VidaForge: Open Research Infrastructure for Video Pretraining Data Recipes

arXiv:2609.06652 · cs.CV · Submitted 2026-09-06 · Read on arXiv

cs.CV

Submitted: 2026-09-06

Updated: 2026-09-27

Code: https://github.com/discus0434/aesthetic-predictor-v2-5

Terminology

• Accuracy — 10× in this paper · explained in 1 episode
• Core — 8× in this paper · explained in 2 episodes
• Consistency — 7× in this paper · explained in 3 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Foundation Model — 6× in this paper · explained in 3 episodes
• Foundation Models — 5× in this paper · explained in 7 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Trace — 4× in this paper · explained in 2 episodes
• Actor — 3× in this paper · explained in 3 episodes
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 3 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Digital Twin — 1× in this paper · explained in 5 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Hamming Distance — 1× in this paper · explained in 2 episodes
• Motion Primitives — 1× in this paper · explained in 2 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Perplexity — 1× in this paper · explained in 3 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• Scaling Properties of Text Conditioning in Visual Generation
• The Faiss library
• Seedance 1.0: Exploring the Boundaries of Video Generation Models
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• TransNet V2: An effective deep network architecture for fast shot transition detection
• Wan: Open and Advanced Large-Scale Video Generative Models
• Motion Attribution for Video Generation
• EasyAnimate: High-Performance Video Generation Framework with Hybrid Windows Attention and Reward Backpropagation
• Moving Alphabet: A Controlled Study of Training Data for Text-to-Video Generation

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