Novel View Video Prediction Using a Dual Representation—0
Object-Centric Temporal Consistency via Conditional Autoregressive Inductive Biases—0
OBJECT DYNAMICS DISTILLATION FOR SCENE DECOMPOSITION AND REPRESENTATION—0
Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video—0
Two-stage Rule-induction Visual Reasoning on RPMs with an Application to Video Prediction—0
On the Benefits of Instance Decomposition in Video Prediction Models—0
Option Discovery in Hierarchical Reinforcement Learning using Spatio-Temporal Clustering—0
Photo-Realistic Video Prediction on Natural Videos of Largely Changing Frames—0
Physics-informed Tensor-train ConvLSTM for Volumetric Velocity Forecasting of Loop Current—0
Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties—0
Predicting Long-horizon Futures by Conditioning on Geometry and Time—0
Predicting Real-Time Locational Marginal Prices: A GAN-Based Video Prediction Approach—0
Simplifying Latent Dynamics with Softly State-Invariant World Models—0
Prediction-assistant Frame Super-Resolution for Video Streaming—0
Prediction Under Uncertainty with Error Encoding Networks—0
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs—0
Pre-trained Visual Dynamics Representations for Efficient Policy Learning—0
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes—0
Probabilistic Video Prediction From Noisy Data With a Posterior Confidence—0
Prospective Messaging: Learning in Networks with Communication Delays—0
Efficient Video Prediction via Sparsely Conditioned Flow Matching—0
Real-time Video Prediction With Fast Video Interpolation Model and Prediction Training—0
Recurrent Deconvolutional Generative Adversarial Networks with Application to Text Guided Video Generation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Struct-VRNN (from Grid-keypoints)FVD395—Unverified
2SV2P time-invariant (from Grid-keypoints)FVD253.5—Unverified
3SV2P time-invariant (from Grid-keypoints)FVD209.5—Unverified
4SAVP (from Grid-keypoints)FVD183.7—Unverified
5SVG-LP (from Grid-keypoints)FVD157.9—Unverified
6SAVP-VAE (from Grid-keypoints)FVD145.7—Unverified
7Grid-keypointsFVD144.2—Unverified
8SVG-LP (from SRVP)Cond10—Unverified
9SLAMPCond10—Unverified
10SAVP (from SRVP)Cond10—Unverified
#ModelMetricClaimedVerifiedStatus
1ConvLSTMMSE103.3—Unverified
2PredRNNMSE56.8—Unverified
3MIMMSE52—Unverified
4PredRNN-V2MSE48.4—Unverified
5Causal LSTMMSE46.5—Unverified
6MIM*MSE44.2—Unverified
7SA-ConvLSTMMSE43.9—Unverified
8LMCMSE41.5—Unverified
9E3D-LSTMMSE41.3—Unverified
10CrevNet+ConvLSTMMSE38.5—Unverified
#ModelMetricClaimedVerifiedStatus
1LVTFVD224.73—Unverified
2OmniTokenizer-ARFVD32.9—Unverified
3RaMViDFVD16.46—Unverified
4RIN (400 steps)FVD11.5—Unverified
5RIN (1000 steps)FVD10.8—Unverified
6LARPFVD5.1—Unverified
7DVD-GAN-FPCond5—Unverified
8MAGVIT (-L-FP)Cond5—Unverified
9MAGVIT (-B-FP)Cond5—Unverified
10TriVD-GAN-FPCond5—Unverified
#ModelMetricClaimedVerifiedStatus
1IAM4VPSSIM0.94—Unverified
2SwinLSTMSSIM0.91—Unverified
3FFINetSSIM0.91—Unverified
4SimVPSSIM0.9—Unverified
5PhyDNetSSIM0.9—Unverified
6E3D-LSTMSSIM0.87—Unverified
7MIMSSIM0.79—Unverified
8PredRNNSSIM0.78—Unverified
9FRNNSSIM0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1SVG (from Hier-VRNN)FVD1,300.26—Unverified
2Hier-VRNNFVD567.51—Unverified
3SLAMPCond.10—Unverified
4SRVPCond.10—Unverified
5GHVAEsCond.2—Unverified
#ModelMetricClaimedVerifiedStatus
1SVG-DetLPIPS0.07—Unverified
2SVG-LPLPIPS0.07—Unverified
3PhyDNetLPIPS0.05—Unverified
4PredRNN++LPIPS0.05—Unverified
5MSPredLPIPS0.03—Unverified
#ModelMetricClaimedVerifiedStatus
1DVGFVD120.03—Unverified
2DVD-GAN-FPFVD109.8—Unverified
3PhenakiFVD97—Unverified
4MMVGFVD85.2—Unverified
#ModelMetricClaimedVerifiedStatus
1ODE2VAETest Error10.06—Unverified
2ODE2VAE-KLTest Error8.09—Unverified
3Latent ODETest Error5.98—Unverified
4Latent SDETest Error4.03—Unverified
#ModelMetricClaimedVerifiedStatus
1DVFLPIPS0.17—Unverified
2FVSLPIPS0.09—Unverified
3DMVFNLPIPS0.06—Unverified
#ModelMetricClaimedVerifiedStatus
1DVFLPIPS0.32—Unverified
2FVSLPIPS0.18—Unverified
3DMVFNLPIPS0.11—Unverified
#ModelMetricClaimedVerifiedStatus
1DVFLPIPS0.08—Unverified
2DMVFNLPIPS0.04—Unverified
3OPTLPIPS0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1ODE2VAETest Error93.07—Unverified
2ODE2VAE-KLTest Error15.99—Unverified
#ModelMetricClaimedVerifiedStatus
1DVFLPIPS0.23—Unverified
2DMVFNLPIPS0.1—Unverified
#ModelMetricClaimedVerifiedStatus
1MGP-VAE (with geodesic loss)MSE4.5—Unverified
#ModelMetricClaimedVerifiedStatus
1SRVPFVD222—Unverified
#ModelMetricClaimedVerifiedStatus
1MCnet [villegas2017mcnet]LPIPS0.22—Unverified
#ModelMetricClaimedVerifiedStatus
1MGP-VAE (with geodesic loss)MSE61.6—Unverified
#ModelMetricClaimedVerifiedStatus
1SDCNetAverage PSNR37.15—Unverified