Build awesome datasets for video generation

CineScale: Tuning-Free High-Resolution Video Generation

arXiv:2508.15774v2 Announce Type: replace Abstract: Visual diffusion models achieve remarkable progress, yet they are typically trained at limited resolutions due to the lack of high-resolution data...

Evaluating Design Video Generation: Metrics for Compositional Fidelity

arXiv:2605. 16223v2 Announce Type: replace-cross Abstract: Generative video models are increasingly used in design animation tasks, yet no standardized evaluation framework exists for this domain.

Video generation models as world simulators

We explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios.

Dancing Stick Figures: An Introductory Dataset for Training Video Generation Models

arXiv:2608.29123v1 Announce Type: new Abstract: Training a video-generation model from scratch is hard for reasons that precede model design. The feedback loop is long: a failure that appears only af...

Scaling robotics datasets with video encoding

Generating HDR Video from SDR Video

The paper presents a framework for converting standard dynamic range (SDR) videos into high dynamic range (HDR) videos using large-scale generative video models. It introduces a Multi-Exposure Video Model (MEVM) that predicts exposure-bracketed linear SDR sequences from a single nonlinear SDR input, and a Video Merging Model (VMM) that fuses these predictions into a high-quality HDR sequence while preserving detail in shadows and highlights. Experiments, qualitative evaluation, and a user study demonstrate robust HDR conversion for casual consumer footage and iconic films, and the approach can be integrated into existing SDR generative video pipelines.