VideoRepair: Improving Text-to-Video Generation via Misalignment Evaluation and Localized Refinement (2411.15115v2)

Abstract: Recent text-to-video (T2V) diffusion models have demonstrated impressive generation capabilities across various domains. However, these models often generate videos that have misalignments with text prompts, especially when the prompts describe complex scenes with multiple objects and attributes. To address this, we introduce VideoRepair, a novel model-agnostic, training-free video refinement framework that automatically identifies fine-grained text-video misalignments and generates explicit spatial and textual feedback, enabling a T2V diffusion model to perform targeted, localized refinements. VideoRepair consists of two stages: In (1) video refinement planning, we first detect misalignments by generating fine-grained evaluation questions and answering them using an MLLM. Based on video evaluation outputs, we identify accurately generated objects and construct localized prompts to precisely refine misaligned regions. In (2) localized refinement, we enhance video alignment by 'repairing' the misaligned regions from the original video while preserving the correctly generated areas. This is achieved by frame-wise region decomposition using our Region-Preserving Segmentation (RPS) module. On two popular video generation benchmarks (EvalCrafter and T2V-CompBench), VideoRepair substantially outperforms recent baselines across various text-video alignment metrics. We provide a comprehensive analysis of VideoRepair components and qualitative examples.

• The paper introduces a training-free, model-agnostic framework that uses a four-stage process to detect and correct misalignments in text-to-video generation.
• It leverages a multimodal language model to generate detailed evaluation questions, enabling precise localized refinements of video regions.
• Experiments on EvalCrafter and T2V-CompBench show significant improvements in count, color, and action details, enhancing overall T2V quality.

Insights on VideoRepair: Enhancing Text-to-Video Generation through Misalignment Evaluation and Localized Refinement

The field of text-to-video generation has gained significant attention with the advent of diffusion models capable of producing photorealistic outputs across a variety of contexts. However, current models often encounter challenges regarding alignment between generated video content and the intricacies described in text prompts, particularly with complex scenes involving multiple objects and attributes. The paper at hand explores this issue by introducing VideoRepair, a model-agnostic and training-free framework aimed at addressing fine-grained text-video misalignments inherent in text-to-video (T2V) diffusion models.

Core Contributions of VideoRepair

VideoRepair stands out by providing a structured four-stage process focused on identifying and correcting misalignments:

1. Video Evaluation: This stage involves generating fine-grained evaluation questions and obtaining responses using a multimodal LLM (MLLM) to detect alignment errors. The approach leverages advancements in LLMs and their capacity to handle complex evaluation tasks involving multiple video aspects.
2. Refinement Planning: Here, the system identifies accurately rendered objects and formulates dedicated prompts to guide the refinement process in certain parts of the video, ensuring that the refinement process is localized and precise.
3. Region Decomposition: Utilizing a combined grounding module, this stage segments areas of the video that have been correctly generated, allowing for specific areas to be targeted for refinement.
4. Localized Refinement: This final stage sees the misaligned regions regenerated, guided by local prompts and newly sampled noise, ensuring that correct regions are maintained while misaligned regions are improved.

Evaluation and Results

VideoRepair was tested against two well-regarded video generation benchmarks, EvalCrafter and T2V-CompBench, yielding strong performance improvements over existing baseline methods. These results underscore the effectiveness of the proposed framework in refining text-to-video alignment without necessitating additional model training.

On EvalCrafter, VideoRepair significantly improved text-video alignment metrics across diverse prompt categories, including count, color, and action details, with impressive results also observed in visual quality metrics. Similarly, on T2V-CompBench, VideoRepair excelled in tasks involving consistent attribute binding, spatial relationships, and generative numeracy.

Implications and Future Directions

The implications of these findings are multi-fold. Practically, the capacity of VideoRepair to operate in a model-agnostic manner suggests immediate applicability across a range of existing T2V models. This could significantly enhance the reliability and accuracy of T2V outputs in practical applications where precision and alignment with human-like comprehension and subtleties are essential.

Theoretically, the modularity of the VideoRepair framework offers a path forward for future research to extend these concepts to other challenges within AI-generated content domains. The refinement framework could lead to more robust systems capable of handling increasingly complex prompts while preserving desired quality without requiring additional computationally expensive training.

Speculative Future Developments

Despite the promising results, existing limitations within the state-of-the-art T2V models could yield further improvements. The framework of VideoRepair might be extended to incorporate more sophisticated methods for object and attribute detection, extending beyond current multimodal and LLM capabilities. Such extensions could include integrating other sensory modalities or advanced machine learning techniques, potentially advancing the field toward achieving near-human-level comprehension and content generation.

Overall, VideoRepair represents a significant stride in T2V technology, showcasing the power of targeted refinements combined with model-agnostic, training-free strategies, and opening doors for future innovations in both research and industry applications.