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Per-Gaussian Embedding-Based Deformation for Deformable 3D Gaussian Splatting

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arxiv 2404.03613 v5 pith:NK7AYLAM submitted 2024-04-04 cs.CV

Jeongmin Bae , Seoha Kim , Youngsik Yun , Hahyun Lee , Gun Bang , Youngjung Uh This is my paper · ORCID
classification cs.CV
keywords deformationdynamicper-gaussiancoordinate-baseddeformationsembeddingsfastfunction
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As 3D Gaussian Splatting (3DGS) provides fast and high-quality novel view synthesis, it is a natural extension to deform a canonical 3DGS to multiple frames for representing a dynamic scene. However, previous works fail to accurately reconstruct complex dynamic scenes. We attribute the failure to the design of the deformation field, which is built as a coordinate-based function. This approach is problematic because 3DGS is a mixture of multiple fields centered at the Gaussians, not just a single coordinate-based framework. To resolve this problem, we define the deformation as a function of per-Gaussian embeddings and temporal embeddings. Moreover, we decompose deformations as coarse and fine deformations to model slow and fast movements, respectively. Also, we introduce a local smoothness regularization for per-Gaussian embedding to improve the details in dynamic regions. Project page: https://jeongminb.github.io/e-d3dgs/

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Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. StreamME: Simplify 3D Gaussian Avatar within Live Stream

    cs.GR 2025-07 conditional novelty 7.0 of 10

    StreamME reconstructs an animatable head avatar from a live monocular video in about five minutes by attaching 3D Gaussian points to a tracked face mesh and pruning unimportant points during training.

  2. 4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A diffusion model trained on 60,000 fitted 4D Gaussian Splatting human clips generates text-prompted, view-consistent dynamic humans directly in 4D, over 10x faster than video-first pipelines.

  3. VoxelSplat: Dynamic Gaussian Splatting as an Effective Loss for Occupancy and Flow Prediction

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A training-only Gaussian splatting loss, which renders predicted 3D semantics and motion into 2D camera views, improves semantic occupancy and scene flow prediction across several camera-based models.

  4. FreeTimeGS: Free Gaussian Primitives at Anytime and Anywhere for Dynamic Scene Reconstruction

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A dynamic-scene representation where Gaussian primitives live freely in 4D space-time with linear motion and Gaussian time windows achieves state-of-the-art novel-view quality on complex-motion benchmarks.

  5. GIFStream: 4D Gaussian-based Immersive Video with Feature Stream

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A 4D Gaussian representation with sparse time-dependent feature streams achieves state-of-the-art rate-distortion performance for immersive video, compressing dynamic scenes to a few megabytes while rendering in real time.

  6. Monocular Dynamic Gaussian Splatting: Fast, Brittle, and Scene Complexity Rules

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A comprehensive benchmark shows monocular dynamic Gaussian splatting methods are fast and brittle, with scene complexity dominating method differences.

  7. Event-boosted Deformable 3D Gaussians for Dynamic Scene Reconstruction

    cs.CV 2024-11 conditional novelty 6.0 of 10

    Event cameras and deformable 3D Gaussians are combined with jointly optimized thresholds and dynamic-static decomposition, achieving state-of-the-art dynamic scene reconstruction on a new benchmark.

  8. LocalDyGS: Multi-view Global Dynamic Scene Modeling via Adaptive Local Implicit Feature Decoupling

    cs.CV 2025-07 conditional novelty 5.0 of 10

    LocalDyGS reconstructs dynamic scenes by decomposing space into seed-based local regions and generating time-varying Temporal Gaussians, though its claim of being first for large-scale scenes omits the existing Swift4...

  9. GSCodec Studio: A Modular Framework for Gaussian Splat Compression

    cs.CV 2025-06 conditional novelty 5.0 of 10

    GSCodec Studio is a modular open-source framework for Gaussian Splat compression, and its composed Static and Dynamic GSCodec pipelines report competitive rate-distortion results against several baselines.

  10. Advances in 4D Representation: Geometry, Motion, and Interaction

    cs.CV 2025-10 conditional novelty 4.0 of 10

    A representation-centric survey of 4D generation and reconstruction, organized by geometry, motion, and interaction, with qualitative trade-off comparisons across seven representation families.

  11. Advancing Extended Reality with 3D Gaussian Splatting: Innovations and Prospects

    cs.CV 2024-12 conditional novelty 4.0 of 10

    3D Gaussian Splatting research relevant to Extended Reality is organized into a five-part taxonomy with suggested future directions.

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