Video Generation
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Benchmarks
UCF-101
BAIR Robot Pushing
Sky Time-lapse
LAION-400M
Taichi
How2Sign
Kinetics-700
MSR-VTT
TrailerFaces
YouTube Driving
Most implemented
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Consistency Models
Everybody Dance Now
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
Video Diffusion Models
MoCoGAN: Decomposing Motion and Content for Video Generation
Papers
GRACE: Generation-aware latent compression for efficient video generation
Highly compressed video autoencoders offer an effective way to accelerate video diffusion models, as the Diffusion Transformer (DiT) operates on far fewer tokens. However, such autoencoders are challenging to train, sinc…
Video GenerationSGF+: Decoupling Gradient Flows for Autoregressive Video Generation
Autoregressive video generation requires denoising the current frames while writing their key-value representations as context for future predictions. However, these two roles typically share parameters, and we find that…
Video GenerationAdSpark: A Large-Scale Dataset and Benchmark for Product-Centric Advertisement Video Generation
Product-centric advertisement video generation aims to create promotional videos that preserve fine-grained product identity while presenting selling points through coherent multi-shot narratives. However, this emerging …
Visual StorytellingVideo GenerationWorld Models' Last Exam in Physics
Video world models can produce visually convincing yet physically inconsistent sequences, raising concerns about their reliability for prediction and planning in embodied AI systems. Existing evaluations often rely on mo…
Video GenerationRealtimeWAM: One-Step Asynchronous World Action Models
World Action Models (WAMs) incorporate visual representations from video generation backbones to guide action prediction. Recent efficient WAMs adopt Mixture-of-Transformers (MoT) architectures and compute video represen…
Video GenerationS2PD: Serial-to-Parallel Diffusion for Physically and Logically Consistent Video Generation
Bidirectional video diffusion models denoise entire videos in parallel, yet when trained on effectively unlimited in-distribution data from procedural generators, continue to violate physical laws and simple symbolic rul…
Physical SimulationsVideo Generation