Unconditional Video Generation

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task (1 more in the archive withheld as spam; see /not-shown), 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

10 shown of 10 papers with code (15 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Video Diffusion Models
  7 Apr 2022 5 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Generating temporally coherent high fidelity video is an important milestone in generative modeling research.
• MOSO: Decomposing MOtion, Scene and Object for Video Prediction
  7 Mar 2023 2 repositories listed
  Experimental results demonstrate that our method achieves new state-of-the-art performance on five challenging benchmarks for video prediction and unconditional video generation: BAIR, RoboNet, KTH, KITTI and UCF101.
• Generative Video Bi-flow
  9 Mar 2025 1 repository listed
  We propose a novel generative video model by robustly learning temporal change as a neural Ordinary Differential Equation (ODE) flow with a bilinear objective of combining two aspects: The first is to map from the past…
• StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN
  21 Mar 2024 1 repository listed Syntology ran 5 of 9 samples · 4 unverified
  We propose a method that can generate cinemagraphs automatically from a still landscape image using a pre-trained StyleGAN.
• StyleInV: A Temporal Style Modulated Inversion Network for Unconditional Video Generation
  31 Aug 2023 1 repository listed Syntology ran 0 of 2 samples · 2 unverified · 2 pointer-only (licence)
  In this paper, we introduce a novel motion generator design that uses a learning-based inversion network for GAN.
• DDLP: Unsupervised Object-Centric Video Prediction with Deep Dynamic Latent Particles
  9 Jun 2023 1 repository listed Syntology ran 4 of 5 samples · 1 unverified
  We propose a new object-centric video prediction algorithm based on the deep latent particle (DLP) representation.
• Video Diffusion Models with Local-Global Context Guidance
  5 Jun 2023 1 repository listed Syntology ran 7 of 17 samples · 10 unverified
  We construct a local-global context guidance strategy to capture the multi-perceptual embedding of the past fragment to boost the consistency of future prediction.
• MotionVideoGAN: A Novel Video Generator Based on the Motion Space Learned from Image Pairs
  6 Mar 2023 1 repository listed
  We present MotionVideoGAN, a novel video generator synthesizing videos based on the motion space learned by pre-trained image pair generators.
• CelebV-HQ: A Large-Scale Video Facial Attributes Dataset
  25 Jul 2022 1 repository listed
  Large-scale datasets have played indispensable roles in the recent success of face generation/editing and significantly facilitated the advances of emerging research fields.
• Latent Neural Differential Equations for Video Generation
  7 Nov 2020 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Generative Adversarial Networks have recently shown promise for video generation, building off of the success of image generation while also addressing a new challenge: time.

Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.