TempCo-Painter: Temporal Consistency Enhanced Painter with Adaptive Diffusion Transformers for Long Video Inpainting

Video inpainting, a critical task in computer vision, aims to plausibly fill missing regions in video sequences while maintaining both spatial realism and robust spatio-temporal consistency. Current methods often struggle with ultra-long videos, highly dynamic occlusions, and achieving extreme coherence efficiently, leading to common artifacts. To address these challenges, we propose TempCo-Painter: Temporal Consistency Enhanced Painter with Adaptive Diffusion Transformers. Our novel framework leverages a specialized 3D-VAE for efficient latent space compression and introduces an innovative Adaptive Diffusion Transformer (ADiT). ADiT integrates hierarchical spatial-temporal attention, a motion-guided attention mechanism for accurate dynamic content restoration, and dynamic mask awareness for robust handling of diverse occlusions. An efficient Flow Matching scheduler further enables TempCo-Painter to generate high-quality results with minimal denoising steps. For processing arbitrarily long videos, we introduce an enhanced MultiDiffusion strategy featuring an adaptive sliding window and temporal smoothing regularization to ensure seamless global consistency. Extensive experiments demonstrate that TempCo-Painter achieves state-of-the-art performance on standard short video benchmarks, significantly outperforming existing methods in PSNR, SSIM, and notably reducing Video Frechet Inception Distance. Furthermore, it exhibits superior robustness and coherence on challenging minute-level long videos and complex mask scenarios, while maintaining high inference efficiency.