paper-with-me

Papers

Interactive Control over Temporal Consistency while Stylizing Video Streams

2023-01-02 · Sumit Shekhar, Max Reimann, Moritz Hilscher, Amir Semmo, Jürgen Döllner, Matthias Trapp

Image stylization has seen significant advancement and widespread interest over the years, leading to the development of a multitude of techniques. Extending these stylization techniques, such as Neural Style Transfer (NST), to videos is often achieved by applying them on a per-frame basis. However, per-frame stylization usually lacks temporal consistency, expressed by undesirable flickering artifacts. Most of the existing approaches for enforcing temporal consistency suffer from one or more of the following drawbacks: They (1) are only suitable for a limited range of techniques, (2) do not support online processing as they require the complete video as input, (3) cannot provide consistency for the task of stylization, or (4) do not provide interactive consistency control. Domain-agnostic techniques for temporal consistency aim to eradicate flickering completely but typically disregard aesthetic aspects. For stylization tasks, however, consistency control is an essential requirement as a certain amount of flickering adds to the artistic look and feel. Moreover, making this control interactive is paramount from a usability perspective. To achieve the above requirements, we propose an approach that stylizes video streams in real-time at full HD resolutions while providing interactive consistency control. We develop a lite optical-flow network that operates at 80 FPS on desktop systems with sufficient accuracy. Further, we employ an adaptive combination of local and global consistency features and enable interactive selection between them. Objective and subjective evaluations demonstrate that our method is superior to state-of-the-art video consistency approaches.

📄 PDF Abstract BibTeX arXiv:2301.00750

Code (1)

MaxReimann/video-stream-consistency 공식 구현 pytorch

Tasks

Image StylizationOptical Flow EstimationStyle TransferVideo StabilizationVideo Temporal Consistency

Similar Papers 제목 키워드 기반

StableWorld: Towards Stable and Consistent Long Interactive Video Generation

2026-01-21 · Ying Yang, Zhengyao Lv, Tianlin Pan, Haofan Wang 외 arxiv

In this paper, we explore the overlooked challenge of stability and temporal consistency in interactive video generation, which synthesizes dynamic and controllable video worlds through interactive behaviors such as came…

Video Generation

SNAP: A Plan-Driven Framework for Controllable Interactive Narrative Generation

2025-11-18 · Geonwoo Bang, DongMyung Kim, Hayoung Oh arxiv

Large Language Models (LLMs) hold great potential for web-based interactive applications, including browser games, online education, and digital storytelling platforms. However, LLM-based conversational agents suffer fro…

Hunyuan-GameCraft: High-dynamic Interactive Game Video Generation with Hybrid History Condition

2025-06-20 · Jiaqi Li, Junshu Tang, Zhiyong Xu, Longhuang Wu 외

Recent advances in diffusion-based and controllable video generation have enabled high-quality and temporally coherent video synthesis, laying the groundwork for immersive interactive gaming experiences. However, current…

Temporal SequencesVideo Generation

FrameDiffuser: G-Buffer-Conditioned Diffusion for Neural Forward Frame Rendering

2025-12-18 · Ole Beisswenger, Jan-Niklas Dihlmann, Hendrik P. A. Lensch arxiv

Neural rendering for interactive applications requires translating geometric and material properties (G-buffer) to photorealistic images with realistic lighting on a frame-by-frame basis. While recent diffusion-based app…

RealCam: Real-Time Novel-View Video Generation with Interactive Camera Control

2026-05-07 · Youcan Xu, Jiaxin Shi, Zhen Wang, Wensong Song 외 arxiv

Camera-controlled video-to-video (V2V) generation enables dynamic viewpoint synthesis from monocular footage, holding immense potential for interactive filmmaking and live broadcasting. However, existing implicit synthes…

Data AugmentationVideo Generation