Research on Methods to Improve Video Generation Quality Based on Google DeepMind’s Veo Model

This study addresses the persistent quality challenges in video generation using Google DeepMind’s Veo model, which is recognized as a state-of-the-art latent diffusion-based framework that has shown remarkable potential in synthesizing dynamic visual content across diverse applications. In its operation, Veo processes text and image prompts through UL2 and image encoders, effectively integrating them into a latent diffusion model to produce high-resolution videos with impressive realism and coherence in various contexts. Despite its notable strengths in generating realistic content across various scenarios, Veo continues to struggle with aspects such as clarity, temporal coherence, and fine details in complex scenarios that involve rapid motions, dynamic lighting, or multi-object interactions, which can lead to artifacts and reduced fidelity in practical use cases. To mitigate these issues comprehensively, we propose targeted enhancements: optimizing the denoising process through multi-scale fusion, adaptive steps, and attention mechanisms, building on the decomposition of image formation into sequential denoising autoencoders as described in [1]: “By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond.” Additionally, we refine embedded prompts with cross-modal fusion, semantic enrichment using BERT and ViT, and dynamic adjustments to better capture nuanced inputs; and we introduce novel training strategies including adversarial integration, multi-task learning, and self-supervised frame prediction to enhance overall model robustness. These improvements aim to boost visual realism, consistency, and generalization, leveraging diffusion models’ superiority over GANs as demonstrated in [2]: “We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models.” Extensive experiments on benchmarks like UCF-101, Kinetics-400, and Something-Something-V2 demonstrate superior performance, with gains in PSNR (up to 4 dB), SSIM (0.08), reduced FID (up to 37.5), and LPIPS (0.11). Subjective user studies further validate enhanced coherence and detail. Our methods provide valuable insights for advancing AI-driven video generation technologies in applications such as filmmaking and virtual reality.