Zero-shot Synthetic Video Realism Enhancement via Structure-aware Denoising

We propose an approach to enhancing synthetic video realism, which can re-render synthetic videos from a simulator in photorealistic fashion. Our realism enhancement approach is a zero-shot framework that focuses on preserving the multi-level structures from synthetic videos into the enhanced one in both spatial and temporal domains, built upon a diffusion video foundational model without further fine-tuning. Specifically, we incorporate an effective modification to have the generation/denoising process conditioned on estimated structure-aware information from the synthetic video, such as depth maps, semantic maps, and edge maps, by an auxiliary model, rather than extracting the information from a simulator. This guidance ensures that the enhanced videos are consistent with the original synthetic video at both the structural and semantic levels. Our approach is a simple yet general and powerful approach to enhancing synthetic video realism: we show that our approach outperforms existing baselines in structural consistency with the original video while maintaining state-of-the-art photorealism quality in our experiments.

ModelGrow: Continual Text-to-Video Pre-training with Model Expansion and Language Understanding Enhancement

Text-to-video (T2V) generation has gained significant attention recently. However, the costs of training a T2V model from scratch remain persistently high, and there is considerable room for improving the generation performance, especially under limited computation resources. This work explores the continual general pre-training of text-to-video models, enabling the model to "grow" its abilities based on a pre-trained foundation, analogous to how humans acquire new knowledge based on past experiences. There is a lack of extensive study of the continual pre-training techniques in T2V generation. In this work, we take the initial step toward exploring this task systematically and propose ModelGrow. Specifically, we break this task into two key aspects: increasing model capacity and improving semantic understanding. For model capacity, we introduce several novel techniques to expand the model size, enabling it to store new knowledge and improve generation performance. For semantic understanding, we propose a method that leverages large language models as advanced text encoders, integrating them into T2V models to enhance language comprehension and guide generation results according to detailed prompts. This approach enables the model to achieve better semantic alignment, particularly in response to complex user prompts. Extensive experiments demonstrate the effectiveness of our method across various metrics. The source code and the model of ModelGrow will be publicly available.