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Open access Aug 2026

Long-Horizon Video Generation with Temporally Consistent Diffusion and Scene-Graph Guidance

A novel framework that integrates temporally consistent diffusion models with dynamic scene-graph guidance that structurally constrains the generative process, ensuring that objects, their attributes, and their interrelationships remain stable over extended durations is introduced.

Jacob A. Jenkins · 0 citations
Preprint Aug 2026

Bridging Event Streams and DiT: Event-Guided Video Frame Interpolation

This work proposes an adapter-based framework that incorporates event-derived cues into a pre-trained image-to-video diffusion model with minimal architectural changes and consistently outperforms existing state-of-the-art approaches.

Guixu Lin, Yuyang Yu, Xiang Ji et al. · 0 citations
Preprint Jul 2026

SPEED: One-Step Pixel Diffusion for High-quality Video Frame Interpolation

Despite the success of diffusion models in Video Frame Interpolation (VFI), existing methods still suffer from two critical limitations. First, latent diffusion inevitably loses fine-grained details when reconstructing images from latent representations back to the pixel space. Second, multi-step sampling incurs prohibitive memory consumption and inference latency. To address these issues, we propose SPEED, a one-step pixel diffusion framework for high-quality VFI. Specifically, SPEED employs a progressive multi-stage architecture with dynamic patch scaling to effectively learn multi-scale motion, structural, and appearance representations. Furthermore, we propose a novel Noise-Update-Only Attention mechanism to prevent semantic degradation of the clean condition frames while reducing the computational overhead by nearly 50%. Besides, we introduce a Drift-aware Timestep Sampling strategy coupled with a tailored training objective to directly predict images in the pixel space, enabling one-step inference without compromising the quality of the generated frames. Extensive experiments show that SPEED achieves state-of-the-art performance. On SNU-FILM, SPEED reduces LPIPS by 8.8% while delivering 63.3% faster inference and 10.6% lower memory usage. On challenging 4K benchmarks, it further surpasses prior methods by up to 51.5% in LPIPS.

Zihao Zhang, Haoyu Zhao, Siqian Yang et al. · 0 citations
Preprint Aug 2026

MotionCraft: Latent World Modeling with Sparse Attention for Visual Upscaling

MotionCraft is presented, a controllable VSR framework that formulates restoration as motion-aware latent state prediction inspired by world models and integrates adaptive sparse attention with an explicit user-accessible control interface to deliver temporally consistent, high-quality reconstructions under streaming constraints.

Rong Fu, Chunlei Meng, Yangcheng Zeng et al. · 0 citations
Preprint Jul 2026

DiffCVE: Diffusion-based Compressed Video Enhancement

Coding Prior-enhanced Dual Conditioning branches are designed to jointly model compressed video and coding prior conditions, where coding priors including residuals and motion vectors provide complementary structural and motion guidance during the diffusion denoising process.

Wenqiang Xiao, Wenzhuo Ma, Junxi Zhang et al. · 0 citations
Book Open access Jul 2026

LongE2V: Long-Horizon Event-based Video Reconstruction, Prediction, and Frame Interpolation with Video Diffusion Models

This work proposes LongE2V, a novel approach that leverages pre-trained video diffusion priors to jointly handle event-based video reconstruction, prediction, and frame interpolation, and introduces Autoregressive Unrolling and Adaptive Context Switching to mitigate temporal drift in extremely long sequences.

Cheng-De Fan, Chun-Wei Tuan Mu, Chen-Wei Chang et al. · 0 citations