Title: GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning
Primary area: generative models
Keywords: video generation, efficient fine-tuning, automatic feedback

Abstract: Recent advances in diffusion models have significantly improved the quality of video generation. However, their real-world deployment often requires fine-tuning for physical constraints, a process that depends on human annotations and large-scale computational resources.
In this paper, we propose GigaVideo-1, an efficient fine-tuning framework that advances video generation without additional human supervision. Rather than injecting large volumes of high-quality data from external sources, GigaVideo-1 unlocks the latent potential of pre-trained video diffusion models through automatic feedback. 
GigaVideo-1 focuses on two key aspects: data and optimization. On the data side, we design a prompt-driven data engine that constructs diverse, weakness-oriented training samples. On the optimization side, we introduce a reward-guided training strategy, which adaptively weights samples using feedback from pre-trained vision-language models with a realism constraint.  
GigaVideo-1 offers a flexible optimization framework adaptable to various capability dimensions. To demonstrate its versatility, we instantiate the framework on VBench-2.0's 17 evaluation dimensions as concrete application instances. Using Wan2.1 as the baseline, GigaVideo-1 yields consistent improvements, with an average gain of $\sim$4\% using only 4 GPU-hours. Requiring no manual annotations and minimal real data, GigaVideo-1 shows both effectiveness and efficiency. Code, model, and data will be publicly available.