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 251–300 of 394 papers

TitleStatusHype
Reduced-Gate Convolutional LSTM Design Using Predictive Coding for Next-Frame Video Prediction—0
Revisiting Adaptive Convolutions for Video Frame Interpolation—0
RoboNet: Large-Scale Multi-Robot Learning—0
Robot Motion Planning as Video Prediction: A Spatio-Temporal Neural Network-based Motion Planner—0
Rolling Diffusion Models—0
S2RMs: Spatially Structured Recurrent Modules—0
See, Plan, Predict: Language-guided Cognitive Planning with Video Prediction—0
Self-Supervised Learning of Object Motion Through Adversarial Video Prediction—0
Self-Supervision by Prediction for Object Discovery in Videos—0
S-HR-VQVAE: Sequential Hierarchical Residual Learning Vector Quantized Variational Autoencoder for Video Prediction—0
Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion—0
SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning—0
Skillful Nowcasting of Convective Clouds With a Cascade Diffusion Model—0
SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models—0
SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving—0
Space Time Recurrent Memory Network—0
Spatially Structured Recurrent Modules—0
Spatially Structured Recurrent Modules—0
State-space Decomposition Model for Video Prediction Considering Long-term Motion Trend—0
STAU: A SpatioTemporal-Aware Unit for Video Prediction and Beyond—0
STIV: Scalable Text and Image Conditioned Video Generation—0
Stochastic Video Prediction with Structure and Motion—0
Structure Preserving Video Prediction—0
TAFormer: A Unified Target-Aware Transformer for Video and Motion Joint Prediction in Aerial Scenes—0
Taming Teacher Forcing for Masked Autoregressive Video Generation—0
Taylor saves for later: disentanglement for video prediction using Taylor representation—0
Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning—0
Text-driven Video Prediction—0
Time Dependence in Non-Autonomous Neural ODEs—0
TKN: Transformer-based Keypoint Prediction Network For Real-time Video Prediction—0
Towards a Generalizable Bimanual Foundation Policy via Flow-based Video Prediction—0
Towards an Interpretable Latent Space in Structured Models for Video Prediction—0
Towards Non-Parametric Models for Confidence Aware Video Prediction on Smooth Dynamics—0
Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better—0
Transformation-based Adversarial Video Prediction on Large-Scale Data—0
Unified Video Action Model—0
Unsupervised Hierarchical Video Prediction—0
Unsupervised Learning of Automotive 3D Crash Simulations using LSTMs—0
Unsupervised Learning of Object Structure and Dynamics from Videos—0
Unsupervised Video Prediction from a Single Frame by Estimating 3D Dynamic Scene Structure—0
VAE^2: Preventing Posterior Collapse of Variational Video Predictions in the Wild—0
Variational Inference for SDEs Driven by Fractional Noise—0
Video Captioning with Boundary-aware Hierarchical Language Decoding and Joint Video Prediction—0
Video Extrapolation in Space and Time—0
VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation—0
Video Interpolation and Prediction with Unsupervised Landmarks—0
Video Prediction by Modeling Videos as Continuous Multi-Dimensional Processes—0
Precipitation Nowcasting with Star-Bridge Networks—0
Video Prediction Models as General Visual Encoders—0
Video prediction using score-based conditional density estimation—0
Show:102550
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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