{"id":64500,"url":"https://github.com/pdaicode/awesome-dynamic-NeRF","name":"awesome-dynamic-NeRF","description":"A curated list of awesome neural radiance fields for dynamic scenes","projects_count":58,"last_synced_at":"2026-06-08T07:00:35.411Z","repository":{"id":239104356,"uuid":"775731057","full_name":"pdaicode/awesome-dynamic-NeRF","owner":"pdaicode","description":"A curated list of awesome neural radiance fields for dynamic scenes","archived":false,"fork":false,"pushed_at":"2026-04-21T19:25:00.000Z","size":514,"stargazers_count":67,"open_issues_count":1,"forks_count":2,"subscribers_count":2,"default_branch":"main","last_synced_at":"2026-05-22T19:41:13.428Z","etag":null,"topics":["3d","awesome-list","dynamic-nerf","literature-review","nerf","volume-rendering","volumetric-rendering"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pdaicode.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-03-21T23:54:32.000Z","updated_at":"2026-05-22T15:32:53.000Z","dependencies_parsed_at":"2024-05-10T03:23:37.975Z","dependency_job_id":"b04b98b8-4a9a-47df-8115-94577caaa069","html_url":"https://github.com/pdaicode/awesome-dynamic-NeRF","commit_stats":null,"previous_names":["pdaicode/awesome-dynamic-nerf"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/pdaicode/awesome-dynamic-NeRF","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pdaicode%2Fawesome-dynamic-NeRF","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pdaicode%2Fawesome-dynamic-NeRF/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pdaicode%2Fawesome-dynamic-NeRF/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pdaicode%2Fawesome-dynamic-NeRF/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pdaicode","download_url":"https://codeload.github.com/pdaicode/awesome-dynamic-NeRF/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pdaicode%2Fawesome-dynamic-NeRF/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34051772,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-08T02:00:07.615Z","response_time":111,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-08-01T18:05:23.867Z","updated_at":"2026-06-08T07:00:35.412Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["2020","2022","2023","2024","2021","Custom Data Preparation","2026","2025"],"sub_categories":["Real"],"readme":"# Dynamic NeRF\n\nVerified: Papers listed with [+] have been verfied by myself or colleagues. The code is runnable. Please leave an issue if you need help on setting up.\n\n# 1. Datasets\n## Custom Data Preparation\n- [Monocular Dynamic View Synthesis: A Reality Check](https://github.com/KAIR-BAIR/dycheck/blob/main/docs/RECORD3D_CAPTURE.md)\n- [Process a video into a Nerfie dataset](https://colab.research.google.com/github/google/nerfies/blob/main/notebooks/Nerfies_Capture_Processing.ipynb)\n- [Robust Dynamic Radiance Fields](https://github.com/facebookresearch/robust-dynrf)\nEstimate monocular depth, Predict optical flows, Obtain motion mask.\n- [Neural Scene Flow Fields](https://github.com/zhengqili/Neural-Scene-Flow-Fields/tree/main)\nInstructions for custom data.\n\n### Synthetic\n- [D-Nerf Dataset](https://www.albertpumarola.com/research/D-NeRF/index.html)\n\n\n### Real\n- [Plenoptic Dataset](https://github.com/facebookresearch/Neural_3D_Video/releases/tag/v1.0)\n- [Hypernerf Dataset](https://github.com/google/hypernerf/releases/tag/v0.1)\n- [Nerfies Dataset](https://github.com/google/nerfies/releases/download/0.1/nerfies-vrig-dataset-v0.1.zip)\n- [Dynamic NeRF](https://github.com/gaochen315/DynamicNeRF)\nBalloon1, Balloon2, Jumping, Playground, Skating, Truck, Umbrella\n\n\n# 2. My Notebooks\n- Robust Dynamic Radiance Fields, Liu et. al., CVPR, 2023. [[Kaggle](https://www.kaggle.com/code/declanide/robust-nerf)]\n\n# 3. Papers\n## 2026\n- 4C4D: 4 Camera 4D Gaussian Splatting, CVPR 2026. [Code](https://github.com/yangzf-1023/4C4D)\n- Splannequin: Freezing monocular mannequin-challenge footage with dual-detection splatting, WACV 2026 [Page](https://chien90190.github.io/splannequin/), [Code](https://github.com/chien90190/splannequin-gs)\n- ClipGStream: Clip-Stream Gaussian Splatting for Any Length and Any Motion Multi-View Dynamic Scene Reconstruction, CVPR 2026. [Project](https://liangjie1999.github.io/ClipGStreamWeb/)\n\n## 2025\n- FreeTimeGS: Free Gaussian Primitives at Anytime Anywhere for Dynamic Scene Reconstruction, CVPR 2025. [Code](https://github.com/OpsiClear/FreeTimeGsVanilla)\n- Adaptive and Temporally Consistent Gaussian Surfels for Multi-view Dynamic Reconstruction, WACV 2025. [Code](https://github.com/fraunhoferhhi/AT-GS)\n- 1000+ FPS 4D Gaussian Splatting for Dynamic Scene Rendering, 2025\n- DASH: 4D Hash Encoding with Self-Supervised Decomposition for Real-Time Dynamic Scene Rendering, ICCV 2025. [Code](https://github.com/chenj02/DASH)\n- Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation, 2025. [Code](https://github.com/ohsngjun/3D-4DGS)\n- 4dslomo: 4d reconstruction for high speed scene with asynchronous capture, Siggraph Asia 2025. [Code](https://github.com/OpenImagingLab/4DSloMo)\n\n- 4K4DGen: Panoramic 4D Generation at 4K Resolution, ICLR 2025. [Code](https://github.com/ShadowIterator/4K4DGen)\n\n## 2024\n- Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis, Luiten et. al., International Conference on 3D Vision (3DV), 2024. [[Paper](https://dynamic3dgaussians.github.io/paper.pdf) | [Project Page](https://dynamic3dgaussians.github.io/) | [Code](https://github.com/JonathonLuiten/Dynamic3DGaussians) | [Explanation Video](https://www.youtube.com/live/hDuy1TgD8I4?si=6oGN0IYnPRxOibpg)]\n- Sync-NeRF : Generalizing Dynamic NeRFs to Unsynchronized Videos, AAAI 2024. [[Paper](https://arxiv.org/abs/2310.13356), [Code](https://github.com/seoha-kim/Sync-NeRF)]\n- Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting, [[Paper](https://arxiv.org/abs/2401.16416) | [Code](https://github.com/lastbasket/Endo-4DGS)]\n- DaReNeRF: Direction-aware Representation for Dynamic Scenes, CVPR 2024\n- Sync-NeRF: Generalizing Dynamic NeRFs to Unsynchronized Videos, AAAI2024. [[Code](https://github.com/seoha-kim/Sync-NeRF)]\n- SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes. [[Code](https://github.com/yihua7/SC-GS)]\n- InstantSplat: Unbounded Sparse-view Pose-free Gaussian Splatting in 40 Seconds, [Project](https://instantsplat.github.io/)\n- GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation\n- Entity-NeRF: Detecting and Removing Moving Entities in Urban Scenes, CVPR 2024. [Project](https://otonari726.github.io/entitynerf/)\n- Ced-NeRF: A Compact and Efficient Method for Dynamic Neural Radiance Fields, AAAI 2024. [Paper](https://ojs.aaai.org/index.php/AAAI/article/view/28138)\n- Shape of Motion: 4D Reconstruction from a Single Video, 2024. [[Project](https://shape-of-motion.github.io/) | [Code](https://github.com/vye16/shape-of-motion/)]\n- MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds, 2024. [Project](https://www.cis.upenn.edu/~leijh/projects/mosca/)\n- Dynamic Gaussian Marbles for Novel View Synthesis of Casual Monocular Videos, 2024\n- Dynamic Gaussian Mesh: Consistent Mesh Reconstruction from Monocular Videos, 2024. [Project](https://www.liuisabella.com/DG-Mesh/)\n- DyNeRFactor: Temporally consistent intrinsic scene decomposition for dynamic NeRFs, 2024. [Paper](https://www.sciencedirect.com/science/article/pii/S0097849324001195?dgcid=rss_sd_all)\n- DynVideo-E: Harnessing Dynamic NeRF for Large-Scale Motion- and View-Change Human-Centric Video Editing, CVPR 2024. [Project](https://showlab.github.io/DynVideo-E/)\n- Point-DynRF: Point-Based Dynamic Radiance Fields From a Monocular Video, WACV 2024. [Paper](https://openaccess.thecvf.com/content/WACV2024/papers/Park_Point-DynRF_Point-Based_Dynamic_Radiance_Fields_From_a_Monocular_Video_WACV_2024_paper.pdf)\n\n- FPO++: efficient encoding and rendering of dynamic neural radiance fields by analyzing and enhancing Fourier PlenOctrees, The Visual Computer, 2024.\n- Evdnerf: Reconstructing event data with dynamic neural radiance fields, WACV 2024. [Code](https://github.com/anish-bhattacharya/EvDNeRF)\n- CTNeRF: Cross-time Transformer for dynamic neural radiance field from monocular video, Pattern Recognition, 2024. [Code](https://github.com/xingy038/ctnerf)\n- DynamicSurf: Dynamic Neural RGB-D Surface Reconstruction with an Optimizable Feature Grid, International Conference on 3D Vision (3DV) 2024. [Code](https://github.com/Mirgahney/dynsurf)\n- [+] Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis, CVPR 2024. [Code](https://github.com/oppo-us-research/SpacetimeGaussians)\n\n## 2023\n- DynIBaR: Neural Dynamic Image-Based Rendering, CVPR, 2023 [[Project Page](https://dynibar.github.io/)]\n- Tensor4D : Efficient Neural 4D Decomposition for High-fidelity Dynamic Reconstruction and Rendering, Shao et. al., CVPR, 2023. [[Paper](https://arxiv.org/abs/2211.11610) | [Code](https://github.com/DSaurus/Tensor4D)]\n- HyperReel: High-Fidelity 6-DoF Video with Ray-Conditioned Sampling, CVPR 2023 (Highlight). [Code](https://github.com/facebookresearch/hyperreel)\n- HexPlane: A Fast Representation for Dynamic Scenes, Cao et. al., CVPR, 2023. [[Paper](https://caoang327.github.io/HexPlane/HexPlane.pdf) | [Project Page](https://caoang327.github.io/HexPlane/) | [Code](https://github.com/Caoang327/HexPlane)]\n- Robust Dynamic Radiance Fields, Liu et. al., CVPR, 2023. [[Code](https://github.com/facebookresearch/robust-dynrf) | [Kaggle](https://www.kaggle.com/code/declanide/robust-nerf)]\n- V4D: Voxel for 4D Novel View Synthesis, Gan et. al., IEEE Transactions on Visualization and Computer Graphics, 2023. [[Paper](https://arxiv.org/abs/2205.14332) | [Code](https://github.com/GANWANSHUI/V4D)] (instructions for custom data)\n- Dynamic Mesh-Aware Radiance Fields, ICCV, 2023. [[Project Page](https://mesh-aware-rf.github.io/) | [Code](https://github.com/YilingQiao/DMRF)]\n- NeRFPlayer: A Streamable Dynamic Scene Representation with Decomposed Neural Radiance Fields, IEEE Transactions on Visualization and Computer Graphics, vol 29(5), 2023. [[Code](https://github.com/lsongx/nerfplayer-nerfstudio)]\n- Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction, Yang et. al., ACM Transactions on Graphics, 2023. [[Paper](https://arxiv.org/pdf/2309.13101.pdf) | [Project Page](https://ingra14m.github.io/Deformable-Gaussians/) | [Code](https://github.com/ingra14m/Deformable-3D-Gaussians)]\n- V4d: Voxel for 4d novel view synthesis, Gan et. al., IEEE Transactions on Visualization and Computer Graphics, 2023. [[Code](https://github.com/GANWANSHUI/V4D)]\n- MixVoxels: Mixed Neural Voxels for Fast Multi-view Video Synthesis, ICCV2023 Oral. [Code](https://github.com/fengres/mixvoxels)\n- DynPoint: Dynamic Neural Point For View Synthesis, NeurIPS 2023.\n\n## 2022\n- Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-time, CVPR 2022 [[Project Page](https://aoliao12138.github.io/FPO/)]\n- D2NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video, NeurIPS, 2022. [[Project Page](https://d2nerf.github.io/) | [Code](https://github.com/ChikaYan/d2nerf)]\n- Monocular Dynamic View Synthesis: A Reality Check, Gao et. al., Neurips 2022. [[Project Page](https://hangg7.com/dycheck/)]\n- TiNeuVox: Fast Dynamic Radiance Fields with Time-Aware Neural Voxels, Fang et. al., ACM SIGGRAPH Asia 2022. [[Project Page](https://jaminfong.cn/tineuvox/) | [Code](https://github.com/hustvl/TiNeuVox)]\n- Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-time, CVPR 2022. [Project](https://aoliao12138.github.io/FPO/)\n\n## 2021\n- Nerfies: Deformable Neural Radiance Fields, ICCV, 2021. [[Code](https://github.com/google/nerfies)] (instructions for **custom data**, this is the one everyone refering to)\n- Dynamic View Synthesis from Dynamic Monocular Video, ICCV, 2021. [[Code](https://github.com/gaochen315/DynamicNeRF)]\n- HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields, ACM Trans. Graph, 2021. [[Code](https://github.com/google/hyperNeRF) | [Project Page](https://hypernerf.github.io/) | [Colab](./colabs/HyperNerf.ipynb)] (instructions for custom data)\n- BARF: Bundle-Adjusting Neural Radiance Fields, Lin et. al., ICCV 2021 (Oral). [[Code](https://github.com/chenhsuanlin/bundle-adjusting-NeRF)]\n\n## 2020\n- D-NeRF: Neural Radiance Fields for Dynamic Scenes, Pumarola et. al, CVPR 2020. [[Project Page](https://www.albertpumarola.com/research/D-NeRF/index.html) | [Code](https://github.com/albertpumarola/D-NeRF)]","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/pdaicode%2Fawesome-dynamic-nerf/projects"}