LDMVFI: Video Frame Interpolation with Latent Diffusion Models (2303.09508v3)

Abstract: Existing works on video frame interpolation (VFI) mostly employ deep neural networks that are trained by minimizing the L1, L2, or deep feature space distance (e.g. VGG loss) between their outputs and ground-truth frames. However, recent works have shown that these metrics are poor indicators of perceptual VFI quality. Towards developing perceptually-oriented VFI methods, in this work we propose latent diffusion model-based VFI, LDMVFI. This approaches the VFI problem from a generative perspective by formulating it as a conditional generation problem. As the first effort to address VFI using latent diffusion models, we rigorously benchmark our method on common test sets used in the existing VFI literature. Our quantitative experiments and user study indicate that LDMVFI is able to interpolate video content with favorable perceptual quality compared to the state of the art, even in the high-resolution regime. Our code is available at https://github.com/danier97/LDMVFI.

• The paper presents LDMVFI, a novel approach that reframes video frame interpolation as a conditional generation problem using latent diffusion models and a custom VQ-FIGAN autoencoder.
• It employs VQ-FIGAN with deformable kernel synthesis and MaxViT-based self-attention to integrate neighboring frame features and improve latent representations.
• Rigorous evaluations across high-resolution datasets and perceptual metrics demonstrate LDMVFI's superior performance over traditional VFI methods.

Overview of "LDMVFI: Video Frame Interpolation with Latent Diffusion Models"

The paper "LDMVFI: Video Frame Interpolation with Latent Diffusion Models" by Duolikun Danier, Fan Zhang, and David Bull introduces a novel approach to video frame interpolation (VFI) by employing latent diffusion models (LDMs). Traditional methods in VFI generally utilize deep neural networks optimized based on loss metrics such as L1, L2, and VGG feature space distances. However, these metrics often fail to accurately assess perceptual quality as perceived by human observers. In contrast, the approach proposed in this paper reformulates VFI as a conditional generation problem, leveraging the generative capabilities of latent diffusion models.

Key Contributions

1. Introduction of LDMVFI:
   • The research presents LDMVFI, a method that reframes the VFI task using LDMs, a form of diffusion models which perform operations in a compact latent space.
   • It employs an innovative autoencoding model, VQ-FIGAN, specifically designed for VFI by using latent diffusion models.
2. VFI-Specific Innovations:
   • The paper proposes a vector-quantized autoencoding model (VQ-FIGAN) that enhances traditional LDMs through frame-specific improvements such as deformable kernel-based synthesis and MaxViT-based self-attention mechanisms.
3. Benchmarking and Evaluation:
   • LDMVFI is rigorously benchmarked across several datasets, including high-resolution scenarios (up to 4K), showing favorable performance over state-of-the-art models according to perceptual metrics like LPIPS, FloLPIPS, and FID.
   • A user paper further corroborates the qualitative benefits of LDMVFI's outputs, highlighting its perceptual superiority.

Methodological Approach

LDMVFI introduces a novel perspective on handling VFI by transforming it into a generative modeling task. Traditional pixel-space-based formulations are replaced by latent space operations via a two-component system: a VQ-FIGAN encoder-decoder network and a denoising U-Net for reverse diffusion.

• VQ-FIGAN Model:
  • This model improves upon typical autoencoders by incorporating neighboring frames' features into the decoding process through cross-attention mechanisms.
  • It adopts a vector quantization approach to improve the perceptual quality and representation capability of the latent features.
• Diffusion Process:
  • Latent diffusion processes in LDMVFI are responsible for incrementally generating intermediate frames by conditioning on adjacent video frames.
  • This generative approach enhances the quality of interpolated frames, addressing the inadequacies of existing perceptual quality metrics.

Implications and Future Developments

The introduction of diffusion models in video frame interpolation opens new avenues for improving perceptual quality in video processing. This work suggests that integrating sophisticated generative techniques with VFI can offer substantial improvements over traditional methods, especially in complex motion scenarios like dynamic textures.

For future developments, optimizing LDM-based architectures for efficiency can mitigate the computational demands observed in LDMVFI. Exploring faster sampling techniques and model distillation could significantly improve inference speeds, making LDMVFI suitable for more real-time applications.

Conclusion

Overall, this paper marks a notable advance in the VFI field, demonstrating the efficacy of using latent diffusion models for generating high-quality video frames with enhanced perceptual fidelity. By creatively adopting engineering principles from generative models, this research provides a robust framework for future studies focused on perception-oriented video synthesis and processing tasks.