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EdMCGS: Event-Driven Markov Chain Gaussian Splatting for Extreme-Low-Frame-Rate Dynamic Scene Reconstruction

Yuzhong Wang, Wenmin Wang, Xinxing Yu

arXiv:2609.08332v1cs.CV

TL;DR

Sparse RGB frames leave the motion between consecutive frames without direct evidence, while storage pressure can favor retaining only a fraction of frames. EdMCGS reconstructs dynamic scenes by using sparse RGB frames to anchor states and event streams to drive scene transitions between them. EdMCGS outperforms RGB-based and event-based methods on synthetic and real-world scenes, and consistently outperforms existing baselines.

  • Problem

    Sparse RGB frames leave the motion between consecutive frames without direct evidence, while storage pressure can favor retaining only a fraction of frames.

    Source: §1 · pp. 2–3

  • Method

    EdMCGS reconstructs dynamic scenes by using sparse RGB frames to anchor states and event streams to drive scene transitions between them.

    Source: §1 · p. 4, §5 · p. 20

  • Results

    EdMCGS outperforms RGB-based and event-based methods on synthetic and real-world scenes, and consistently outperforms existing baselines.

    Source: §1 · p. 5, §5 · p. 20

  • Takeaways & Limitations

    EdMCGS uses event streams to predict scene states directly, with experiments on synthetic and real-world scenes.

    Source: §1 · p. 4, §5 · p. 20

    The pipeline assumes known camera poses.

    Source: §5 · p. 20

Abstract

from arXiv · show

We present EdMCGS (Event-driven Markov chain Gaussian Splatting), an end-to-end method for reconstructing dynamic 3D scenes from extreme-low-frame-rate RGB together with an event stream, which can then be rendered at any intermediate timestamp. Methods relying solely on RGB images generate numerous artifacts due to the lack of evidence from between consecutive frames. To supply this missing evidence, we model the scene motion as an event-driven Markov chain, in which the sparse RGB frames anchor the state at their own timestamps while the events recorded within an interval drive the transition across it. Since the transition reads the events of the current interval, it remains active at inference and produces the in-between motion of the 3D Gaussians directly from the events rather than by interpolation, which sets our method apart from prior work that uses events only as training-time supervision. The state is carried by a compact set of control points, each driven by the events sampled in the neighborhood of its own image projection, and a temporal local isometry term keeps the propagated motion locally rigid. Experiments on synthetic and real-world scenes show that EdMCGS outperforms both RGB-based and event-based baselines, while rendering in real time with far fewer Gaussians than the strongest event-based baseline. We release our source code and a new dataset at https://github.com/joseclipse/EdMCGS.

1. Introduction

Extreme-low-frame-rate RGB leaves large temporal gaps, while event streams provide evidence within them. EdMCGS uses those events to drive scene-state transitions between RGB-anchored states, and reports stronger results than existing RGB- and event-based methods.

1. Introduction

  • RGB-only reconstruction lacks evidence between sparse frames, leaving unobserved in-between motion unsupported.Low frame rates can result from bandwidth, storage, long exposures, or commodity hardware; retaining fewer RGB frames with a compact event stream can extend footage retention.Source: §1 · pp. 2–3
  • EdMCGS models motion as an event-driven Markov chain: sparse RGB frames anchor states, and events within each interval drive transitions that remain active at inference.This produces in-between motion directly from events rather than by interpolation.Source: §1 · p. 4
  • A compact set of control points drives canonical Gaussians through linear blend skinning, with local event sampling assigning each point motion codes from events at its image projection.The temporal local isometry regularizer preserves nearby inter-point distances across deformed timestamps, keeping propagated motion locally rigid and stabilizing unobserved-time rendering.Source: §1 · p. 4
  • The paper releases its source code and a new synthetic benchmark for event-based monocular dynamic scene reconstruction.The introduction frames these resources as addressing the lack of a reproducible baseline for fully dynamic, extreme-low-frame-rate reconstruction.Source: §1 · pp. 4–5
  • EdMCGS outperforms RGB-based and event-based methods on synthetic and real-world scenes, while training and rendering faster and using several times fewer Gaussians than the strongest event-based baseline.The introduction reports these comparisons across every tested frame rate.Source: §1 · p. 5

2. Related Work

Related work spans RGB-based dynamic reconstruction, event-based static reconstruction, and newer event-based dynamic methods. EdMCGS addresses the gap between sparse RGB anchors by using events to drive motion at inference.

2.1. RGB-based Dynamic Scene Reconstruction

  • MCGS propagates control-point motion using temporal attention over past states, but still relies on densely sampled RGB observations.EdMCGS retains sparse control points while changing the transition mechanism and targeting inter-frame gaps rather than motion blur within frames.Source: §2.1 · p. 5

2.2. Event-based Static Scene Reconstruction

  • Event-based static reconstruction uses events as geometric cues, but static-scene assumptions do not model temporal deformation.Source: §2.2 · p. 6

2.3. Event-based Dynamic Scene Reconstruction

  • Event-based dynamic methods address varied capture settings, but their events serve only as training supervision, leaving inference motion interpolated rather than event-driven.EdMCGS reconstructs dynamic scenes from sparse RGB and a monocular event stream, with transitions active at inference.Source: §2.3 · p. 7
  • EdMCGS is presented as a lightweight, mask-free method requiring no external optical-flow model, per-scene fine-tuning, or pose network.The authors release code and a dataset as a reproducible baseline for the extreme-low-frame-rate setting.Source: §2.3 · p. 7

3. Method

EdMCGS reconstructs dynamic scenes from extreme-low-frame-rate RGB and an asynchronous event stream, rendering at timestamps between observed frames. Sparse RGB anchors the control-point state, while events drive the interval transition.

3. Method

  • EdMCGS reconstructs a dynamic 3D scene from an extreme-low-frame-rate RGB sequence and asynchronous event stream, enabling rendering at any intermediate timestamp not covered by an RGB frame.Source: §3 · p. 7

3.1.1. Deformable 3D Gaussian Splatting

  • Under extreme-low-frame-rate input, a time-conditioned deformation MLP overfits the few observed timestamps and tears apart at unobserved ones.The MLP must be evaluated for all N ∼ 10^5 Gaussians at every step.Source: §3.1.1 · p. 9

3.1.2. Markov Chain

  • The method treats control-point configurations as Markov states, propagating motion within an interval from the preceding RGB frame using events.A first-order Markov chain makes the future conditionally independent of the past given the present state.Source: §3.1.2 · p. 9

3.1.3. Event Generation Model

  • The event generation model links rendered brightness change to accumulated signed events and supplies the basis for event supervision.Source: §3.1.3 · p. 9

3.2. Event-Driven Markov Chain

The event-driven Markov chain uses sparse RGB frames to anchor control-point states and interval events to propagate them forward. Its transitions depend on the current interval’s events, while control points carry motion to the canonical Gaussians.

  • A sparse control-point set drives nearby canonical Gaussians through linear-blend skinning, rather than predicting motion independently for every Gaussian.The method samples control points from canonical Gaussians and connects them to nearby Gaussians using K-nearest neighbors and distance-based Gaussian weights.Source: §3.2 · pp. 9–10
  • At query time, the state is the control-point configuration propagated from the immediately preceding RGB anchor using events observed since that anchor.RGB frames constrain states at their timestamps, while intermediate states must be inferred.Source: §3.2 · p. 10
  • The transition adds an event-driven displacement to the RGB deformation prior, which is evaluated at control points to keep RGB-driven motion low-dimensional.At the RGB anchor, the zero-initialized event branch vanishes; accumulated events then propagate the state forward.Source: §3.2 · p. 10
  • The transition is Markovian because its motion code accumulates only events from the current anchor-to-query interval, making the increment conditionally independent of earlier history.Each RGB frame resets the event window, so errors accumulated within one interval do not propagate across anchors.Source: §3.2 · p. 11

3.3. Local Event Sampling

Local event sampling builds a motion code for each control point from events near its image projection, rather than pooling the event stream globally. Shared event processing and spatial regularization support coherent transitions.

  • Events from the anchored interval are accumulated into a polarity map, encoded by a shallow CNN, and bilinearly sampled at each projected control point.Source: §3.3 · p. 11
  • Projection-local sampling lets events from a moving region drive its corresponding control points instead of mixing them with camera and background events through global pooling.The neighborhood is defined by the feature network’s receptive field and bilinear sampling, without an explicit radius.Source: §3.3 · p. 11
  • The event branch is zero-initialized and gradually warmed in, while sharing it across control points and local sampling supports spatial coherence.A KNN Laplacian smoothness term further encourages coherence.Source: §3.3 · p. 12

3.4. Temporal Local Isometry Regularization

The temporal local isometry regularizer preserves local distances between nearby deformed states, penalizing stretching and shearing without requiring rotation estimation. The training objective combines RGB photometric supervision, event supervision in frame gaps, and motion regularizers.

3.4. Temporal Local Isometry Regularization

  • LTLI penalizes changes in KNN control-point edge lengths between nearby deformed timestamps, while locally rigid motion that preserves distances is not penalized.Stretching and shearing are penalized instead.Source: §3.4 · p. 12
  • Unlike SC-GS’s ARAP regularizer, LTLI preserves inter-point distances rather than fitting per-point rotations, and requires no rotation estimation.It compares two deformed timestamps rather than a deformed configuration against the static canonical one.Source: §3.4 · p. 12

3.5. Training Objective

  • Event supervision renders dense sub-step timestamps, matches their log-luminance difference to events, and detaches color gradients so the loss updates geometry and motion.The authors report color contamination when event gradients were allowed to update Gaussian colors.Source: §3.5 · p. 13
  • The full objective combines RGB L1 and D-SSIM photometric terms, event loss, temporal local isometry, and KNN Laplacian smoothness on the event-driven transition.The smoothness term keeps neighboring control points coherent; weights balance the loss terms.Source: §3.5 · pp. 12–13

4. Experiments

The experiments evaluate EdMCGS’s intermediate-motion reconstruction on synthetic and real-world sequences, reporting image-quality gains over baselines with a lightweight event-based representation. Ablations examine the roles of event-driven transitions, temporal local isometry, and control-point-local event sampling.

4.1. Datasets

  • Synthetic sequences span articulated full-frame, localized-limb, and rigid mechanical motion; training uses frames sampled at 10 fps or 5 fps, with discarded frames serving as ground truth at unobserved intermediate timestamps.Source: §4.1 · p. 13

4.2. Experimental Setup

  • EdMCGS is compared with RGB-based deformable baselines D-3DGS and SC-GS, E2VID + D-3DGS, E-D3DGS, and an internal Ours w/o Markov baseline.Source: §4.2 · p. 14

4.3. Comparison

  • Figures 2 and 3 show RGB-based methods tearing moving parts apart or smearing them, while E-D3DGS suppresses tearing but blurs fast-moving regions; compared with previous methods, our method reconstructs better details.Source: §4.3 · pp. 15–16
  • 31.56 dB at 10 fps and 29.37 dB at 5 fps on synthetic data exceed all baselines; on four real scenes EdMCGS ranks first on all three metrics.The relative gain over E-D3DGS is larger on LPIPS than PSNR, and the advantage grows as the input frame rate becomes sparser.Source: §4.3 · p. 16, Table 1 · p. 16, Table 2 · p. 17

4.4. Ablation Study

  • Removing the event stream yields 24.05 dB on synthetic data and 30.04 dB on real data.This variant reduces the model to a control-point deformation field driven by time alone.Source: §4.4 · p. 18
  • The reported 1.09 dB gap to the full model quantifies the contribution of events to the method.The passage describes this difference as measuring everything the events are worth to the method.Source: §4.4 · p. 18
  • At 5 fps, removing the event-driven transition yields 21.67 dB on Jumpingjack, 10.14 dB below the full model, but only 0.24 dB below it on Lego at 10 fps.When little happens between frames, time-only conditioning is nearly sufficient; events must drive the transition when motion is substantial.Source: §4.4 · pp. 18–19
  • Global event-map pooling mixes object and background events into one descriptor shared by all control points, causing cross-contamination that local projected sampling avoids.The pooled map also mixes sensor noise, while point-specific sampling reads events at each control point’s own image projection.Source: §4.4 · p. 19
  • Temporal local isometry preserves nearby control-point distances, keeping propagated motion locally rigid; it matters most on synthetic sequences and complements the event-driven transition.The transition supplies motion but can displace points inconsistently on noisy, uneven event maps, so the full model needs both components.Source: §4.4 · pp. 19–20

5. Conclusion

EdMCGS reconstructs dynamic 3D scenes from extreme-low-frame-rate RGB and events, with an event-driven Markov chain that predicts intermediate deformations during inference. The authors report consistent baseline outperformance and identify known camera poses and limited evaluation diversity as boundaries for future work.

5. Conclusion

  • Unlike temporal interpolation, the event-driven Markov chain remains active during inference to predict intermediate deformations directly from events.Source: §5 · p. 20
  • EdMCGS consistently outperforms existing baselines on synthetic and real-world datasets.The method reconstructs dynamic scenes from extreme-low-frame-rate RGB together with an event stream.Source: §5 · p. 20
  • The authors release code and a dataset to facilitate future research on event-based dynamic scene reconstruction.Source: §5 · p. 20
  • The pipeline assumes known camera poses, an assumption shared with the compared deformable Gaussian splatting methods.Source: §5 · p. 20
  • The authors identify joint recovery of camera poses and dynamic geometry as a natural next step.They also propose higher-order event-driven temporal models and evaluation on larger real-world event datasets with more diverse motions.Source: §5 · p. 20

CRediT authorship contribution statement

The contribution statement assigns methodology, software, validation, data curation, drafting, and visualization to Yuzhong Wang, and lists supervision and writing roles for Wenmin Wang and Xinxing Yu.

CRediT authorship contribution statement

Declaration of Generative AI and AI-assisted technologies in the writing process

The authors report using Claude to improve the manuscript’s language and readability, then reviewing and editing the content and retaining responsibility for it.

Declaration of Generative AI and AI-assisted technologies in the writing process

Appendix A. Per-scene Results

The appendix reports per-scene quantitative comparisons behind the dataset averages, with separate tables for synthetic and real-world results.

Appendix A. Per-scene Results

  • The appendix provides per-scene results behind the averages reported in Tables 1 and 2.Source: Appendix A · p. 22
  • Table A.1 presents per-scene quantitative comparisons on the synthetic dataset.Source: Table A.1 · p. 22
  • Table A.2 presents per-scene quantitative comparisons on the real-world E-D3DGS dataset at 5 fps.Source: Table A.2 · p. 23
  • The appendix states that the average over four scenes is reported in Table 2 and highlights the best and second-best values for each column.Source: Appendix A · pp. 22–23
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