arXiv Computer Vision
CineScale: Tuning-Free High-Resolution Video Generation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Oct 16
Dynamic Video Generation: Shaping Video Generation Across Time and Space
The paper introduces DVG, a Dynamic Video Generation framework that allocates computation across time and space to accelerate diffusion-based video generation. By selecting content-aware acceleration strategies automatically, DVG achieves near-lossless speedups—up to 7× on HunyuanVideo models and 18× when combined with distillation—without manual tuning or retraining. The authors provide code on GitHub for reproducibility.
arXiv Computer Vision
Oct 19
PixelWizard: Towards Efficient High-Fidelity Video Generation at Ultra-Large Spatial Resolution
arXiv:2605.25801v2 Announce Type: replace Abstract: High-resolution video generation faces a coupled bottleneck of optimization instability and prohibitive computational costs. The massive expansion...
arXiv Computer Vision
Oct 22
NeuralSRender: G-Buffer Guided Video Diffusion for Sim-to-Real Rendering
NeuralSRender is a sim‑to‑real video translation framework that improves the photorealism of synthetic driving videos while preserving geometric consistency. It builds on a pretrained video diffusion transformer and uses a lightweight projector to inject G‑buffer geometric and material priors, with LoRA for efficient adaptation. The two‑stage training first learns G‑buffer conditioning from synthetic data, then adapts to real‑world appearance, adding only 1.45 % parameter overhead and requiring 24 GB GPU memory. The authors also release GenReal, a large‑scale dataset of synthetic and real driving videos with G‑buffers, and show that training perception models on NeuralSRender‑translated videos boosts detection AP₅₀ on nuImages from 35.1 to 40.6 and segmentation mIoU on Cityscapes from 32.4 to 40.8.
arXiv Machine Learning
Oct 21
FiTv2: Scalable and Improved Flexible Vision Transformer for Diffusion Model
arXiv:2410.13925v2 Announce Type: replace Abstract: \textit{Nature is infinitely resolution-free}. In the context of this reality, existing diffusion models, such as Diffusion Transformers, often fac...
arXiv Computer Vision
Sep 10
GTA: Advancing Image-to-3D World Generation via Geometry Then Appearance Video Diffusion
arXiv:2605.12957v2 Announce Type: replace Abstract: Recent developments in generative models and large-scale datasets have substantially advanced 3D world generation, facilitating a broad range of do...
arXiv Machine Learning
Jun 9
MilliVid: Hierarchical Latents for Long-Range Consistency in Video Generation
arXiv:2606. 09056v1 Announce Type: cross Abstract: Video generative models have become increasingly powerful, but long-range consistency remains challenging to achieve because even a few dozen frames require impractically long transformer sequence lengths.