# DGD: Dynamic 3D Gaussians Distillation

arXiv: https://arxiv.org/abs/2405.19321 · HTML: https://mlsanity.com/paper/2405.19321/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 54 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 30 of 48)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [D-NeRF: Neural Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2011.13961/) (2020)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [Nerfies: Deformable Neural Radiance Fields](https://mlsanity.com/paper/2011.12948/) (2020)
- [4D Gaussian Splatting for Real-Time Dynamic Scene Rendering](https://mlsanity.com/paper/2310.08528/) (2023)
- [Convolutional Occupancy Networks](https://mlsanity.com/paper/2003.04618/) (2020)
- [Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction](https://mlsanity.com/paper/2309.13101/) (2023)
- [K-Planes: Explicit Radiance Fields in Space, Time, and Appearance](https://mlsanity.com/paper/2301.10241/) (2023)
- [Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis](https://mlsanity.com/paper/2308.09713/) (2023)
- [Language-driven Semantic Segmentation](https://mlsanity.com/paper/2201.03546/) (2022)
- [Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields](https://mlsanity.com/paper/2112.03907/) (2021)
- [LERF: Language Embedded Radiance Fields](https://mlsanity.com/paper/2303.09553/) (2023)
- [Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting](https://mlsanity.com/paper/2310.10642/) (2023)
- [In-Place Scene Labelling and Understanding with Implicit Scene Representation](https://mlsanity.com/paper/2103.15875/) (2021)
- [LangSplat: 3D Language Gaussian Splatting](https://mlsanity.com/paper/2312.16084/) (2023)

## Built on by (most cited first, 23 of 23)

- [FreeTimeGS: Free Gaussian Primitives at Anytime and Anywhere for Dynamic Scene Reconstruction](https://mlsanity.com/paper/2506.05348/) (2025)
- [4D LangSplat: 4D Language Gaussian Splatting via Multimodal Large Language Models](https://mlsanity.com/paper/2503.10437/) (2025)
- [VoxelSplat: Dynamic Gaussian Splatting as an Effective Loss for Occupancy and Flow Prediction](https://mlsanity.com/paper/2506.05563/) (2025)
- [RePerformer: Immersive Human-centric Volumetric Videos from Playback to Photoreal Reperformance](https://mlsanity.com/paper/2503.12242/) (2025)
- [Segment then Splat: Unified 3D Open-Vocabulary Segmentation via Gaussian Splatting](https://mlsanity.com/paper/2503.22204/) (2025)
- [SurgTPGS: Semantic 3D Surgical Scene Understanding with Text Promptable Gaussian Splatting](https://mlsanity.com/paper/2506.23309/) (2025)
- [EgoSplat: Open-Vocabulary Egocentric Scene Understanding with Language Embedded 3D Gaussian Splatting](https://mlsanity.com/paper/2503.11345/) (2025)
- [4D Synchronized Fields: Motion-Language Gaussian Splatting for Temporal Scene Understanding](https://mlsanity.com/paper/2603.14301/) (2026)
- [SharpTimeGS: Sharp and Stable Dynamic Gaussian Splatting via Lifespan Modulation](https://mlsanity.com/paper/2602.02989/) (2026)
- [Splat and Distill: Augmenting Teachers with Feed-Forward 3D Reconstruction For 3D-Aware Distillation](https://mlsanity.com/paper/2602.06032/) (2026)
- [TIBR4D: Tracing-Guided Iterative Boundary Refinement for Efficient 4D Gaussian Segmentation](https://mlsanity.com/paper/2602.08540/) (2026)
- [PanopticQuery: Unified Query-Time Reasoning for 4D Scenes](https://mlsanity.com/paper/2604.05638/) (2026)
- [Rad-GS: Radar-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments](https://mlsanity.com/paper/2511.16091/) (2025)
- [RetimeGS: Continuous-Time Reconstruction of 4D Gaussian Splatting](https://mlsanity.com/paper/2603.13783/) (2026)
- [Inst4DGS: Instance-Decomposed 4D Gaussian Splatting with Multi-Video Label Permutation Learning](https://mlsanity.com/paper/2603.18402/) (2026)
- [FreeTimeGS++: Secrets of Dynamic Gaussian Splatting and Their Principles](https://mlsanity.com/paper/2605.03337/) (2026)
- [Do Gaussian Scenes Contain Enough Structure for Intrinsic Segmentation?](https://mlsanity.com/paper/2606.18623/) (2026)
- [Multi4D: High-Fidelity Dynamic Gaussian Splatting via Multi-Level Competitive Allocation](https://mlsanity.com/paper/2606.22197/) (2026)
- [DeGenseGS: Geometrically and Semantically Decoupled Surgical Scene Understanding in 4D Gaussian Splatting](https://mlsanity.com/paper/2607.04761/) (2026)
- [RoofGS: Roofline-Guided End-to-End Acceleration of 3D Gaussian Splatting](https://mlsanity.com/paper/2608.15785/) (2026)
- [SMG: Semantic Motion Graph for Monocular Dynamic Gaussian Splatting](https://mlsanity.com/paper/2608.31023/) (2026)
- [UniFusion: Sparse-View 4D Reconstruction via Unified Spatio-temporal Depth Alignment](https://mlsanity.com/paper/2609.05888/) (2026)
- [ProDyGS: Dynamic Gaussian Splatting from a Single Static Monocular Camera](https://mlsanity.com/paper/2609.32711/) (2026)

## Abstract (arXiv)

We tackle the task of learning dynamic 3D semantic radiance fields given a single monocular video as input. Our learned semantic radiance field captures per-point semantics as well as color and geometric properties for a dynamic 3D scene, enabling the generation of novel views and their corresponding semantics. This enables the segmentation and tracking of a diverse set of 3D semantic entities, specified using a simple and intuitive interface that includes a user click or a text prompt. To this end, we present DGD, a unified 3D representation for both the appearance and semantics of a dynamic 3D scene, building upon the recently proposed dynamic 3D Gaussians representation. Our representation is optimized over time with both color and semantic information. Key to our method is the joint optimization of the appearance and semantic attributes, which jointly affect the geometric properties of the scene. We evaluate our approach in its ability to enable dense semantic 3D object tracking and demonstrate high-quality results that are fast to render, for a diverse set of scenes. Our project webpage is available on https://isaaclabe.github.io/DGD-Website/