Dynamic Novel View Synthesis

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Overview

Dynamic novel view synthesis studies how to render a scene from new camera positions while preserving moving objects, changing appearance, and temporal continuity. Methods include neural radiance fields and 4D Gaussian splatting, which represent scene geometry and motion, as well as video diffusion models guided by camera paths, depth, optical flow, or multi-view observations. Researchers evaluate these approaches on synthetic dynamic scenes, captured human and object videos, and driving datasets such as Waymo, with tests ranging from nearby views to large viewpoint changes and future-frame synthesis. A central challenge is balancing realistic detail and fast rendering with geometric and temporal consistency, especially when training footage is sparse, monocular, or captured from cameras with little overlap.

Latest papers

CardsList

41 papers4 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

  1. LiveStre4m: Feed-Forward Live Streaming of Novel Views from Unposed Multi-View Video

    Apr 8, 2026Pedro Quesado, Erkut Akdag, Yasaman Kashefbahrami +2Novel View SynthesisDynamic Novel View Synthesis

    AIMSGroup, Department of Electrical Engineering, Eindhoven University of Technology

  2. GRVS: a Generalizable and Recurrent Approach to Monocular Dynamic View Synthesis

    Mar 31, 2026Thomas Tanay, Mohammed Brahimi, Michal Nazarczuk +5Dynamic Novel View SynthesisNovel View Synthesis

    Huawei Noah’s Ark Lab

  3. RehearsalNeRF: Decoupling Intrinsic Neural Fields of Dynamic Illuminations for Scene Editing

    Mar 30, 2026Changyeon Won, Hyunjun Jung, Jungu Cho +3Neural Radiance Fields3D Scene Editing

    Department of AI Convergence, GIST, Gwangju, Korea. · AI R&D Division, CJ Corporation, Seoul, Korea. · Department of Artificial Intelligence, Yonsei University, Seoul, Korea.

  4. DriveFix: Spatio-Temporally Coherent Driving Scene Restoration

    Mar 17, 2026Heyu Si, Brandon James Denis, Muyang Sun +94D ReconstructionDynamic Novel View Synthesis

    Zhejiang University · Huawei

  5. CamLit: Unified Video Diffusion with Explicit Camera and Lighting Control

    Mar 15, 2026Zhiyi Kuang, Chengan He, Egor Zakharov +6Video RelightingCamera-Controlled Video Generation

    Codec Avatars Lab, Reality Labs, Meta · State Key Lab of CAD&CG, Zhejiang University · University of Tübingen

  6. HDR-NSFF: High Dynamic Range Neural Scene Flow Fields

    Mar 9, 2026Shin Dong-Yeon, Kim Jun-Seong, Kwon Byung-Ki +1HDR Video ReconstructionHDR Imaging

    School of Electrical Engineering, KAIST · Dept. of Electrical Engineering, POSTECH · Grad. School of Artificial Intelligence, POSTECH +1

  7. GA-Drive: Geometry-Appearance Decoupled Modeling for Free-viewpoint Driving Scene Generation

    Feb 24, 2026Hao Zhang, Lue Fan, Qitai Wang +5Autonomous Driving SimulationDynamic Novel View Synthesis

    MMLab, CUHK · CASIA · Shanghai Jiaotong University +1

  8. Time-Archival Camera Virtualization for Sports and Visual Performances

    Feb 16, 2026Yunxiao Zhang, William Stone, Suryansh KumarDynamic Novel View SynthesisNeural Rendering

  9. AnyView: Synthesizing Any Novel View in Dynamic Scenes

    Jan 23, 2026Basile Van Hoorick, Dian Chen, Shun Iwase +7Multi-View ConsistencyCamera-Conditioned Video Generation

    Toyota Research Institute, CA, USA · Amazon Web Services, CA, USA

  10. EVolSplat4D: Efficient Volume-based Gaussian Splatting for 4D Urban Scene Synthesis

    Jan 22, 2026Sheng Miao, Sijin Li, Pan Wang +53D Gaussian SplattingDynamic Scene Reconstruction

    Zhejiang University, China. · Huawei, China. · University of Tübingen, Germany.

  11. InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem

    Dec 5, 2025Yeobin Hong, Suhyeon Lee, Hyungjin Chung +1Camera-Controlled Video GenerationVideo Diffusion Models

    Graduate School of AI, KAIST, South Korea · EverEx, South Korea · Korea University, South Korea